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GIS and Scenario Analysis: Tools for Better Urban Planning
2.17
GIS and Scenario Analysis: Tools for Better Urban Planning
Arnab Chakraborty and Andrew McMillan, University of Illinois at Urbana-Champaign, Champaign, IL, United States © 2018 Elsevier Inc. All rights reserved.
2.17.1 2.17.2 2.17.3 2.17.3.1 2.17.3.2 2.17.3.3 2.17.3.4 2.17.4 2.17.4.1 2.17.4.2 2.17.4.3 2.17.4.4 2.17.5 References
Introduction Principles: Scenario Analysis in Urban Planning Techniques: GIS and Planning Analysis Basic GIS Software Packages Planning Support Systems Urban Models Using Tools to Compare and Assess Scenarios Application: CMAP’s GO TO 2040 Plan Overview of the Project How GIS Tools Were Employed in Each Task Modeling, Assessment, and Site Selection Public Outreach and GIS Analysis Conclusions
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Glossary Civic Technology: Information technology applications that enable engagement or participation of the public. Computer Modeling Tools: Tools that have computing capabilities to model the interaction of multiple urban phenomena and their response to policy interventions and uncertainties. Explorative Scenarios: Scenarios that broadly identify “what can happen?” (Börjeson et al., 2006) or develop not just likely or plausible futures but possible futures. Normative Scenarios: Scenarios that start with a well-defined target or seek participant inputs to first identify targets. Open Data: Data available freely to use and republish, without restrictions from copyright, patents, or other mechanisms of control. Planning Support Systems: Interactive computer-based tools that can provide mapping and analysis capabilities. Predictive Scenarios: Scenarios designed to depict the most likely future based on data-driven trends and input from actors (forecasting), or if the future outcomes are a direct result of decisions made in an earlier period. Qualitative Tools: Tools that primarily employ data gathered through interviews, essays, or opinion surveys, or constructed through narrative forms such as stories or personal experiences.
I agree with a growing number of scholars who suggest that planners abandon the futile effort to predict what the future will be and prepare a range of scenarios suggesting what the future may be. (Klosterman 2013, p. 164)
2.17.1
Introduction
Scenario planning allows us to consider multiple facets of a complex problem, imagine alternate possibilities, and study the future impacts of present-day decisions in light of important unknowns and uncertainties. Scenario planning is widely used in disciplines ranging from business and military planning to organizational behavior as a tool that allows participants to think critically about how a future might unfold (van der Heijden, 1996) and to identify new insights or strategies (Malinga et al., 2013). Urban planners and policymakers use scenario planning in a number of ways, but its most common use is in regional comprehensive planning (Chakraborty and McMillan, 2015). That is the angle this article deals with most closely. Scenario analysis in regional planning can be useful (1) to assess how urban areas may change in the future, (2) to explore how uncertainties about the future and interdependence between parts of the urban systems may lead to different possible outcomes, (3) to analyze how different planning and policy options impact the outcomes, and (4) to involve stakeholders in the learning and decision-making process. To achieve these goals, scenario planning activities in regional planning often rely on geographic information systems (GIS). This is because scenarios are most commonly expressed as spatial datasets such as future land-use outcomes. GIS is used to develop these outcomes, create visualizations for use in public or analytical processes, and test their impact on a range of indicators.
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For example, when Chicago Metropolitan Agency (CMAP) conducted a scenario planning process as part of its GO TO 2040 comprehensive plan, it created three scenariosdpreserve, reinvest, and innovatedeach of which, as discussed later, focuses primarily on land-use outcomes. GIS data and tools are also used to model the relationship between the current built environment, land-use, and infrastructure decisions. For example, CMAP used the Land use Evolutions and impact Assessment Model (LEAM) to generate different population distribution trends through 2040 under each of the tested scenarios. Moreover, GIS tools are used to assess the end results or scenarios using a set of quality-of-life indicators and to collect inputs from stakeholders and decision-makers. For example, CMAP collaborated with MetroQuest, a private planning tool developer, to display land-use scenarios as web dashboards, with indicator scores for each, and forms for participant to provide inputs about their preferences. Finally, GIS can assist us in understanding how uncertainties about the future may interact with decision choices that planners face and to identify which decisions may work across a range of future uncertainties and which ones may not. For example, models built with GIS can explore how economic trends, such as changing gasoline prices, may increase development in outlying urban areas and how that may increase land prices, congestion, and demand elsewhere within a region. GIS has the potential to advance scenario planning from an analytical framework for analyzing plans to a broader paradigm for better urban planning. So far, use of scenario analysis has remained largely restricted to regional planning applications and focused on the use of tools to develop scenarios with limited utility in the plan-making process. However, when employed effectively, the promise of scenario planning is much greater. GIS can help scenario planning processes involve more diverse stakeholders in a region, make strategic decisions in uncooperative environments, and promote greater citizen engagement. New frontiers of open data and civic technology hold considerable promise for combining scenario analysis and GIS capabilities. Scenario analysis and GIS can also be employed together in planning-related fields such as public health, security, and environmental management. For example, scenario analysis has been used to project the different ways a pandemic can spread, implications of a terrorist attack, or fallouts from different global climate change scenarios. GIS tools can be useful in each of those situations to identify mitigation responses or to develop contingency or adaptation plans. While our focus in this article is on urban planning problems, we believe that a careful reader would be able to extend many of these lessons to other fields. The rest of this article is organized as follows. We start with an overview of scenario applications in urban planning and discuss how GIS provides the backbone of the analytical aspects of such applications. We share some examples on how GIS is used to develop and analyze scenarios. We present a case study of a scenario planning application, specifically highlighting the role of GIS in the process. We conclude with a discussion about some ongoing trends and the promises it makes for advances in GIS and scenario analysis.
2.17.2
Principles: Scenario Analysis in Urban Planning
Much has been written about the benefits and limitations of scenario planning (the terms “scenario planning” and “scenario analysis” are often used interchangeably in the literature. We use scenario analysis to refer to a narrow set of activities, primarily technical components, and scenario planning to refer to the broader planning process that may also involve engagement and implementation considerations); see, for example, Hopkins and Zapata, 2007; Klosterman, 2013; Schwartz, 1996; van der Heijden, 1996. Scenario planning was pioneered by the RAND Corporation (Kahn, 1962). It is a tool meant to foster the imagination and spur critical thinking about how the future might unfold. Scenario planning, forecasting, visioning, and alternative testing are some of the distinct approaches for future-oriented analysis that have become commonplace in urban planning (Bartholomew, 2005; Isserman, 1985; Shipley et al., 2004; Throgmorton, 1992; Quay, 2010). These approaches can be used by the participants in a plan-making process to formulate shared goals, simulate future outcomes of present-day decisions, address systemic uncertainties, and identify robust strategies (Hopkins and Zapata, 2007). Planners’ use of scenario analysis is, in many ways, a response to the critiques of planning for a single and agreed-upon future (Hopkins and Zapata, 2007). The scenario planning approach allows planners to create alternative but possible futures, and advance several goals over the predict-and-plan approach. The following notes from Oregon Department of Transportation or ODOT’s (2013, p. 6) recent scenario planning handbook for municipal governments illustrates the key benefits of scenario planning. “. scenario planning . allows a community to look long-term and envision the future it wants, rather than accept the trend line embodied in most existing plans. Scenario planning is not about predicting the future or providing a specific answer. Rather, it is a methodology for “seeing” futures not easily estimated using past trends or assumptions. The expectation is that through the process of conceiving, developing, and evaluating a series of future scenarios and the outcomes they produce, a preferred and feasible course of action can be identified.” While this approach improves on past practices of planners to construct and analyze a single prediction, it nonetheless promotes the idea of identifying a single “preferred” vision among the scenarios created. The clearest purpose of this “preferred” vision for the future is to serve as a guide for plans and policies that will steer development closer to this shared vision. This practice is known as normative scenario planning. A complementary and sometimes alternative approach to this is “exploratory scenario planning,” which aims to generate scenarios that broadly identify what can happen (Börjeson et al., 2006), as opposed to what is likely or plausible. These processes often incorporatedexplicitlydvariations in critical uncertainties and identify how the same policy may produce different outcomes based on variations in uncertainties (normative and exploratory scenarios build on traditional predictive approaches that are designed to depict the most likely future based on data-driven trends and input from actors, or if the future outcomes are a direct result of decisions made in an earlier period).
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Context Organizational structure Primary decisions
Outcome
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Secondary decisions Scenario type
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Fig. 1 A scenario planning typology. From Chakraborty, A., and McMillan, A. (2015). Scenario planning for urban planners: Toward a practitioner’s guide. Journal of the American Planning Association, 81(1), 18–29. doi:10.1080/01944363.2015.1038576.
According to Chakraborty and McMillan (2015), scenario planning projects can vary widely. Fig. 1 illustrates a typology or key components of a scenario planning process. The typology shows that aspects of the project may be constrained by the context in which the scenario process is situated. For example, project “scope” or whether the key questions concern land-use policy choices or transportation investment decisions may be a given considering the goals of the process. However, planners may have to choose whether to look at normative or exploratory scenarios, or whether to engage with general public or selected stakeholders. The typology suggests making these “primary” decisions about how the broader process is scoped and structured before selecting the tools. Finally, while different parameters of the scenario planning processes are made iteratively in practice, the general guidance is that the needs of the planning process should set the parameters of the technical tools, and not the other way round. Within the broader framework of scenario planning, GIS tools have two basic roles. One is developing and analyzing scenarios, where the primary focus is on technical capacity. The other role is using scenario planning to engage the public in the planning process, where the focus may be more on community engagement. Many of the GIS tools attempt to serve both of those roles whereas others may specialize in one of those two roles. The next section discusses these roles and tools in more detail.
