Development of an Environment and energy Geographical Information System (E-GIS) construction model to support environmentally friendly urban planning

Applied Energy 104 (2013) 723–739

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Development of an Environment and energy Geographical Information System (E-GIS) construction model to support environmentally friendly urban planning In-Ae Yeo a, Seong-Hwan Yoon b, Jurng-Jae Yee a,⇑ a b

Department of Architectural Engineering, Dong-A University, 840 Hadan 2-dong, Saha-gu, Busan 604-714, Republic of Korea Department of Architecture, Pusan National University, San 30 Jangjeon 2-dong, Geumjeong-gu, Busan 609-735, Republic of Korea

h i g h l i g h t s " An E-GIS construction model for environmentally friendly urban planning is proposed. " This model includes urban GIS integration, E-GIS DB, and visualization. " The urban GIS integration model consolidates building, land, and topography data. " The E-GIS DB model distributes a mesh DB with environment and energy information. " The E-GIS DB is visualized in 2D/3D space as a planning support system.

a r t i c l e

i n f o

Article history: Received 17 August 2012 Received in revised form 22 October 2012 Accepted 22 November 2012

Keywords: Climate change Low carbon green city Urban planning Planning support system (PSS) Environment and energy information Geographic Information System (GIS)

a b s t r a c t This study proposes a method to create an urban planning support model, applying the Environment and energy Geographical Information System Database (E-GIS DB) through the urban life cycle to reduce energy use for environmentally friendly urban planning. The results deduced from this study are organized as follows. The proposed E-GIS construction model is composed of (a) an urban GIS integration model, (b) an E-GIS DB model, and (c) a visualization model. The urban GIS integration model has the ability to integrate urban GIS constructs as well as to connect and visualize urban planning and the environment and energy DB in 3D space. The E-GIS DB model includes a function to visualize the 2D and 3D information, which is used in the environment and energy planning of a city. To validate the proposed E-GIS construction model, a Korean city undergoing the urban planning process is selected as a case study. An E-GIS DB section with an 8 km  12 km area for the research subject area was constructed in 2D and 3D GIS, and the urban space, climate elements, and energy distribution characteristics are compared. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction After the Kyoto Protocol took effect, energy reducing environmentally friendly building and urban planning has gradually changed from voluntary to mandatory.1 The consideration of energy in urban planning is a global trend due to the revision of energy-related laws/regulations and urban planning and development laws/regulations. However, a system incorporating urban planning and energy ⇑ Corresponding author. Tel.: +82 51 200 7609; fax: +82 51 294 2256. E-mail address: [email protected] (J.-J. Yee). Member states such as the European Union and various other countries, including the United States, Australia, and Korea, obligated to reduce greenhouse gases, have been attempting to enact urban planning regulations that improve the efficiency of buildings’ energy and green development through climate change and green development legislation since the latter half of the 21st century. 1

0306-2619/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apenergy.2012.11.053

planning is still dichotomized. Urban/local energy planning was developed later than urban planning, lacks systematic specificity and suggests conceptual strategies for economic, environmental and energy-related law and policy implementation. Therefore, environmentally friendly city planning needs to facilitate the integration of ‘urban planning’ and ‘energy planning’ into a unified ‘urban/local energy planning’ system, elaborate on a systematic process, and support technology for each process. Based on the above perspectives, the authors of the ‘U-Eco City development project’, a national research and development (R&D) project initiated by the Korean government, established an environmentally friendly urban energy planning process (Fig. 1) and developed the Energy Integrated Planning Support System (EnerISS), which integrates urban energy planning technology to support each process (Fig. 2). EnerISS consists of Modeler, which models urban space and generates a GIS mesh DB;

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Current process

Proposed process

Energy use plan submission

Optimal energy plan deduction yes no Assessment Scenario review Selection of optimal site for low carbon urban energy system

Energy system/facilities plan

Environmentally friendly energy supply scenario suggestion

Scope of this study Urban energy demand forecast Urban energy demand forecast Land use plan

E-GIS DB construction Urban energy consumption unit DB

Urban energy consumption unit DB

Fig. 1. Suggested environmentally friendly urban energy planning processes.

Viewer Scope of this study

Urban space DB

Modeler

Solver

Evaluator

Mesh GIS DB

- Micro climate - Heating/cooling load - End-use (Electricity/Heat) - Primary energy

- Energy supply scenario - Optimal facility site

Evaluation DB (LCC, LCCO2, Stability of energy supply)

Integrated

Output Input

Validation E-GIS DB

Compare

Energy consumption unit DB

Fig. 2. Composition of EnerISS.

Solver, which predicts urban microclimate and energy demands; Evaluator, which supports energy supply planning and the estimation of energy supply scenarios and optimal sites for energy systems; and Viewer, which visualizes the results of each module. The technology discussed in this paper proposes that urban space modeling completed in the EnerISS Modeler should begin with constructing urban GIS mesh DBs for the compact prediction of the urban energy demand, which is finally deduced in EnerISS, and should be linked with the Environment and energy Geographic Information System (E-GIS) for feedback on urban energy planning from the urban planning stage. In this paper, the E-GIS construction model was verified for the following processes: constructing an urban GIS mesh DB in Modeler, predicting urban microclimate and heating/cooling loads in Solver and integrating the E-GIS DB.

Meanwhile, the building and urban planning sectors should collaborate with the environment and energy sectors when environmentally friendly planning is included in the project design. When planning agents, such as urban specialists, energy specialists, and policy makers, make decisions, the information from each of the sectors should support the planning processes of those in the other sectors. The planning process typically addresses a wide range of information from the interests of the participating planning agents, under conditions of uncertainty and feasibility. The agents must then narrow the extensive real data into objective and detailed planning information through a logical methodology. To simultaneously reduce energy use and avoid harm to the environment, the planning process must begin on some common ground. The environmental and energy information can be individually integrated into this common starting point. To better achieve

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(Decision Support System) Data-Driven Spatial Data/GIS

Knowledge -Driven

Model -Driven Hybrid

Communication -Driven

Document -Driven

Fig. 3. Composition of DSS.

Table 1 Status of research and development of SDSS/SPSS. Subject -

City (growth/transport/greenbelt/energy/landscape, etc.) Land (agriculture/forest/soil/waterway/floodgate) Animals and plants Human activity Disaster Waste/pollution

Purpose -

Methodology

Development/planning Resource/energy utilization Effect evaluation/management Protection/preservation

planning objectives, the realization of planning should more closely and accurately predict the actualities found during the processes that give shape to the plan. Then, meaningful feedback can be provided to improve the planning process. When planning for a sustainable and environmentally friendly city, it is important to establish integrated urban planning information to be utilized and managed throughout the life cycle of the city. It is necessary to integrate environmental and energy information that reflects changes in the life cycle of the city into the urban planning information and to provide this information as quantitative and visual information to each of the agents participating in the planning and decision-making processes. After taking these perspectives into consideration, this study aims to develop an E-GIS construction model, a general-purpose urban energy planning support system that can be utilized for urban planning. The E-GIS construction model will address the integration of urban planning information and urban microclimate and energy information for supporting environmentally friendly urban planning processes and deducing planning support material in the draft plan development stage. The agents requiring information for decision-making are, first, urban planners and energy planning engineers who are supplied with quantified planning information and microclimate and energy information in urban units, which enables them to give feedback in the draft plan development stage to support progress in urban design. The second agents to receive information are policy makers and citizens, and these individuals are supplied with visualized reports and publications to support their roles as urban planning agents. The research is presented in 3 stages. Section 2 examines the existing research trends regarding urban planning support sys-

-

Data manufacturing/processing Modeling technique Systemization Development of web device for participation/ monitoring of user

tems, defines the concept of E-GIS and proposes an E-GIS construction model. Section 3 describes the details of the E-GIS construction model, including the algorithms employed, and Section 4 describes the results of testing the E-GIS construction model on an actual city.

2. Materials and scope of the article 2.1. Definition of an urban energy planning support system Using this concept, a decision support system (DSS) has been suggested to support the planning process across several parameters, including nation/area, long term/short term, and public/private. DSS is a computer-based information system, which combines data, model, and a user interface. DSS can be defined as a technical decision-making tool that provides the necessary data to decision makers, enabling the extraction of systemized and detailed information from vague and indefinite information [1,2]. A DSS that handles spatial data, such as GIS, is referred to as a spatial decision support system (SDSS) and signifies a group of information systems consisting of spatial data/information, a model, and a visualization tool [3]. The range of DSS includes all 5 types, data-driven, model-driven, knowledge-driven, communication-driven, and documentdriven, as well as hybrid DSS, which is a combination of two or more types of DSS [4], as shown in Fig. 3. The urban energy planning support system proposed in this study has similar characteristics to those of the hybrid DSS because it is a system that processes quantitative and spatial data to apply

726

Table 2 Research trends of SDSS/SPSS dealing with environment and energy issues. Category

Author

Title

DSS methodology A

Environment/ecology Energy

Demand and supply plan

Oh KS.[2005]. Broek MVD. [2010]

Urban Carrying Capacity Assessment System (UCCAS) ArcGIS/MARKAL (MARKet ALlocation)-based toolbox

Ramachandra TV. [2009]

RIEP: Regional Integrated Energy Plan

Vettorato D.[2011]

Supply

Consumption

Ecology-energy combination

Quinonez-Varela G. [2007] Chen F. [2007], Krajacˇic´ G.[2009] Manfren M.[2011]

Tools for distributed generation projects

Muselli G. [1999]

Integration of renewable-energy systems

Finney KN. [2012] Omitaomu OA. [2012] Kumar A. [2012]

Identification of potential expansions using heat mapping (GIS modeling techniques) Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE) tool Optimal site and size of bioenergy facilities allocation model