2.17.3
Techniques: GIS and Planning Analysis
GIS are commonly used to categorize, analyze, and visualize urban systems. GIS datasets in an urban area may include transportation networks, existing land-use, and critical environmental resources. Data can also contain spatial constraints and policy measures on development such as floodplains and zoning regulations, respectively. Since such datasets and the knowledge about their relationships are fundamental to planning analysis (see Fig. 2), GIS has become an essential component of urban planning. Scenario planning relies on tools that offer planners the ability to view complex data, to project potential outcomes of decisions and anticipate their implications, and to communicate these to the wider public. Central to these are tools that can forecast or illustrate and evaluate land-use change over space and time. Such tools offer planners and stakeholders the ability to view and assess potential future outcomes of policy decisions or investment choices before final decisions are made about implementation. In short, GIS tools provide the ability to construct and demonstrate scenarios. There are several GIS-aided scenario planning platforms and software applications, ranging from standard GIS software packages such as ArcGIS to highly specialized modeling applications. The following section describes how scenario planning is typically Computer software
Computer hardware
GIS
Geographic data
Technical staff
Monitoring Recording Measuring Analyzing Mapping Modeling Communicating
Planning intelligence
Fig. 2 GIS and planning analysis. From Berke PR, Godschalk DR, Kaiser EJ, and Rodriguez DA (2007). Chapter 4: Planning Support Systems, In Urban Land Use Planning, Figure 4-2, pp. 91. University of Illinois Press.
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handled through three types of GIS applications. First, with standard ArcGIS software, then with specialized GIS-based planning support systems, and finally with software tools that employ more sophisticated modeling approaches. Each of these utilities has their own suitability context. Scenario planning with the ArcGIS software is useful in that it does not require additional training beyond a familiarity with ArcGIS. Scenario-based planning support systems offer the ability to develop scenarios more efficiently, control for a wider range of relevant variables, and produce more detailed analysis than standard GIS packages such as ArcGIS. But these systems may be proprietary and require additional training. Finally, modeling tools offer the ability to generate land-use and land cover scenarios that are theoretically sound and can produce the most likely outcomes. But these models also require additional skill development, may lack the easy customization available on planning support systems, and doesn’t allow for users to customize or edit scenario outcomes. Each type of scenario planning application is described in the following section.
2.17.3.1
Basic GIS Software Packages
Standard GIS software packages like the popular ArcGIS program generally have little in the way of tools specifically designed for scenario development and analysis. But the suite of tools available do allow for some rudimentary scenario creation and analysis. ArcGIS is advantageous in that it is already widely used by planners and other practitioners. At its simplest, ArcGIS users can create layers to represent different scenarios, such as different potential future land-use patterns. These layers are essentially “hand-drawn,” and they will lack predictive output data that allows for a detailed quantitative analysis of alternative scenarios. But ArcGIS’s extensive map layout capabilities are suitable for producing visualizations that can be presented to stakeholders or the general public. ArcGIS also comes with the tools for the creation and analysis of multicriteria decision analysis (MCDA), a process that is often a component of scenario planning exercises. MCDA is a decision-making methodology that attempts to identify the most suitable sites for a planning project while acknowledging conflicting goals or preferences. Rendered in ArcGIS, MCDA can be visualized by overlaying several layers representing social or physical aspects of the urban environment. The variables associated with the layers are weighted based on their importance, and the creation of multiple scenarios will result from changing the weights of the variables. For example, Zhan and Zhou (2010) used ArcGIS to create three future development scenarios for the Houguan Lake Ecological District in China. Variables included data on developable areas, water environment, and biological environment. Three scenarios identifying preferred areas for development were created that addressed three broad themes: (1) the district as a residential area, (2) the district as a tourist destination, and (3) the district as a green industry hub. These three outcomes were produced by modifying the weights of the input variables. While the standard ArcGIS package lacks tools solely focused on scenario development, there are features and extensions that can aid in scenario creation and analysis. GeoPlanner for ArcGIS is an online app that allows for the creation and addition of layers and the easy modification of layers for the creation of different scenarios. Scenarios can be compared side-by-side along a range of preselected variables. Additionally, the ArcGIS 3D Analyst extension, a package of tools that aid in visualization, can offer visualizations suitable for scenario presentation. The 3D Analyst is capable of producing scenes based on common shapefiles such as building footprints, digital elevation models, or aerial images, and is capable of rendering a range of urban form aspects, such as fences or streets as well as producing surface-conforming polygons. While the primary ArcGIS lacks the detailed scenario creation and analysis components of more specialized scenario planning programs, it is well-suited for the creation of multiple scenarios related to site selection processes or suitability analysis. Siting certain landmarks requires a substantial amount of spatial data, such as land use and other regulations, environmental factors, economic factors, engineering factors, and social and cultural considerations. And land-use suitability or site selection allows for these characteristics to be analyzed collectively. Site selection is typically composed of two parts: identification of potential sites based on certain criteria, and an analysis of each candidate site’s suitability. The site selection process typically uses the integration of multiple map overlays to identify acceptable candidate sites. Layers can be weighted based on relative suitability or unsuitability. ArcGIS’s system based on multiple layers is amenable to the creation of several siting scenarios, or the ability to use a combination of multiple layers to identify suitable land for a particular use.
2.17.3.2
Planning Support Systems
Spatial analysis tools in GIS and other statistical approaches may be employed to understand the relationships between different components of the urban system, such as between high-density development and pedestrian activity. This knowledge can be programmed into GIS software such as ArcGIS or used in combination with other spreadsheet and database analysis tools such as Microsoft Excel or Access. However, scenario planning practices in urban planning that employ GIS often rely on specifically created tools that are called planning support systems, such as Envision Tomorrow or CommunityViz. Planning support systems are generally defined as GIS tools specifically tailored to meet the specific needs of planners. In the past two decades, several planning support systems have been developed for creating and analyzing planning and planning-related scenarios. As specialized tools, PSSs typically offer more efficient methods of scenario development and analysis as well as a wider range of analysis options, although their sophistication requires additional training in order to use. The primary advantage of PSSs over the basic ArcGIS software package is that many offer users the ability to “sketch” aspects such as different land uses on the fly. While ArcGIS allows users to create different land-use layers, the process can be time-consuming and difficult to edit. In contrast, the sketching tools contained within scenario planning PSSs allow users to quickly add and edit land uses.
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CommunityViz, developed by the Placeways planning consultants, is a proprietary software application that operates as an ArcGIS extension. It is primarily a land-use analysis and decision-making tool that allows users to view the predicted impact of various land-use scenarios. Scenarios are created by sketching hypothetical land-use scenarios within a prescribed area. The characteristics of these land-use types, such as the number of households, property tax revenue, and total energy use, can be determined by the user. While sketching, users receive dynamic feedback of predicted variables based on certain assumptions and input values. This could include the predicted number of new jobs, estimated traffic congestion, as well as resource analysis such as predicted water and energy use. CommunityViz can also operate as a suitability analysis tool that employs weighted measurements to separate layers. Envision Tomorrow is another popular scenario planning ArcGIS extension. Developed by Fregonese Associates, it is free and open access. The program is designed to make use of data that is typically accessible to both planners, such as tax assessor parcel data and Census data. Similar to CommunityViz, users sketch predefined land uses within a defined geographical area. Envision Tomorrow allows users to define several building types along several sets of values, such as population density, rent or sale price, or energy and water use. Combinations of these building types are bundled into different development types, such as mediumdensity residential or town center. These development types are then sketched onto a map. Predicted social, physical, and economic outputs are generated on the fly as development types are added or removed from a map. Scenarios can then be compared along fiscal, demographic, or land-use values. Since debuting in 2006, Envision Tomorrow’s open source status has made continual refinement and revision possible. Envision Tomorrow has been used in many regional planning initiatives. Increasingly, GIS and planning applications have employed online access as a means of quickly and effectively displaying and disseminating data, as well as collaboration. The Sustainable Places Analytical Resources Core (SPARC) is a web-based, open-source GIS data processing storage service developed by Criterion Planners. No GIS software is required, as the application is accessed entirely through a web-browser. The webapp, once set up, offers access to open-access national datasets, as well as data storage. The data management component includes the ability to transform and normalize geographic data, making collecting and utilizing data from several different organizations for a single planning project possible. SPARC’s scenario planning tool, INDEX, also available as a standalone PSS, allows users to “paint” land-use and transportation scenarios within a determined area. Since the application is entirely web-based, collaboration between multiple users is possible. Similar to other scenario planning applications, the INDEX tool provides basic layer geoprocessing functions, such as layer clipping or merging. Land-use types can be classified and linked to indicators, such as population density, development mix, or vehicle miles traveled. Indicators are dynamically calculated as land-use types are added, removed, or edited during the scenario constriction process, and scenarios can be ranked along important social or physical indicators.