Murphy JD. [2011] Ramirez-Rosado IJ. [2011] Jana C. [2004]

Regional potential for a grass biomethane Long-term forecasting of small power photovoltaic systems Multi-objective energy resource allocation programming (MOFLP)

Howard B. [2012]

Spatial distribution of urban building energy consumption by end use model

Shin DY. [2012]

Integrated energy monitoring and visualization system

Quattrocchi F. [2012] Burkhard B. [2012] Cai YP. [2009] Huang SL. [2011] a

Energy planning tool for island energy systems (H2RES)

Energy Density Potential in Land (EDPL) Mapping ecosystem service supply, demand and budgets UREM, University of Regina Energy Model-interactive decision support system (UREM-IDSS) Framework of ecosystem valuation

A: Data-driven, B: Model-driven, C: Knowledge-driven, D: User Interface/Web-base.

C

D p

p p

p

p

p

p

p

p

p

p

p

p

p

p

p

p p

p

p p

p p

p p p

p p

p

p

p

p

p p p

p

p

Time

Regional National/Regional

– Period

Regional

–

Regional/Local (Resolution:100 m) National/Regional

Annual

Regional

Hourly

Regional/Local (Community) Regional/Local

Hourly

Regional/Local

p

p p

Space

p

–

Representative time –

National/Regional Regional/Local (Resolution:300 m) National Regional/Local Regional/Local (Block level) Regional/Local

– –

Regional/Local

Hourly

Regional/Local Regional/Local Regional/Local (Community) Regional/Local

– Annual Annual/Seasonal/ Daily –

– – Hourly peak Annual

I.-A. Yeo et al. / Applied Energy 104 (2013) 723–739

Production and networking

Renewable Energy Sources (RES), Energy Demand of Buildings (EDB) and composite maps Integrated GIS/Power system simulation planning tool

B p p

Resolution of results a

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(a) Urban GIS integration model Combination of urban planning information

Building

Land Cover

(c) Visualization model

(b) E-GIS DB model

Mesh conversion

E-GIS DB Urban geography Urban energy Urban climate

Weather /energy DB

Altitude

Visualization and planning support Range

Existing/Planned city

Info.

E-GIS DB

Dim.

2D/3D

Exterior program: Urban Climate Simulation System Inter-model

Intra-model

Fig. 4. Concept map of the E-GIS construction model.

Bldg. plan information load

Data1: DTM.shp

Assigning bldg. address to DTM

Joining of DTM and bldg. management registry

Overlapping of bldg.- land registration formation of bldg. address

Data2: Land registration map.shp

Data3:Administrative boundary map.shp

Completion of Integrated bldg. data

Address matching? NO

Integration of bldg.- bldg. management registry YES Address matching of DTM and bldg. management registry

Manual input of bldg.address Bldg.- land registration layer ON

Data4: Bldg. Management registry.xls

Focusing of unmatched buildings Manual input according to land registration map

Land cover information load

Subtraction of bldg. polygon from land cover

Segregation of land cover layers

Completion of land cover data

Data5: Land cover map.shp

Topography information load

Point conversion of raster dataset

Completion of altitude data

Data6: DEM.img

Fig. 5. GIS integration model for an existing city.

knowledge-based information to model urban planning. There has been a tendency in existing studies to use the term ’spatial planning supper system (SPSS) ’ and ’SDSS’ without clearly differentiating the meaning; in this research, the meaning of SDSS/SPSS tend to be similar and will be used together while reflecting it in the subsequent literature review. 2.2. Literature review SDSS/SPSS-related research has been conducted extensively for the purposes of resource development and utilization, effect evaluation, and protection/preservation of the overall ecosystem. This includes activities such as urban planning, land use, ecological planning for plants and animals, human activity and disaster, and waste and pollution. Research methodologies [5] are also diverse, ranging from DB, on-line analytical processing (OLAP), data mining,

and development and utilization of web-based DSS, according to the characteristics of the DSS as clarified in Section 2.1 (Table 1). After greenhouse gas reduction became a global issue, there has been an increase in SDSS/SPSS-related research, reflecting the interest in reducing energy and preserving the ecology in urban construction. Especially in urban areas with a high level of human activity and subsequent carbon emissions, researchers link resource utilization, new renewable energy potential, emergy, etc., which are calculated from ecological and energy information, extending from the differentiation phenomenon of the ecology and energy field (Table 2). In the field of urban environment/ecology, Oh et al. [6] developed a GIS-based evaluation system that determines land use and development density based on the carrying capacity of the city, and Broek et al. [7] applied a GIS-based energy model (MARKAL) to analyze the effects of carbon collection and storage (CCS)

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Attributes of bldg. layer assigned by land registration address A -

B

…

Attributes of bldg. management registry

Code

…

a

Hy121

-

-

-

b

…

Integrated bldg. data

Code

…

A

Hy121

-

-

-

B

…

Code

a

Hy121

-

-

b

…

Address

Address Join

Fig. 6. Formation of integrated building data.