2.17.3.3
Urban Models
Urban models consist of GIS software that can aid the scenario planning process by constructing scenarios based on algorithms. Unlike planning support systems, the scenarios generated by urban models are generated based on a set of theorized causeand-effect relationships and predictions (Klosterman, 1994) rather than by allowing a user to sketch proposed land uses. While the nature of these applications typically does not allow for users to decide precisely where and when land-use classification or land-use change will occur, the land-use changes produced by these models are based on calculations that are determined to be based on theoretical and empirical foundations, although nearly all models must be calibrated to its local context in order to be effective. SLEUTH is a cellular automata terrain and land-cover mapping model that generates predicted patterns of urban growth (Jantz et al., 2010; Chakraborty et al., 2015). The model employs physical input data, such as slope, and historical data, such as land-use type, urbanized area, and transportation infrastructure, to produce predicted future land-use and land-cover changes. SLEUTH has been particularly popular for predicting the land-use and land-cover impacts of different policy scenarios. For example, Wu et al. (2010) used SLEUTH and data from 1988 to 2004 to model predicted growth in Shenyang City, China, out to 2030 based on (1) a current trends scenario, (2) a regional policy scenario, and (3) an environmental protection scenario. Similarly, Bihamta et al. (2015) used historical data from 1976 to 2010 to model urban growth in Isfahan, Iran, based on (1) a current trends scenario and (2) a compact development scenario.
2.17.3.4
Using Tools to Compare and Assess Scenarios
One of the primary functions of a GIS-based tool is to compare and assess scenarios. Such assessments are usually based on a set of performance indicators (Chakraborty and McMillan, 2015). These indicators may capture simple measures of change in a key attribute of the system, such as land converted from agricultural to residential use, or estimates of the broader impact of changes, such as on fiscal conditions or air quality. Basic tools, such as the ArcGIS program, can generate many measures of change using the data layers that represent before and after conditions. The participants can perceive these changes visually, for example, a compact development pattern versus a sprawled one, or using basic metrics such as acres of land converted under each scenario. Most planning support systems have a preprogrammed set of indicators that are a part of the toolbox or are custom-developed for a place based on its unique conditions or issues of interests. Indicators preprogrammed into these tools can be generated on the fly or after each step in the scenario development process, offering participants a way to iteratively achieve a desirable value on a particular set of indicators. For
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example, stakeholders aiming to allocate 10,000 new people to a community using CommunityViz may “paint” higher density patterns and score high on land preservation indicator, or choose the single family option and score low. Estimates of impacts of scenarios that go beyond simple measurements require additional capabilities. Some PSS tools offer in-built impact assessment models for a number of estimated impacts that can be, with additional effort or for a fee, calibrated to the local context. For example, Envision Tomorrow offers additional measures called “Location Efficiency,” “Regional Fiscal Impact Tool,” and “Public Health.” The fiscal impact tool, for example, models local finance as a function of “tax rates and municipal population as well as scenario outputs relating to population, employment, and property value of new construction” (for more information on RIFT model of Envision Tomorrow, please visit: http://envisiontomorrow.org/fiscal-impact-model/ (accessed 20 February 2017). Most planning processes use a variety of indicators that include environmental, economic, and social impacts. A number of frameworks exist on how to compare scenarios with multiple indicators. These include assigning weights to different indicators and comparing them using a multicriteria decision-making approach (Stewart and Scott, 1995), or prioritizing them on the core values of the community, which can in turn be identified through a visioning process (Chakraborty, 2010). Chakraborty et al. (2015) suggest that stakeholders should clearly identify factors that are within their control and those that are beyond their control in identifying how to use scenarios effectively. According to this approach, decisions that provide superior outcome across all indicators and under all possible uncertainties are robust decisions. However, they argue that decisions that provide better outcomes under some indicators or under some future conditions may also be used for contingency planning. Ultimately, which indicators are employed to assess scenarios and how their results are compiled and used to make policy should be decided locally.
2.17.4
Application: CMAP’s GO TO 2040 Plan
Regional planning agencies like the Chicago Metropolitan Agency for Planning (CMAP) show the value of GIS-supported scenario planning in long-range and comprehensive planning. Such planning activities offer useful case studies because they directly engage questions of interrelationships between land-use and transportation planning, stakeholder engagement, as well as long-range regional planning where the combination of GIS and scenario analysis has been most popular. But this emerging framework can grow complex and unmanageable. Scenario planning offers a method for addressing the complicated relationship between land use and transportation without losing sight of important broad themes, such as environmental protection or regional equity.
2.17.4.1
Overview of the Project
The Chicago Metropolitan Agency for Planning was established in 2005 by combining the Northeastern Illinois Planning Commission and Chicago Area Transportation Study (Fig. 3). Part of CMAP’s responsibility includes reviewing and approving projects in the Chicago region that use federal transportation funds. But the organization’s area of concern extends beyond transportation planning to encompass goals such as more livable communities, enhanced human capital, and better government efficiencies. One of CMAP’s founding goals is to provide better integration between land use and transportation planning for the Chicago region. As such, CMAP’s first major undertaking was GO TO 2040, an update of the Chicago region’s comprehensive plan that would reflect these new priorities. The plan proved to be major undertaking in that it involved consulting with several partner organizations and numerous outreach efforts to the general public in order to develop recommended policies and investments. Scenario planning played a key role in GO TO 2040, as scenarios allowed CMAP to address issues raised by comprehensive planning, such as the interdependence of several planning decisions, potential impacts of different decisions, as well as involving a range of stakeholders. Before constructing scenarios to compare, planners must agree on certain themes, topics, and parameters that are important to the scenario planning project. When developing scenarios, CMAP officials had to address several important questions, such as identifying the overarching themes that would define each scenario, the components that both define the scenarios as well as highlight their differences, and parameters for the variables that make up these components. A number of working committees with stakeholders throughout the region helped identify the key components that would be used to define each scenario. After stakeholder engagement and opinion polling, CMAP officials created three broad themes that defined each scenario (Fig. 4). The “Preserve” scenario sought to accommodate growth without drastic change to region’s level of urbanization. Characteristics included moderate density increases in built-up areas, extended transit access, and extensive open space preservation and habitat restoration. The “Reinvest” scenario sought to extensively increase density in existing areas and prioritize transit-oriented development throughout the region. The “Innovate” scenario prioritized outward growth with an emphasis on green building and a reliance on emerging technologies to mitigate the negative environmental effects of sprawl. The scenarios were not meant to be mutually exclusive and the final plans were achieved by combining the best of all three scenarios.
2.17.4.2
How GIS Tools Were Employed in Each Task
The scenarios consisted of several of the same subcomponents related to important planning issues, such as land use, transportation, and energy plans, and they differed based on the goals of each scenario. For example, the land-use plan in the Reinvest Scenario called for intense TOD, while in the Preserve Scenario it called for moderate infill, and in the Innovate Scenario, it called for conservationist design. To create scenarios based on these subcategories, CMAP officials used 250 regional indicators, where 53 datasets on different
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Fig. 3 CMAP jurisdiction. Source: Chicago Metropolitan Agency for Planning (CMAP) - http://www.cmap.illinois.gov/programs-and-resources/lta/ ghn-chicago.
Courses of action in “reinvest” scenario
Courses of action in “preserve” scenario
Courses of action in “innovate” scenario
Infrastructure
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Themes by topic and course of action (CMAP Scenario Construction Notes (2008)).
types of assessments have been compiled, analyzed, and prepared as shapefiles. These indicators included land consumption, open space, air quality, congestion, and environmental justice. The specific variables were chosen based on feedback from experts and the community. Additionally, GIS software allowed planners to stipulate certain “floors” and “ceilings” for input data. For example, certain minimums for education funding and safety and security funding were put in place to ensure that scenarios would produce outcomes that underfunded important services. For example, a sample of the “Land use inventory” dataset is shown below (Fig. 5). It was used to both assess current land use and provide a basis for future land uses according to the goals of each scenario. CMAP used GIS to construct the dataset by combining parcel-level data from each of the seven counties in CMAP’s jurisdiction. Additionally, each scenario stipulated different goals for the amount of parkland and open space. GIS was employed to determine available and potential future parks. First, all undeveloped and park space in the region was identified. Next, spatial processing was
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Fig. 5 A commercial strip on Chicago’s South Side. Left: aerial photograph. Center: “polygon-based” land use from 2005. Right: “parcel-based” 2010 land use; dark gray represents road and alley rights-of-way. Source: CMAP Land Use Inventory - http://www.cmap.illinois.gov/data/land-use/ inventory.GIS and Scenario Analysis: Tools for Better Urban Planning.
used to divide the region into subzones of 160 acres. Subzones that were “undeparked” according to the standards set by the National Parks and Recreation Association were identified. Once the total amount of lacking park space was identified, CMAP professionals identified the areas where new parkland should be added with a map that projected 13,000 new park acres in the entire region. These areas were targeted to add between 5 and 25 acres for a grand total nearly 13,000 new park acres in the entire region.