Urban planning data load Data1: District unit planning map.shp

Segregation of land cover layers

Bldg. facility layer

- Bldg. facility including residential etc.

Park facility layer

- Neighborhood - Children’s park - River park

Green space layer

-

Planned use of bldg. group Planned BCR of bldg. group Planned FAR of bldg. group

Data2: Specific-use area map.shp

Data3: Bldg. width statistics of the existing city DB.xls Data4: Classified bldg. group use DB.xls

Water layer

Buffer green area Connecting green area Pond Creek green area

- River/sea

Bldg. group

Non-bldg. group

Bldg. group

Non-bldg. group

Alt Green cover

Water cover

Bare ground layer

- Sqaure/ plaza - Public space - Military town

Bare

Road layer

- Planned road

Artificial cover

Bldg. group scale per facility site

Bldg. use per facility site

-

Number of bldg. BCR FAR Number of stories Bldg.width

-

Residential Commercial Office Other

Alt

Default

Completion of land cover data

Completion of Integrated Bldg. data Fig. 7. GIS integration model for a planned city.

facilities based on policy scenarios, while also providing detailed facility planning information. Related research in the field of energy includes (a) urban energy supply and demand planning, (b) production and networking, (c) supply, and (d) consumption. With respect to (a), Ramachandra [8] applied the DSS technique to an integrated energy plan (demand forecast, conversion, energy resource review, evaluation, and calculation) and Vettorato et al. [9] developed a new renewable energy resource map and building an energy demand map to analyze the spatial distribution of energy supply and demand. With respect to (b), Quinonez-Varela et al. [10] developed the PSS, which utilizes GIS and the Power System Simulation, for the location and volume selection to connect a new renewable energy development (REG) system with the existing electrical grid. Chen

et al. [11,12] developed H2RES, which is a tool that evaluates an energy supply scenario of isolated/island areas to predict energy demand and link new renewable energy (wind power, solar power, hydraulic power, biomass, and geothermal heat), fossil fuel, and the existing electrical grid together. Manfren et al. [13] developed a planning tool at the community level for a new distributed renewable energy generator system, combining a simulation model with GIS. Muselli et al. [14] suggested incorporated SPSS process for decentralized renewable energy systems’ management plan and optimization of physical and technical–economical configurations. Ramirez-Rosado et al. [15] used the GIS-based PV-energy– density map to provide quantitative and visual information, combined with the geographical distribution for extension plans

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facility site

bldg. group

(b) building group size of facility site unit Use

Bldg. stories

BCR

FAR

b a 2 (a) building size information of facility site unit

(c) building segregation

Fig. 8. Formation of building information from facility site in a planned city.

Altitude from the sea level

Topographic data load DEM

Land cover data load

Formation of land volume Separate formation of land cover layer

Altitude from the sea level

Formation of land surface Formation of integrated 3D GIS data

Land cover map Integrated 2D GIS layer Integrated Bldg. data load GIS of existing city GIS of planned city

- Bldg. width - Bldg. area - Number of bldg.

Altitude from the sea level

Formation of 3D bldg. volume

Data integration (single layer)

Fig. 9. 3D GIS integration model of a city.

of small capacity PV systems. With respect to (c) energy supply planning, Finney et al. [16] constructed a ‘thermal map’ that considers the use of urban facilities for thermal production and location planning by regional cooling and heating. Omitaomu

et al. [17] developed GIS-based multicriteria decision methodology for siting energy plants for effective use of cleaner energy sources. Sultana and Kumar [18] developed a location-allocation model of optimal site planning for bioenergy facilities, system specifications

I.-A. Yeo et al. / Applied Energy 104 (2013) 723–739

individual buildings, and planning information with a resolution of a couple hundred mesh units is also suggested. There is also a research requirement for total facility capacity modeling in the urban planning stage, and a need for temporal data to support energy production planning in the urban operating stage, to enable the integration of the environment and energy information to reflect changes in the urban life cycle. To include spatial variation, the planning data must also be linked to the environment and energy information at the spatial resolution of the mesh. Then, the model can provide feedback to the urban planning process. Based on the above perspectives, the differences between this study and the existing research are as follows. The environmental and energy PSS proposed in this study combines the environmental and energy information with planning information for environmentally friendly urban planning. This is significant in that it can (a) link spatial information at the building and city levels, (b) predict environmental and energy information at the completion of city construction, during the planning stage, while providing feedback to urban planners, and (c) provide a quantitative DB and the integration of visual planning information with environmental and energy information, which can be used by urban planners. 2.3. Concept of E-GIS The E-GIS proposed in this study gives feedback about environmental and energy factors of the post-development phase, such as predicted temperature and energy consumption, during the urban planning stage. When used in building design, usage, density and position are determined through the urban planning stage, and typical urban climate characteristics are modeled that differ from than those predicted during pre-development. Additionally, energy consumption during the operation of the city is predicted, and ultimately, an E-GIS DB is constructed. The urban planning information that can be collected from the E-GIS construction method is different depending on the progress of the urban development; thus, this study divides the urban development stage into concepts of an ‘existing city’ and a ‘planned city’. The divisions are based on the building and land cover plans. The title of an ‘existing city’ applies to a current city that has few or no changes in building or land cover after a new city is planned or

1) Altitude points

Bldg. use

Bldg. use Structure

3) Planned stories

Land use map 1) Bare ground 2) Green 3) Creek 4) Artificial

1) Wood 2) Block 3) RC 4) Steel

7) Gross BCR

1) Bldg.width

Bldg. size

6) Bldg. width

Bldg. size

2) Bldg. group area

5) RC

Bldg. code 1) Single family house 2) Duplex ... 36) Sewage disposable facility

Structure

Bldg. group use Structure

1) Number of bldg.

1) Residential 2) Office 3) Commercial 4) Other

’ Item calculation

8) Number of stories

2) Gross BCR

’ Mesh overlapping

’ 2D GIS formation

3) Number of stories

Land cover

Land cover

13) Mean altitude

Topo.

Land cover map 9) Water 10) Green 11) Bare 12) Artificial

Topo.

Use BCR

1) RC

Bldg. group size

BCR

Bldg. stories FAR Bldg. stories FAR

Mesh conversion algorithm of exsiting city GIS

Representative value

Urban facilities\ 1) Single family house 2) Duplex ... 26) Sewage disposable facility

Land cover group

Duplex

Use

Item calculation

Topo.

Land use plan/district unit planning map DEM

Single family house

Mesh overlapping

DTM/bldg. management registry

Mesh conversion algorithm of planned city GIS 2D GIS formation

1) Built-up 2) Agriculture 3) Forest 4) Grass 5) Wetland 6) Bare ground 7) Water

1) Altitude points

Fig. 10. Mesh conversion algorithm of urban GIS.

Land cover map

(optimal number and size) and minimum cost. Murphy et al. [19] developed ‘bioenergy potential map’ of Ireland that was deduced from national unit-grass biogas potential factors and assessed by multicriteria decision. With respect to (d) energy consumption, Jana and Chattopadhyay [20] developed an energy allocation model for the calculation of the total energy costs, energy self-sufficiency, energy efficiency, and total energy reduction. Howard et al. [21] collected heating, hot water, and cooling electricity consumption data at the ZIP code level, for a selection of building units, to formulate the allocation model for urban unit energy and GIS-based urban energy planning information. In the area of ecology and energy combination, Kim et al. [22] developed a model that can visualize web-based E-GIS information by utilizing monitoring data and energy sensors of buildings and the city; Quattrocchi et al. [23] proposed a strategic mixedenergy planning tool and method that constructs diffuse degassing structures by deducing Energy Density Potential in Land (EDPL) from renewable energy potential (solar, wind, and geo thermal). Burkhard et al. [24] deduced the knowledge-based energy planning data that visualize the integrated ecology service matrix and the supply and demand load. Cai et al. [25] developed an energy management system that analyzes and visualizes the interactive effect among energy and environmental policies, sustainable development methods for region/community, emission reduction measures, and climate change. Huang et al. [26] conducted a study modeling the natural energy potential and emergy from ecological information. From the above research examples, it is evident that the combination of two or more methodologies of data, modeling, knowledge-based systems, and user interface/web-based systems are common in SPSS/SDSS development. The existing SPSS/SDSS shows a tendency for differentiation between the urban planning stage and the post development stage. For urban planning, the results are reported in total capacity units without a time dependence. The time dependence is applied to the energy demand forecast and production planning information in the urban operation stage. The spatial domain where the existing SPSS/SDSS applies is between several square kilometers to hundreds of square kilometers. The spatial resolution primarily affects the urban areas or

DEM

730

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I.-A. Yeo et al. / Applied Energy 104 (2013) 723–739 Table 3 Reclassification of building use for the establishment of an E-GIS DB. Usage reclassification

Building use in existing city

Facility use in planned city

Residence

Single family house, duplex dwelling

Commercial

Neighborhood facility (type 0, 1, 2) commercial facility, sales and business facility Work facility, education research and welfare facility, cultural and meeting facility Factory, storage facility, medical facility, accommodation facilities, tourism and rest facility, recreational facility, public facility, cemetery, storage and treatment facility of dangerous material, automobile-related facility, animal- and plant-related facility, waste- and trash-treatment facility, sport facility

Single family house, block-type single family house, apartment (low density) Neighborhood facility, multipurpose buildings, central commerce, general commerce, self-sufficient site,a distribution exhibition sale Work complex, police station, fire station, public work, post office, community complex, education facility Sports ground (sewage disposable facility), electricity supply equipment, utility facility, gas station site, automobile-related facility, religious facility, pressure facility, railway facility, wastewater pump facility

Office Other

a The facilities that activate employment and urban economy, such as a university, research facility, public facility, convention facility, medical center, industrial facility, sales office, shopping facility, and culture or welfare facility.