2.17.4.3
Modeling, Assessment, and Site Selection
Each scenario was designed to represent a plausible future state of the Chicago region based on one of the three overriding themes of the project, and each scenario produced several informational maps. The majority of these maps were created with modeling tools. GIS-based modeling allows the scenario designers to efficiently create a dataset of potential future built environment patterns without the need for manual categorization. When planning at the regional scale, where such a task would be very time-consuming, modeling becomes an efficient solution. In many cases, the scenario planning process creates alternative scenarios based on a single theme or interest. For example, a scenario planning exercise could produce an environmental scenario, an economic development scenario, and an equity scenario. But when envisioned without the aid of GIS software, these scenarios often serve as unrealistic extreme illustrations of these policies, meant to inspire discussion rather than depict realizable future states. The three scenarios produced by GO TO 2040 depicted future states that were easier to grasp for the layperson than scenarios that are designed without GIS-based planning tools. The scenarios produced consisted of an achievable balance between various competing topics and concerns, although they lacked the visual impact of simpler scenarios meant to convey an important idea, such as growth management or environmental preservation. CMAP has a number of travel demand models which illustrate the above approach. Travel demand models are a set of computational tools used to predict loads on the transportation network under a variety of socioeconomic conditions and public policy scenarios. Between 2010 and 2040, planners estimated that the Chicago region would grow by about two million people. The travel demand scenarios produced by the GO TO 2040 sought to address how the 284 communities in the Chicago region would be affected by this growth and how future transportation decisions would seek to address these effects. The trip-based models generated by CMAP’s scenario generation are useful in evaluating long-range regional planning strategies, as well as estimate future air quality. The scenarios generated rely on several submodels, such as estimations of household composition, household income, vehicle ownership, pedestrian friendliness, and transit access. Travel demand modeling in the Chicago region predates GIS software, most notably with the large-scale Chicago Area Transportation Study of the 1950s and 1960s. GIS has been employed to maintain the Chicago region’s travel demand database since the 1990s. According to CMAP “current trip-based models are used to evaluate long-range regional planning strategies and to estimate transportation contributions to regional air quality.” The current iteration was created in 2006 by CMAP. Current applications include GIS databases that provide highway network and transit service information that are processed together to produce network models and employed in a four-step, trip-based demand model that considers trip generation, distribution, mode choice, and assignment. The current CMAP travel demand models rely on GIS format for the street network dataset, which is frequently updated to reflect new construction or new street network features. The extensive street network layer is important for producing accurate land use and transportation scenarios.
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GIS is also employed in these models to produce estimations when there is a lack of data. For example, Census data at the block level does not report several population characteristics necessary for the current model. For example, block-level data does not stipulate the characteristics of the population living in households (a group of people living in a residential structure) and the population living in group quarters (people living in nonresidential structures such as automobiles and transitional shelters). Because many blocks include no people living in group quarters, CMAP’s current model employs an algorithm based on a “near” function that analyzes nearby blocks with no group quarters in order to estimate the age characteristics of those living in households and those living in group quarters. GIS tools were also employed in CMAP’s site selection process for solar energy development on brownfield sites in Will County. First, a GIS layer of state and federal EPA agencies that listed all brownfield sites in the Chicago region was used, as well as parcel data from the Supervisor of Assessments with data on lot size and land value, in addition to layers that depicted EPA Resource Conservation and Recovery Act Facilities, EPA Toxic Release Inventory sites, and Leaking Underground Storage Tank Incident Tracking sites. After combining these layers, GIS was used to implement a prioritization procedure that included nonagricultural and nonresidential land uses, had a high improvement-to-land (I/L) ratio, appropriate slope and location within a floodplain, less than a mile from an existing road, and over two acres in area.
2.17.4.4
Public Outreach and GIS Analysis
The growth of interactive online applications presents the opportunity to open the scenario planning process to a wider audience than was previously possible. Cities and regions have used community engagement software like MetroQuest to depict web-based scenario visualizations and highlight the tradeoffs between different scenarios. The web application allows users to vote on scenarios, create comment tags on maps, and rank priorities. The data generated by users can then be compiled, analyzed, and displayed online. CMAP used Metro Quest web application to display the findings of the scenario analysis process as well as to engage the public in testing their own “what-if” scenarios. Users of the web-interface may change key drivers of scenarios, such as expenditures on infrastructure, to identify their implications on future outcomes. Alternatively, they may choose a set of future outcomes, such as cleaner air, or more compact development, to identify consequent what-would-it-take drivers for outcomes. Using the Metro Quest software suite, CMAP’s planners were able to present extensive visualizations for each scenario, which was helpful during the public engagement phase of the planning project, which consisted of a series of workshops titled “Invent the Future.” CMAP planners presented the simulated outcomes of each subcategory based on each of the three scenario types. After viewing the presentation of each scenario and its components, workshop participants were polled on their preferred scenarios. The GIS analysis was also used to compare scenario outcomes. For example, the amount of farmland and open space consumed by each scenario was compared, was the amount of new growth directed into infill sites, or the estimated amount of water used in each scenario. For example, with GIS-enabled scenarios, CMAP could quantify the different predicted levels of farmland lost in each of the three scenarios, with the Reinvest scenario resulting in the largest loss of about 138,000 acres, and the Innovate scenario resulting in the lightest loss of about 20,000 acres. After public input and evaluation, the most preferable aspects of all three scenarios were used to develop a preferred scenario, which constituted a Regional Vision and aimed to inform the planning and policy goals of GO TO 2040. Using indicators through GIS, planners could ensure that scenarios would meet a certain goal, such as economic growth or environmental preservation. In addition, GIS-based scenario planning allowed planners to identify scenarios with unbalanced results, such as those that predicted great economic growth but with stark negative effects on the environment. As such, planners would be able to develop scenarios that were well-balanced between various and sometimes competing indicators.
2.17.5
Conclusions
Understanding the principles of scenario analysis and how it can be employed in combination with GIS tools can enhance the effectiveness of the planning process. GIS is now an integral part of land-use planning and, more generally, urban and regional analysis. In the planning process, GIS tools are often used in combination with other planning techniques to display the future outcomes of present-day actions or to allow participants in the process to specify or alter the causal relationships between components of the urban system. These considerations often involve key uncertainties and decisions that are linked to one another. Moreover, many aspects of using GIS tools in the planning process require practitioners to go beyond technical analysis and use planning skills such as effective engagement with stakeholder interests and capabilities, assigning different values to different datasets, and telling stories. These are opportunities for using scenarios. Scenarios can be used in combination with GIS tools in a number of ways. First, and most commonly, they can be created using standard GIS tools such as ArcGIS. By using straightforward manipulations in spatial datasets, users may create land-use outcomes that are different for current conditions. Second, planning support systems designed specifically for scenario planning allow planners to quickly and efficiently create and compare different scenarios, as well as analyze scenarios along a range of important variables such as projected costs or carbon emissions. Their increased interactive power can help planners create more engaging public presentations by allowing them to modify scenarios based on stakeholder opinions or suggestions in real time. Finally, computationally intensive urban models often use underlying GIS datasets, operationalize relationships between components of the urban system, and extend past growth trends into the future. These models provide a baseline scenario for comparison and, when correctly calibrated, can also provide sound and defensible future projections.
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As the CMAP GO TO 2040 example illustrates, scenario analysis users maydand shoulddemploy more than one GIS tools because each of them serves a different and often complementary purpose in the planning process. Basic GIS tools can help survey existing conditions and provide a foundation for the scenario analysis process. Urban models provide a set of baseline scenarios for thinking about one possible set of future conditions. Urban models with interactive capabilities may allow planners to change parameters and generate a number of other exploratory scenarios. Planning support systems can add to this process by allowing other stakeholders to create scenarios more heuristically and compare them with scenarios generated through other means. The range of scenarios generated through the above mechanisms can serve a variety of purposes in the planning process. They may help decision-makers identify what set of policies will help achieve the desired outcomes, what policies or investments may work under a range of future uncertainties (robust decisions), or just provide a set of inputs for planning discussion.
References Bartholomew K (2005) Integrating land use issues into transportation planning: Scenario planning. Federal Highway Administration. http://faculty.arch.utah.edu/bartholomew/SP_ SummaryRpt_Web.pdf. (Accessed on 12/10/2016). Berke PR, Godschalk DR, Kaiser EJ, and Rodriguez DA (2007) Chapter 4: Planning Support Systems, In Urban Land Use Planning, Figure 4-2, pp. 91. University of Illinois Press. Bihamta, N., Soffianian, A., Fakheran, S., Gholamalifard, M., 2015. Using the SLEUTH urban growth model to simulate future urban expansion of the Isfahan metropolitan area, Iran. Journal of the Indian Society of Remote Sensing 43 (2), 407–414. Börjeson, L., Höjer, M., Dreborg, K.-H., Ekvall, T., Finnveden, G., 2006. Scenario types and techniques: Towards a user’s guide. Futures 38 (7), 723–739. Chakraborty, A., 2010. Scenario planning for effective regional governance: Promises and limitations. State and Local Government Review 42 (2), 156–167. Chakraborty, A., McMillan, A., 2015. Scenario planning for urban planners: Toward a practitioner’s guide. Journal of the American Planning Association 81 (1), 18–29. https://doi. org/10.1080/01944363.2015.1038576. Chakraborty, A., Wilson, B., Bin Kashem, S., 2015. The pitfalls of regional delineations in land use modeling: Implications for Mumbai region and its planners. Cities 45, 91–103. Chicago Metropolitan Agency for Planning (2008) “Scenario Construction Notes”, http://www.cmap.illinois.gov/documents/10180/29783/ScenarioConstruction.pdf/7a72ec7a090c-4676-9e5e-d5006b9a6901 (Accessed April 14, 2017) Hopkins, L.D., Zapata, M.A., 2007. Engaging the future: Tools for effective planning practices. In: Engaging the future: Forecasts, scenarios, plans, and projects. Lincoln Institute of Land Policy, Cambridge, MA, pp. 1–17. Isserman, A.M., 1985. Dare to plan: An essay on the role of the future in planning practice and education. Town Planning Review 56 (4), 483. Jantz, C.A., Goetz, S.J., Donato, D., Claggett, P., 2010. Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Computers, Environment and Urban Systems 34 (1), 1–16. Kahn, H., 1962. Thinking about the unthinkable. Horizon Press, New York. Klosterman, R.E., 1994. Large-scale urban models retrospect and prospect. Journal of the American Planning Association 60 (1), 3–6. Klosterman, R.E., 2013. Lessons learned about planning: Forecasting, participation, and technology. Journal of the American Planning Association 79 (2), 161–169. https://doi.org/ 10.1080/01944363.2013.882647. Malinga, R., Gordon, L.J., Lindborg, R., Jewitt, G., 2013. Using participatory scenario planning to identify ecosystem services in changing landscapes. Ecology and Society 18 (4). https://doi.org/10.5751/ES-05494-180410. Oregon Department of Transportation (ODOT). (2013). Oregon scenario planning guidelines: Resources for developing and evaluating alternative land use and transportation scenarios. http://www.oregon.gov/ODOT/TD/OSTI/docs/ODOT-GuidelinesApril2013.pdf. (Accessed on 12/10/2016). Quay, R., 2010. Anticipatory governance: A tool for climate change adaptation. Journal of the American Planning Association 76 (4), 496–511. https://doi.org/10.1080/01944363. 2010.508428. Schwartz, P., 1996. The art of the long view: Paths to strategic insight for yourself and your company. Richmond Ventures, North Sydney. Shipley, R., Feick, R., Hall, B., Earley, R., 2004. Evaluating municipal visioning. Planning Practice and Research 19 (2), 195–210. Stewart, T.J., Scott, L., 1995. A scenario-based framework for multicriteria decision analysis in water resources planning. Water Resources Research 31 (11), 2835–2843. Throgmorton, J.A., 1992. Planning as persuasive storytelling about the future: Negotiating an electric power rate settlement in Illinois. Journal of Planning Education and Research 12 (1), 17–31. Van der Heijden K (1996) Scenarios: The art of strategic conversation. http://documents.irevues.inist.fr/handle/2042/32161. (Accessed on 12/10/2016). Wu, X., Hu, Y., He, H., Xi, F., Bu, R., 2010. Study on forecast scenarios for simulation of future urban growth in Shenyang City based on SLEUTH model. Geo-Spatial Information Science 13 (1), 32–39. Zhan Q and Zhou J (2010) Research on GIS based scenario planning methods and applications. In Management and Service Science (MASS), 2010 International Conference on, pp. 1–4. IEEE. https://www.researchgate.net/profile/Q_Zhan/publication/251954783_Research_on_GIS_Based_Scenario_Planning_Methods_and_Applications/links/ 00b7d536a566e8f1a7000000.pdf. (Accessed on 12/10/2016).