Table 4 Method for calculating the mesh representative value for the building scale category. Building scale

Existing city

Planned city

BCR

R building area

R building group areað¼Rðfacility areaBCR for facilityÞÞ

Number of stories

Rðbuilding storiesbuilding areaÞ R building area

(first)

FAR

R floor areað¼Rðbuilding storiesbuilding areaÞÞ

R floor areað¼Rðfacility areaFAR for facilityÞÞ

mesharea

mesharea

mesharea

Building width

(second)

Rðbuilding storiesbuilding areaÞ R building area

mesharea

R number of building polygon

(first) Assign mean building ratio per site of existing city

Number of polygons within mesh

(first) According to usage region

R building area

Number of buildings

FAR a BCR

(second)

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

R building group area R number of building group

R building group area buliding width2

(second) sum of first number of buildings according to usage region a

In the case of multi-dwellings, planning information directly suggests story information.

Table 5 Reclassification of land cover for the establishment of an E-GIS DB. Land cover reclassification

Planned city

Existing city

Artificial coverage Green Water Bare ground

Road, non-building area in building facility site Green area group, non-building area in building facility site Water group, non-building area in building facility site Bare ground group, non-building area in building facility site

Built-up area Forest Water, wetland, farm Bare ground, grass

enters the post development stage, and a ‘planned city’ applies to cities that will be newly constructed. The climate information and spatial information, such as building, land cover, and digital elevation maps (DEM), which provide two-dimensional (2D) and 3D urban spatial planning information, are collected to construct an E-GIS DB. The building information that is utilized in the construction of an E-GIS DB for existing cities is the digital topographic map (DTM), which provides the density information of individual buildings, property information from the building management registration, which include usage information, and the land cover map, which provides the current state of the land cover. Planned cities do not have established, individual building plans, and thus, changes in the building and land cover plans are possible, and various alternatives can exist. The building information used in construction of an E-GIS DB for a planned city needs to include the use and scale information in the notion of the ‘building group’ so that the levels between the DTM and a specificuse area/district plan map are suitable. For the planned city land use information, it is appropriate to use land use maps, which provide planning information.

model combines urban GIS information and constructs an E-GIS DB using climate energy simulations. By visualizing the E-GIS DB in 2D and 3D urban space, the model can ultimately be utilized by urban or energy planners. The E-GIS construction model is composed of (a) an urban GIS integration model, (b) an E-GIS DB model, and (c) a visualization model, as shown in Fig. 4. The model combines the planning information from an existing city and a planned city in an urban GIS integration model, constructs an E-GIS DB, which includes effects from environment and energy information, and visualizes the E-GIS DB in 2D and 3D using a visualization model to provide data to planning agents. 3. Proposal for an E-GIS construction model 3.1. Urban GIS integration model This section describes the urban GIS integration model, displayed in Fig. 4a. This model creates a DB of building, land cover, and altitude information for an existing city and a planned city in detail.

2.4. Concept of E-GIS construction model The E-GIS construction model proposed in this study is presented in the form of a hybrid DSS/PSS, including GIS-based spatial data, numerical modeling, and use of a knowledge-based DB. The

3.1.1. GIS integration model of an existing city The GIS data of an existing city are composed of integrated building data, land cover data, and geographic data. The GIS integration model for an existing city is shown in Fig. 5.

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I.-A. Yeo et al. / Applied Energy 104 (2013) 723–739 Table 6 Simulation input conditions for each mesh. Category

Variable

Unit

Ratio of land cover area Water, green, bare ground, artificial coverage Building information BCR Building width Building height = (number of stories  4) Building use (residence, office, commercial, other) Cooling type (domestic air conditioner, air-cooling HP) Geography Altitude View factors BCR, building height, form of urban block calculated from building width (wall ? horizontal wall/perpendicular wall/ground surface ground surface ? 4 surfaces of wall)

(m2/m2) (m2/m2) (m) (m) – – (m) –

Table 7 Environment and energy categories.

Fig. 11. Concept of urban block and heat balance dealt with in UCSS.

3.1.1.1. Formation of building information. Currently, the DTM and building management registry are not consolidated for city buildings, increasing the difficulty in assembling the 3D information for the individual buildings. Therefore, in the GIS integration model for the existing cities, the integrated building data are compiled by linking the building layer of the DTM and the building management registry. To match the building information in the DTM in a 1:1 correspondence with the administrative property information in the building management registry, the lot number was set as the common parameter. The lot number was used because address information is not used in the current DTM. The DTM is overlaid on the land registration map layer, and the address in the land registration map is assigned to each building’s polygon. The user can edit properties manually by adding building addresses that are not automatically assigned. When the building layer of DTM with the assigned lot number is joined with the property information in the building management registry in .xml format, the address, building coverage ratio (BCR), floor area ratio (FAR), number of stories, and building area fields can be automatically produced for each building shape (Fig. 6). The attribute value for the scale of the building is assigned by calculating it, using the spatial data from the GIS. 3.1.1.2. Formation of land cover information. The land cover status of an existing city is produced by deleting the space where buildings exist in the land cover map2 and then separating and storing the layer according to land cover classification. 2 The land cover maps produced by the Ministry of Environment of Korea exist on 3 scales: large, medium, and small. However, given that the urban climate simulation categorizes the ground coverage characteristics into water, bare ground, green, and artificial covering, and the main categories of the land cover map are in 7 layers: builtup area, agriculture, forest, grassland, bare ground, wetland, and water.