GIS and Scenario Analysis: Tools for Better Urban Planning
Arnab Chakraborty and Andrew McMillan, University of Illinois at Urbana-Champaign, Champaign, IL, United States © 2018 Elsevier Inc. All rights reserved.
2.17.1 2.17.2 2.17.3 2.17.3.1 2.17.3.2 2.17.3.3 2.17.3.4 2.17.4 2.17.4.1 2.17.4.2 2.17.4.3 2.17.4.4 2.17.5 References
Introduction Principles: Scenario Analysis in Urban Planning Techniques: GIS and Planning Analysis Basic GIS Software Packages Planning Support Systems Urban Models Using Tools to Compare and Assess Scenarios Application: CMAP’s GO TO 2040 Plan Overview of the Project How GIS Tools Were Employed in Each Task Modeling, Assessment, and Site Selection Public Outreach and GIS Analysis Conclusions
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Glossary Civic Technology: Information technology applications that enable engagement or participation of the public. Computer Modeling Tools: Tools that have computing capabilities to model the interaction of multiple urban phenomena and their response to policy interventions and uncertainties. Explorative Scenarios: Scenarios that broadly identify “what can happen?” (Börjeson et al., 2006) or develop not just likely or plausible futures but possible futures. Normative Scenarios: Scenarios that start with a well-defined target or seek participant inputs to first identify targets. Open Data: Data available freely to use and republish, without restrictions from copyright, patents, or other mechanisms of control. Planning Support Systems: Interactive computer-based tools that can provide mapping and analysis capabilities. Predictive Scenarios: Scenarios designed to depict the most likely future based on data-driven trends and input from actors (forecasting), or if the future outcomes are a direct result of decisions made in an earlier period. Qualitative Tools: Tools that primarily employ data gathered through interviews, essays, or opinion surveys, or constructed through narrative forms such as stories or personal experiences.
I agree with a growing number of scholars who suggest that planners abandon the futile effort to predict what the future will be and prepare a range of scenarios suggesting what the future may be. (Klosterman 2013, p. 164)
2.17.1
Introduction
Scenario planning allows us to consider multiple facets of a complex problem, imagine alternate possibilities, and study the future impacts of present-day decisions in light of important unknowns and uncertainties. Scenario planning is widely used in disciplines ranging from business and military planning to organizational behavior as a tool that allows participants to think critically about how a future might unfold (van der Heijden, 1996) and to identify new insights or strategies (Malinga et al., 2013). Urban planners and policymakers use scenario planning in a number of ways, but its most common use is in regional comprehensive planning (Chakraborty and McMillan, 2015). That is the angle this article deals with most closely. Scenario analysis in regional planning can be useful (1) to assess how urban areas may change in the future, (2) to explore how uncertainties about the future and interdependence between parts of the urban systems may lead to different possible outcomes, (3) to analyze how different planning and policy options impact the outcomes, and (4) to involve stakeholders in the learning and decision-making process. To achieve these goals, scenario planning activities in regional planning often rely on geographic information systems (GIS). This is because scenarios are most commonly expressed as spatial datasets such as future land-use outcomes. GIS is used to develop these outcomes, create visualizations for use in public or analytical processes, and test their impact on a range of indicators.
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For example, when Chicago Metropolitan Agency (CMAP) conducted a scenario planning process as part of its GO TO 2040 comprehensive plan, it created three scenariosdpreserve, reinvest, and innovatedeach of which, as discussed later, focuses primarily on land-use outcomes. GIS data and tools are also used to model the relationship between the current built environment, land-use, and infrastructure decisions. For example, CMAP used the Land use Evolutions and impact Assessment Model (LEAM) to generate different population distribution trends through 2040 under each of the tested scenarios. Moreover, GIS tools are used to assess the end results or scenarios using a set of quality-of-life indicators and to collect inputs from stakeholders and decision-makers. For example, CMAP collaborated with MetroQuest, a private planning tool developer, to display land-use scenarios as web dashboards, with indicator scores for each, and forms for participant to provide inputs about their preferences. Finally, GIS can assist us in understanding how uncertainties about the future may interact with decision choices that planners face and to identify which decisions may work across a range of future uncertainties and which ones may not. For example, models built with GIS can explore how economic trends, such as changing gasoline prices, may increase development in outlying urban areas and how that may increase land prices, congestion, and demand elsewhere within a region. GIS has the potential to advance scenario planning from an analytical framework for analyzing plans to a broader paradigm for better urban planning. So far, use of scenario analysis has remained largely restricted to regional planning applications and focused on the use of tools to develop scenarios with limited utility in the plan-making process. However, when employed effectively, the promise of scenario planning is much greater. GIS can help scenario planning processes involve more diverse stakeholders in a region, make strategic decisions in uncooperative environments, and promote greater citizen engagement. New frontiers of open data and civic technology hold considerable promise for combining scenario analysis and GIS capabilities. Scenario analysis and GIS can also be employed together in planning-related fields such as public health, security, and environmental management. For example, scenario analysis has been used to project the different ways a pandemic can spread, implications of a terrorist attack, or fallouts from different global climate change scenarios. GIS tools can be useful in each of those situations to identify mitigation responses or to develop contingency or adaptation plans. While our focus in this article is on urban planning problems, we believe that a careful reader would be able to extend many of these lessons to other fields. The rest of this article is organized as follows. We start with an overview of scenario applications in urban planning and discuss how GIS provides the backbone of the analytical aspects of such applications. We share some examples on how GIS is used to develop and analyze scenarios. We present a case study of a scenario planning application, specifically highlighting the role of GIS in the process. We conclude with a discussion about some ongoing trends and the promises it makes for advances in GIS and scenario analysis.
2.17.2
Principles: Scenario Analysis in Urban Planning
Much has been written about the benefits and limitations of scenario planning (the terms “scenario planning” and “scenario analysis” are often used interchangeably in the literature. We use scenario analysis to refer to a narrow set of activities, primarily technical components, and scenario planning to refer to the broader planning process that may also involve engagement and implementation considerations); see, for example, Hopkins and Zapata, 2007; Klosterman, 2013; Schwartz, 1996; van der Heijden, 1996. Scenario planning was pioneered by the RAND Corporation (Kahn, 1962). It is a tool meant to foster the imagination and spur critical thinking about how the future might unfold. Scenario planning, forecasting, visioning, and alternative testing are some of the distinct approaches for future-oriented analysis that have become commonplace in urban planning (Bartholomew, 2005; Isserman, 1985; Shipley et al., 2004; Throgmorton, 1992; Quay, 2010). These approaches can be used by the participants in a plan-making process to formulate shared goals, simulate future outcomes of present-day decisions, address systemic uncertainties, and identify robust strategies (Hopkins and Zapata, 2007). Planners’ use of scenario analysis is, in many ways, a response to the critiques of planning for a single and agreed-upon future (Hopkins and Zapata, 2007). The scenario planning approach allows planners to create alternative but possible futures, and advance several goals over the predict-and-plan approach. The following notes from Oregon Department of Transportation or ODOT’s (2013, p. 6) recent scenario planning handbook for municipal governments illustrates the key benefits of scenario planning. “. scenario planning . allows a community to look long-term and envision the future it wants, rather than accept the trend line embodied in most existing plans. Scenario planning is not about predicting the future or providing a specific answer. Rather, it is a methodology for “seeing” futures not easily estimated using past trends or assumptions. The expectation is that through the process of conceiving, developing, and evaluating a series of future scenarios and the outcomes they produce, a preferred and feasible course of action can be identified.” While this approach improves on past practices of planners to construct and analyze a single prediction, it nonetheless promotes the idea of identifying a single “preferred” vision among the scenarios created. The clearest purpose of this “preferred” vision for the future is to serve as a guide for plans and policies that will steer development closer to this shared vision. This practice is known as normative scenario planning. A complementary and sometimes alternative approach to this is “exploratory scenario planning,” which aims to generate scenarios that broadly identify what can happen (Börjeson et al., 2006), as opposed to what is likely or plausible. These processes often incorporatedexplicitlydvariations in critical uncertainties and identify how the same policy may produce different outcomes based on variations in uncertainties (normative and exploratory scenarios build on traditional predictive approaches that are designed to depict the most likely future based on data-driven trends and input from actors, or if the future outcomes are a direct result of decisions made in an earlier period).
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Resources
Context Organizational structure Primary decisions
Outcome
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Secondary decisions Scenario type
Participation extent
Stakeholder engagement
Engagement medium
Scenario tools
Fig. 1 A scenario planning typology. From Chakraborty, A., and McMillan, A. (2015). Scenario planning for urban planners: Toward a practitioner’s guide. Journal of the American Planning Association, 81(1), 18–29. doi:10.1080/01944363.2015.1038576.
According to Chakraborty and McMillan (2015), scenario planning projects can vary widely. Fig. 1 illustrates a typology or key components of a scenario planning process. The typology shows that aspects of the project may be constrained by the context in which the scenario process is situated. For example, project “scope” or whether the key questions concern land-use policy choices or transportation investment decisions may be a given considering the goals of the process. However, planners may have to choose whether to look at normative or exploratory scenarios, or whether to engage with general public or selected stakeholders. The typology suggests making these “primary” decisions about how the broader process is scoped and structured before selecting the tools. Finally, while different parameters of the scenario planning processes are made iteratively in practice, the general guidance is that the needs of the planning process should set the parameters of the technical tools, and not the other way round. Within the broader framework of scenario planning, GIS tools have two basic roles. One is developing and analyzing scenarios, where the primary focus is on technical capacity. The other role is using scenario planning to engage the public in the planning process, where the focus may be more on community engagement. Many of the GIS tools attempt to serve both of those roles whereas others may specialize in one of those two roles. The next section discusses these roles and tools in more detail.
2.17.3
Techniques: GIS and Planning Analysis
GIS are commonly used to categorize, analyze, and visualize urban systems. GIS datasets in an urban area may include transportation networks, existing land-use, and critical environmental resources. Data can also contain spatial constraints and policy measures on development such as floodplains and zoning regulations, respectively. Since such datasets and the knowledge about their relationships are fundamental to planning analysis (see Fig. 2), GIS has become an essential component of urban planning. Scenario planning relies on tools that offer planners the ability to view complex data, to project potential outcomes of decisions and anticipate their implications, and to communicate these to the wider public. Central to these are tools that can forecast or illustrate and evaluate land-use change over space and time. Such tools offer planners and stakeholders the ability to view and assess potential future outcomes of policy decisions or investment choices before final decisions are made about implementation. In short, GIS tools provide the ability to construct and demonstrate scenarios. There are several GIS-aided scenario planning platforms and software applications, ranging from standard GIS software packages such as ArcGIS to highly specialized modeling applications. The following section describes how scenario planning is typically Computer software
Computer hardware
GIS
Geographic data
Technical staff
Monitoring Recording Measuring Analyzing Mapping Modeling Communicating
Planning intelligence
Fig. 2 GIS and planning analysis. From Berke PR, Godschalk DR, Kaiser EJ, and Rodriguez DA (2007). Chapter 4: Planning Support Systems, In Urban Land Use Planning, Figure 4-2, pp. 91. University of Illinois Press.
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handled through three types of GIS applications. First, with standard ArcGIS software, then with specialized GIS-based planning support systems, and finally with software tools that employ more sophisticated modeling approaches. Each of these utilities has their own suitability context. Scenario planning with the ArcGIS software is useful in that it does not require additional training beyond a familiarity with ArcGIS. Scenario-based planning support systems offer the ability to develop scenarios more efficiently, control for a wider range of relevant variables, and produce more detailed analysis than standard GIS packages such as ArcGIS. But these systems may be proprietary and require additional training. Finally, modeling tools offer the ability to generate land-use and land cover scenarios that are theoretically sound and can produce the most likely outcomes. But these models also require additional skill development, may lack the easy customization available on planning support systems, and doesn’t allow for users to customize or edit scenario outcomes. Each type of scenario planning application is described in the following section.
2.17.3.1
Basic GIS Software Packages
Standard GIS software packages like the popular ArcGIS program generally have little in the way of tools specifically designed for scenario development and analysis. But the suite of tools available do allow for some rudimentary scenario creation and analysis. ArcGIS is advantageous in that it is already widely used by planners and other practitioners. At its simplest, ArcGIS users can create layers to represent different scenarios, such as different potential future land-use patterns. These layers are essentially “hand-drawn,” and they will lack predictive output data that allows for a detailed quantitative analysis of alternative scenarios. But ArcGIS’s extensive map layout capabilities are suitable for producing visualizations that can be presented to stakeholders or the general public. ArcGIS also comes with the tools for the creation and analysis of multicriteria decision analysis (MCDA), a process that is often a component of scenario planning exercises. MCDA is a decision-making methodology that attempts to identify the most suitable sites for a planning project while acknowledging conflicting goals or preferences. Rendered in ArcGIS, MCDA can be visualized by overlaying several layers representing social or physical aspects of the urban environment. The variables associated with the layers are weighted based on their importance, and the creation of multiple scenarios will result from changing the weights of the variables. For example, Zhan and Zhou (2010) used ArcGIS to create three future development scenarios for the Houguan Lake Ecological District in China. Variables included data on developable areas, water environment, and biological environment. Three scenarios identifying preferred areas for development were created that addressed three broad themes: (1) the district as a residential area, (2) the district as a tourist destination, and (3) the district as a green industry hub. These three outcomes were produced by modifying the weights of the input variables. While the standard ArcGIS package lacks tools solely focused on scenario development, there are features and extensions that can aid in scenario creation and analysis. GeoPlanner for ArcGIS is an online app that allows for the creation and addition of layers and the easy modification of layers for the creation of different scenarios. Scenarios can be compared side-by-side along a range of preselected variables. Additionally, the ArcGIS 3D Analyst extension, a package of tools that aid in visualization, can offer visualizations suitable for scenario presentation. The 3D Analyst is capable of producing scenes based on common shapefiles such as building footprints, digital elevation models, or aerial images, and is capable of rendering a range of urban form aspects, such as fences or streets as well as producing surface-conforming polygons. While the primary ArcGIS lacks the detailed scenario creation and analysis components of more specialized scenario planning programs, it is well-suited for the creation of multiple scenarios related to site selection processes or suitability analysis. Siting certain landmarks requires a substantial amount of spatial data, such as land use and other regulations, environmental factors, economic factors, engineering factors, and social and cultural considerations. And land-use suitability or site selection allows for these characteristics to be analyzed collectively. Site selection is typically composed of two parts: identification of potential sites based on certain criteria, and an analysis of each candidate site’s suitability. The site selection process typically uses the integration of multiple map overlays to identify acceptable candidate sites. Layers can be weighted based on relative suitability or unsuitability. ArcGIS’s system based on multiple layers is amenable to the creation of several siting scenarios, or the ability to use a combination of multiple layers to identify suitable land for a particular use.
2.17.3.2
Planning Support Systems
Spatial analysis tools in GIS and other statistical approaches may be employed to understand the relationships between different components of the urban system, such as between high-density development and pedestrian activity. This knowledge can be programmed into GIS software such as ArcGIS or used in combination with other spreadsheet and database analysis tools such as Microsoft Excel or Access. However, scenario planning practices in urban planning that employ GIS often rely on specifically created tools that are called planning support systems, such as Envision Tomorrow or CommunityViz. Planning support systems are generally defined as GIS tools specifically tailored to meet the specific needs of planners. In the past two decades, several planning support systems have been developed for creating and analyzing planning and planning-related scenarios. As specialized tools, PSSs typically offer more efficient methods of scenario development and analysis as well as a wider range of analysis options, although their sophistication requires additional training in order to use. The primary advantage of PSSs over the basic ArcGIS software package is that many offer users the ability to “sketch” aspects such as different land uses on the fly. While ArcGIS allows users to create different land-use layers, the process can be time-consuming and difficult to edit. In contrast, the sketching tools contained within scenario planning PSSs allow users to quickly add and edit land uses.