Category

Output element (hourly)

Unit

Urban climate

Temperature Wind direction Wind speed

(°C) (°) (m/s)

Urban energy

Heating and cooling load End-use of electricity and heat Primary energy

(W/m2) (kW-electricity, Gcal-heat) (kW-electricity, N m3LNG, kg-LPG, ‘-Oil)

3.1.1.3. Formation of altitude information. DEM is a data that displays the geography as altitude in pixel form. DEM is converted from the original data into a shape file, which is then stored as altitude point data. 3.1.2. GIS integration model of a planned city The GIS integration model of a planned city is composed of the same categories as the existing city; however, because building and land use plans do not already exist, there are differences in the methodology developing building information and land use information. Development of a GIS integration model for a planned city is shown in Fig. 7. 3.1.2.1. Formation of building information. The building information for planned cities is developed from the urban management planning maps, which provide facility density plans. The building and parking facility sites, specifying the BCR, FAR, number of stories, and usage plans, are placed into the building layer group and the land cover group, and the remaining facility layers are placed into the land cover group. When developing a building group, buildings within facility sites are grouped together by linking the statistical values3 for the buildings’ widths, for the case of the existing city (Fig. 8). The intervals between building placements are calculated as follows:

a2 2

b

¼ BCR ¼

Building area Building facility site area

b  a ¼ a pitch between buildings

ð1Þ ð2Þ

3 The results of the analysis of the characteristics of the buildings’ widths for the cities that exist in 14 usage districts, according to the urban planning legislation of our country, show that the mean building width is in the range of 10–35 m, according to the district’s usage.

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(m, n)

(1, 1)

Energy14h[W/m2] 120 100 80 60 40

(m, n)

Temp.14h[ ]

20

32.0 31.0 30.0 29.0 28.0 27.0 26.0 25.0 24.0 23.0

0

1

11

Energy14h[W/m2]

21

31

meshy41

120 100 80

(1, 1)

60

Y-axis

32.0 31.0 30.0 29.0 28.0 27.0 26.0 25.0 24.0 23.0

0

1

11

20.0 19.5

meshy41

21

31

mesh y21

X-axis

Energy demand characteristics

11 21 Humidity 14h[%]

Temp. 14h[ ]

20

21.0

(m, n)

20.5

1

40

Humidity14h[%] 21.5

Temp humidity

19.0 31

21.5

Temp humidity

(1, 1)

21.0 20.5 20.0 19.5 19.0

1

11

21

31

mesh y21

Temperature and Humidity characteristics Fig. 12. Mesh section information from urban 3D GIS and environment and energy planning.

Building of existing city Gyunggi-do, Republic of Korea

Central commerce district

8km

Low density apartment district

12km

Low density apartment district Public housing district of Gwangmyung· Si-heung

(a) Geographical location

Central commerce district

(b) Case study area

(c) Subject area development plan

Fig. 13. Outline of the urban development plan for the research area of interest.

Item

Existing city 2D

3D

Building

Fig. 14. Building GIS formation results for an existing city.

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Item

Existing city 2D

3D

Land Cover Water Grass Wetland Bare Forest Agriculture Road Built-up

Fig. 15. Land cover GIS formation results for an existing city.

Fig. 16. Building GIS formation results for a planned city.

Item

Planned city 2D

3D

Land Cover

Fig. 17. Land cover GIS formation results for a planned city.

where a is the building width (building width statistics of specificuse area) and b is the distance between the building centers. The building’s use is applied evenly to the building group in the same facility site. The building’s group structure is difficult to obtain from the urban planning information, and thus, RC, which is the most general domestic building structure in Korea, is applied as a default value. 3.1.2.2. Formation of land cover information. The land cover of planned cities is composed of the spaces around and between the buildings in the building areas and the other non-building facility areas, such as green and water areas. The space excluding the building area within the building facility site is generally artificially covered after development; therefore, artificial covering is treated as a default value. The ability to choose ecologic covering as an alternative is enabled for environmentally friendly urban planning. The park and green belt are treated as a green area group; streams, plazas, public areas, and military towns are in the bare group; and the road is regarded as an artificial covering.4 4 Green area group: buffer green area, connecting green area, pond, creek green area, neighborhood parks excluding building space, children’s park, river park, and parts of building facility sites. Water group: creek, river, and sea. Bare ground group: plaza, public space, military town, and parts of building facility sites.