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CommunityViz, developed by the Placeways planning consultants, is a proprietary software application that operates as an ArcGIS extension. It is primarily a land-use analysis and decision-making tool that allows users to view the predicted impact of various land-use scenarios. Scenarios are created by sketching hypothetical land-use scenarios within a prescribed area. The characteristics of these land-use types, such as the number of households, property tax revenue, and total energy use, can be determined by the user. While sketching, users receive dynamic feedback of predicted variables based on certain assumptions and input values. This could include the predicted number of new jobs, estimated traffic congestion, as well as resource analysis such as predicted water and energy use. CommunityViz can also operate as a suitability analysis tool that employs weighted measurements to separate layers. Envision Tomorrow is another popular scenario planning ArcGIS extension. Developed by Fregonese Associates, it is free and open access. The program is designed to make use of data that is typically accessible to both planners, such as tax assessor parcel data and Census data. Similar to CommunityViz, users sketch predefined land uses within a defined geographical area. Envision Tomorrow allows users to define several building types along several sets of values, such as population density, rent or sale price, or energy and water use. Combinations of these building types are bundled into different development types, such as mediumdensity residential or town center. These development types are then sketched onto a map. Predicted social, physical, and economic outputs are generated on the fly as development types are added or removed from a map. Scenarios can then be compared along fiscal, demographic, or land-use values. Since debuting in 2006, Envision Tomorrow’s open source status has made continual refinement and revision possible. Envision Tomorrow has been used in many regional planning initiatives. Increasingly, GIS and planning applications have employed online access as a means of quickly and effectively displaying and disseminating data, as well as collaboration. The Sustainable Places Analytical Resources Core (SPARC) is a web-based, open-source GIS data processing storage service developed by Criterion Planners. No GIS software is required, as the application is accessed entirely through a web-browser. The webapp, once set up, offers access to open-access national datasets, as well as data storage. The data management component includes the ability to transform and normalize geographic data, making collecting and utilizing data from several different organizations for a single planning project possible. SPARC’s scenario planning tool, INDEX, also available as a standalone PSS, allows users to “paint” land-use and transportation scenarios within a determined area. Since the application is entirely web-based, collaboration between multiple users is possible. Similar to other scenario planning applications, the INDEX tool provides basic layer geoprocessing functions, such as layer clipping or merging. Land-use types can be classified and linked to indicators, such as population density, development mix, or vehicle miles traveled. Indicators are dynamically calculated as land-use types are added, removed, or edited during the scenario constriction process, and scenarios can be ranked along important social or physical indicators.
2.17.3.3
Urban Models
Urban models consist of GIS software that can aid the scenario planning process by constructing scenarios based on algorithms. Unlike planning support systems, the scenarios generated by urban models are generated based on a set of theorized causeand-effect relationships and predictions (Klosterman, 1994) rather than by allowing a user to sketch proposed land uses. While the nature of these applications typically does not allow for users to decide precisely where and when land-use classification or land-use change will occur, the land-use changes produced by these models are based on calculations that are determined to be based on theoretical and empirical foundations, although nearly all models must be calibrated to its local context in order to be effective. SLEUTH is a cellular automata terrain and land-cover mapping model that generates predicted patterns of urban growth (Jantz et al., 2010; Chakraborty et al., 2015). The model employs physical input data, such as slope, and historical data, such as land-use type, urbanized area, and transportation infrastructure, to produce predicted future land-use and land-cover changes. SLEUTH has been particularly popular for predicting the land-use and land-cover impacts of different policy scenarios. For example, Wu et al. (2010) used SLEUTH and data from 1988 to 2004 to model predicted growth in Shenyang City, China, out to 2030 based on (1) a current trends scenario, (2) a regional policy scenario, and (3) an environmental protection scenario. Similarly, Bihamta et al. (2015) used historical data from 1976 to 2010 to model urban growth in Isfahan, Iran, based on (1) a current trends scenario and (2) a compact development scenario.
2.17.3.4
Using Tools to Compare and Assess Scenarios
One of the primary functions of a GIS-based tool is to compare and assess scenarios. Such assessments are usually based on a set of performance indicators (Chakraborty and McMillan, 2015). These indicators may capture simple measures of change in a key attribute of the system, such as land converted from agricultural to residential use, or estimates of the broader impact of changes, such as on fiscal conditions or air quality. Basic tools, such as the ArcGIS program, can generate many measures of change using the data layers that represent before and after conditions. The participants can perceive these changes visually, for example, a compact development pattern versus a sprawled one, or using basic metrics such as acres of land converted under each scenario. Most planning support systems have a preprogrammed set of indicators that are a part of the toolbox or are custom-developed for a place based on its unique conditions or issues of interests. Indicators preprogrammed into these tools can be generated on the fly or after each step in the scenario development process, offering participants a way to iteratively achieve a desirable value on a particular set of indicators. For
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example, stakeholders aiming to allocate 10,000 new people to a community using CommunityViz may “paint” higher density patterns and score high on land preservation indicator, or choose the single family option and score low. Estimates of impacts of scenarios that go beyond simple measurements require additional capabilities. Some PSS tools offer in-built impact assessment models for a number of estimated impacts that can be, with additional effort or for a fee, calibrated to the local context. For example, Envision Tomorrow offers additional measures called “Location Efficiency,” “Regional Fiscal Impact Tool,” and “Public Health.” The fiscal impact tool, for example, models local finance as a function of “tax rates and municipal population as well as scenario outputs relating to population, employment, and property value of new construction” (for more information on RIFT model of Envision Tomorrow, please visit: http://envisiontomorrow.org/fiscal-impact-model/ (accessed 20 February 2017). Most planning processes use a variety of indicators that include environmental, economic, and social impacts. A number of frameworks exist on how to compare scenarios with multiple indicators. These include assigning weights to different indicators and comparing them using a multicriteria decision-making approach (Stewart and Scott, 1995), or prioritizing them on the core values of the community, which can in turn be identified through a visioning process (Chakraborty, 2010). Chakraborty et al. (2015) suggest that stakeholders should clearly identify factors that are within their control and those that are beyond their control in identifying how to use scenarios effectively. According to this approach, decisions that provide superior outcome across all indicators and under all possible uncertainties are robust decisions. However, they argue that decisions that provide better outcomes under some indicators or under some future conditions may also be used for contingency planning. Ultimately, which indicators are employed to assess scenarios and how their results are compiled and used to make policy should be decided locally.
2.17.4
Application: CMAP’s GO TO 2040 Plan
Regional planning agencies like the Chicago Metropolitan Agency for Planning (CMAP) show the value of GIS-supported scenario planning in long-range and comprehensive planning. Such planning activities offer useful case studies because they directly engage questions of interrelationships between land-use and transportation planning, stakeholder engagement, as well as long-range regional planning where the combination of GIS and scenario analysis has been most popular. But this emerging framework can grow complex and unmanageable. Scenario planning offers a method for addressing the complicated relationship between land use and transportation without losing sight of important broad themes, such as environmental protection or regional equity.
2.17.4.1
Overview of the Project
The Chicago Metropolitan Agency for Planning was established in 2005 by combining the Northeastern Illinois Planning Commission and Chicago Area Transportation Study (Fig. 3). Part of CMAP’s responsibility includes reviewing and approving projects in the Chicago region that use federal transportation funds. But the organization’s area of concern extends beyond transportation planning to encompass goals such as more livable communities, enhanced human capital, and better government efficiencies. One of CMAP’s founding goals is to provide better integration between land use and transportation planning for the Chicago region. As such, CMAP’s first major undertaking was GO TO 2040, an update of the Chicago region’s comprehensive plan that would reflect these new priorities. The plan proved to be major undertaking in that it involved consulting with several partner organizations and numerous outreach efforts to the general public in order to develop recommended policies and investments. Scenario planning played a key role in GO TO 2040, as scenarios allowed CMAP to address issues raised by comprehensive planning, such as the interdependence of several planning decisions, potential impacts of different decisions, as well as involving a range of stakeholders. Before constructing scenarios to compare, planners must agree on certain themes, topics, and parameters that are important to the scenario planning project. When developing scenarios, CMAP officials had to address several important questions, such as identifying the overarching themes that would define each scenario, the components that both define the scenarios as well as highlight their differences, and parameters for the variables that make up these components. A number of working committees with stakeholders throughout the region helped identify the key components that would be used to define each scenario. After stakeholder engagement and opinion polling, CMAP officials created three broad themes that defined each scenario (Fig. 4). The “Preserve” scenario sought to accommodate growth without drastic change to region’s level of urbanization. Characteristics included moderate density increases in built-up areas, extended transit access, and extensive open space preservation and habitat restoration. The “Reinvest” scenario sought to extensively increase density in existing areas and prioritize transit-oriented development throughout the region. The “Innovate” scenario prioritized outward growth with an emphasis on green building and a reliance on emerging technologies to mitigate the negative environmental effects of sprawl. The scenarios were not meant to be mutually exclusive and the final plans were achieved by combining the best of all three scenarios.
2.17.4.2
How GIS Tools Were Employed in Each Task
The scenarios consisted of several of the same subcomponents related to important planning issues, such as land use, transportation, and energy plans, and they differed based on the goals of each scenario. For example, the land-use plan in the Reinvest Scenario called for intense TOD, while in the Preserve Scenario it called for moderate infill, and in the Innovate Scenario, it called for conservationist design. To create scenarios based on these subcategories, CMAP officials used 250 regional indicators, where 53 datasets on different
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McHenry
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Lake
Kane DuPage Chicago
Cook Kendall
Will
Aux Sable Township Grundy County
Fig. 3 CMAP jurisdiction. Source: Chicago Metropolitan Agency for Planning (CMAP) - http://www.cmap.illinois.gov/programs-and-resources/lta/ ghn-chicago.
Courses of action in “reinvest” scenario
Courses of action in “preserve” scenario
Courses of action in “innovate” scenario
Infrastructure
Human capital
Innovation
Conservation — land use and transportation
Conservation — buildings
Clean energy
Accessibility
Biodiversity
Green development
Housing
Transit oriented development
Preservation of affordability
Green housing
Land use
Very dense
Moderately dense
Least dense
Major capital investment
Management and operations
Technology
Theme Economic development Energy Environment
Transportation Fig. 4
Themes by topic and course of action (CMAP Scenario Construction Notes (2008)).
types of assessments have been compiled, analyzed, and prepared as shapefiles. These indicators included land consumption, open space, air quality, congestion, and environmental justice. The specific variables were chosen based on feedback from experts and the community. Additionally, GIS software allowed planners to stipulate certain “floors” and “ceilings” for input data. For example, certain minimums for education funding and safety and security funding were put in place to ensure that scenarios would produce outcomes that underfunded important services. For example, a sample of the “Land use inventory” dataset is shown below (Fig. 5). It was used to both assess current land use and provide a basis for future land uses according to the goals of each scenario. CMAP used GIS to construct the dataset by combining parcel-level data from each of the seven counties in CMAP’s jurisdiction. Additionally, each scenario stipulated different goals for the amount of parkland and open space. GIS was employed to determine available and potential future parks. First, all undeveloped and park space in the region was identified. Next, spatial processing was
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Fig. 5 A commercial strip on Chicago’s South Side. Left: aerial photograph. Center: “polygon-based” land use from 2005. Right: “parcel-based” 2010 land use; dark gray represents road and alley rights-of-way. Source: CMAP Land Use Inventory - http://www.cmap.illinois.gov/data/land-use/ inventory.GIS and Scenario Analysis: Tools for Better Urban Planning.
used to divide the region into subzones of 160 acres. Subzones that were “undeparked” according to the standards set by the National Parks and Recreation Association were identified. Once the total amount of lacking park space was identified, CMAP professionals identified the areas where new parkland should be added with a map that projected 13,000 new park acres in the entire region. These areas were targeted to add between 5 and 25 acres for a grand total nearly 13,000 new park acres in the entire region.
2.17.4.3
Modeling, Assessment, and Site Selection
Each scenario was designed to represent a plausible future state of the Chicago region based on one of the three overriding themes of the project, and each scenario produced several informational maps. The majority of these maps were created with modeling tools. GIS-based modeling allows the scenario designers to efficiently create a dataset of potential future built environment patterns without the need for manual categorization. When planning at the regional scale, where such a task would be very time-consuming, modeling becomes an efficient solution. In many cases, the scenario planning process creates alternative scenarios based on a single theme or interest. For example, a scenario planning exercise could produce an environmental scenario, an economic development scenario, and an equity scenario. But when envisioned without the aid of GIS software, these scenarios often serve as unrealistic extreme illustrations of these policies, meant to inspire discussion rather than depict realizable future states. The three scenarios produced by GO TO 2040 depicted future states that were easier to grasp for the layperson than scenarios that are designed without GIS-based planning tools. The scenarios produced consisted of an achievable balance between various competing topics and concerns, although they lacked the visual impact of simpler scenarios meant to convey an important idea, such as growth management or environmental preservation. CMAP has a number of travel demand models which illustrate the above approach. Travel demand models are a set of computational tools used to predict loads on the transportation network under a variety of socioeconomic conditions and public policy scenarios. Between 2010 and 2040, planners estimated that the Chicago region would grow by about two million people. The travel demand scenarios produced by the GO TO 2040 sought to address how the 284 communities in the Chicago region would be affected by this growth and how future transportation decisions would seek to address these effects. The trip-based models generated by CMAP’s scenario generation are useful in evaluating long-range regional planning strategies, as well as estimate future air quality. The scenarios generated rely on several submodels, such as estimations of household composition, household income, vehicle ownership, pedestrian friendliness, and transit access. Travel demand modeling in the Chicago region predates GIS software, most notably with the large-scale Chicago Area Transportation Study of the 1950s and 1960s. GIS has been employed to maintain the Chicago region’s travel demand database since the 1990s. According to CMAP “current trip-based models are used to evaluate long-range regional planning strategies and to estimate transportation contributions to regional air quality.” The current iteration was created in 2006 by CMAP. Current applications include GIS databases that provide highway network and transit service information that are processed together to produce network models and employed in a four-step, trip-based demand model that considers trip generation, distribution, mode choice, and assignment. The current CMAP travel demand models rely on GIS format for the street network dataset, which is frequently updated to reflect new construction or new street network features. The extensive street network layer is important for producing accurate land use and transportation scenarios.
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GIS is also employed in these models to produce estimations when there is a lack of data. For example, Census data at the block level does not report several population characteristics necessary for the current model. For example, block-level data does not stipulate the characteristics of the population living in households (a group of people living in a residential structure) and the population living in group quarters (people living in nonresidential structures such as automobiles and transitional shelters). Because many blocks include no people living in group quarters, CMAP’s current model employs an algorithm based on a “near” function that analyzes nearby blocks with no group quarters in order to estimate the age characteristics of those living in households and those living in group quarters. GIS tools were also employed in CMAP’s site selection process for solar energy development on brownfield sites in Will County. First, a GIS layer of state and federal EPA agencies that listed all brownfield sites in the Chicago region was used, as well as parcel data from the Supervisor of Assessments with data on lot size and land value, in addition to layers that depicted EPA Resource Conservation and Recovery Act Facilities, EPA Toxic Release Inventory sites, and Leaking Underground Storage Tank Incident Tracking sites. After combining these layers, GIS was used to implement a prioritization procedure that included nonagricultural and nonresidential land uses, had a high improvement-to-land (I/L) ratio, appropriate slope and location within a floodplain, less than a mile from an existing road, and over two acres in area.
2.17.4.4
Public Outreach and GIS Analysis
The growth of interactive online applications presents the opportunity to open the scenario planning process to a wider audience than was previously possible. Cities and regions have used community engagement software like MetroQuest to depict web-based scenario visualizations and highlight the tradeoffs between different scenarios. The web application allows users to vote on scenarios, create comment tags on maps, and rank priorities. The data generated by users can then be compiled, analyzed, and displayed online. CMAP used Metro Quest web application to display the findings of the scenario analysis process as well as to engage the public in testing their own “what-if” scenarios. Users of the web-interface may change key drivers of scenarios, such as expenditures on infrastructure, to identify their implications on future outcomes. Alternatively, they may choose a set of future outcomes, such as cleaner air, or more compact development, to identify consequent what-would-it-take drivers for outcomes. Using the Metro Quest software suite, CMAP’s planners were able to present extensive visualizations for each scenario, which was helpful during the public engagement phase of the planning project, which consisted of a series of workshops titled “Invent the Future.” CMAP planners presented the simulated outcomes of each subcategory based on each of the three scenario types. After viewing the presentation of each scenario and its components, workshop participants were polled on their preferred scenarios. The GIS analysis was also used to compare scenario outcomes. For example, the amount of farmland and open space consumed by each scenario was compared, was the amount of new growth directed into infill sites, or the estimated amount of water used in each scenario. For example, with GIS-enabled scenarios, CMAP could quantify the different predicted levels of farmland lost in each of the three scenarios, with the Reinvest scenario resulting in the largest loss of about 138,000 acres, and the Innovate scenario resulting in the lightest loss of about 20,000 acres. After public input and evaluation, the most preferable aspects of all three scenarios were used to develop a preferred scenario, which constituted a Regional Vision and aimed to inform the planning and policy goals of GO TO 2040. Using indicators through GIS, planners could ensure that scenarios would meet a certain goal, such as economic growth or environmental preservation. In addition, GIS-based scenario planning allowed planners to identify scenarios with unbalanced results, such as those that predicted great economic growth but with stark negative effects on the environment. As such, planners would be able to develop scenarios that were well-balanced between various and sometimes competing indicators.
2.17.5
Conclusions
Understanding the principles of scenario analysis and how it can be employed in combination with GIS tools can enhance the effectiveness of the planning process. GIS is now an integral part of land-use planning and, more generally, urban and regional analysis. In the planning process, GIS tools are often used in combination with other planning techniques to display the future outcomes of present-day actions or to allow participants in the process to specify or alter the causal relationships between components of the urban system. These considerations often involve key uncertainties and decisions that are linked to one another. Moreover, many aspects of using GIS tools in the planning process require practitioners to go beyond technical analysis and use planning skills such as effective engagement with stakeholder interests and capabilities, assigning different values to different datasets, and telling stories. These are opportunities for using scenarios. Scenarios can be used in combination with GIS tools in a number of ways. First, and most commonly, they can be created using standard GIS tools such as ArcGIS. By using straightforward manipulations in spatial datasets, users may create land-use outcomes that are different for current conditions. Second, planning support systems designed specifically for scenario planning allow planners to quickly and efficiently create and compare different scenarios, as well as analyze scenarios along a range of important variables such as projected costs or carbon emissions. Their increased interactive power can help planners create more engaging public presentations by allowing them to modify scenarios based on stakeholder opinions or suggestions in real time. Finally, computationally intensive urban models often use underlying GIS datasets, operationalize relationships between components of the urban system, and extend past growth trends into the future. These models provide a baseline scenario for comparison and, when correctly calibrated, can also provide sound and defensible future projections.
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As the CMAP GO TO 2040 example illustrates, scenario analysis users maydand shoulddemploy more than one GIS tools because each of them serves a different and often complementary purpose in the planning process. Basic GIS tools can help survey existing conditions and provide a foundation for the scenario analysis process. Urban models provide a set of baseline scenarios for thinking about one possible set of future conditions. Urban models with interactive capabilities may allow planners to change parameters and generate a number of other exploratory scenarios. Planning support systems can add to this process by allowing other stakeholders to create scenarios more heuristically and compare them with scenarios generated through other means. The range of scenarios generated through the above mechanisms can serve a variety of purposes in the planning process. They may help decision-makers identify what set of policies will help achieve the desired outcomes, what policies or investments may work under a range of future uncertainties (robust decisions), or just provide a set of inputs for planning discussion.
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