3.1.2.3. Formation of altitude information. Altitude change occurs in planned cities before and after urban planning, but this change is within several meters and cannot be observed as an element with much influence on the state of the climate and energy change for urban planning purposes. Therefore, the same method that is used for the existing cities is applied. 3.1.3. 3D GIS integration model The existing city constructed in subsection of 3.1.1 and the planned city in subsection of 3.1.2 is loaded into layers in the model with similar property fields. Then, they are combined into one layer according to the 3D GIS model, shown in Fig. 9. The 3D GIS model is constructed as follows: (a) The ground volume is formed by entering geographic information from DEM; (b) The surface color/texture is assigned through the land cover layer; and (c) The building volume is built from the ground up by retrieving the integrated building data. Using the 3D GIS model for a city, the spatial plan can be examined using alternative scenarios, varying building density and story combinations within the tolerance limits of the planning process. The results can be used to provide feedback or modifications to a new plan. To improve urban planning, research is also needed to compare and review quantitative environmental and energy

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Existing city

Planned city

Fig. 18. 3D GIS formation results for the research area of interest.

information with planning information. Methods to establish an EGIS DB and utilize it as a PSS are proposed in Section 4. 3.2. E-GIS DB model This section proposes the E-GIS DB model shown in Fig. 4b. The model sets the urban block resolution to distribute urban GIS data into a mesh, adds urban climate and energy demands, and then deduces the E-GIS DB. 3.2.1. Conversion of urban GIS into a mesh To improve urban planning by applying environment and energy information, the feedback must be available on spatial units that connect the building level and the facility site level. In this study, a 200 m  200 m mesh was set, to reflect the resolution of the urban block, to evenly manage the entire urban area, and simultaneously control each unit area. Fig. 10 is the algorithm that distributes urban GIS information into a mesh. To construct a mesh DB, the model (a) creates 2D GIS data output, (b) creates a mesh and overlaps it with the 2D GIS output, (c) calculates building information (usage, size, structure/material), land cover, and geographic category, and (d) calculates the mesh value that represents the categorical information. When forming an urban GIS output data value, building use is assigned to individual buildings according to the building law in the existing cities, and it is assigned to the units of building groups according to the urban planning facility classification in the planned cities. However, in Section 4, building use is reclassified into 4 use categories for the Urban Climate Simulation System (UCSS) calculations, according to heat load characteristics and characteristics of the cooling system (Table 3). The mesh value rep-

resenting building use assigns the maximum value of the building use ratio as the primary value. When the primary values, according to use, are overlapped, the representative value is selected in the order of commercial, office, residential, and other, after considering the energy consumption characteristics. The building’s size is calculated within the mesh as a gross value. The method for calculating building size in the existing city and the planned city is identical; however, in a planned city, 2 stages of building size calculation is performed before and after the building group is separated from the facility site. The number of stories is obtained by assigning the planned number of stories within a facility site, and then, the mean number of stories of the building group within the mesh is calculated. The building width is calculated by separating the building polygon from the facility site, using the width statistics for the existing city, finding an average building area by dividing the sum of the buildings areas by the polygon number, then taking the square root of the average building area. The number of buildings is obtained by separating the building polygons according to the building width then summing polygons within the mesh. The mesh representative value equation presented above is shown in Table 4. The land cover established in the urban GIS integration model was reclassified into artificial coverage, water, green area, and bare ground classifications as representative values in the mesh (Table 5), according to the evaporation/transpiration characteristics of the land surface, based on the classification of UCSS input condition. Altitude was calculated from the mean value of altitude points within the mesh. The mesh conversion of the urban GIS data was completed with the above-mentioned procedure. These data describe the planning elements of mesh units, both space

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Area ratio for mesh

Area ratio for mesh 0.0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1.0

0.0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1.0 0.

(a) Built-up

( b ) A g ri cu l tur e

Area ratio for mesh

( i) B ld g. c o ver ag e r ati o

( j) Bldg. width

FAR(%)

1-4 5-8 9-12 13-16 17-20

0-100 100-200 200-300 300-400 400-500

( l ) Fl oo r are a r a t i o

( k ) Bl dg . s t o r ie s

Number of bldg.

0-30 30-60 60-90 90-120 120-150

(n) Commercial bldg.

0-5 5-40 40-110 110-180 180-250

Number of bldg.stories

Number of bldg.

0-60 60-120 120-180 180-240 240-300

Altitude[m]

(h) Topography

(g) Water

0-30 30-60 60-90 90-120 120-150

Number of bldg.

(d) G rass

0.0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0 0.8-1.0

Width(m)

0-20 20-40 40-60 60-80 80-100

0.0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1.0

Area ratio for mesh

0.0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1.0

BCR(%)

(m) Residential bldg.

(c) Forest

(f) Bare ground

Area ratio for mesh

0.0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1.0

Area ratio for mesh

0.0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1.0

(e) Wetland

Area ratio for mesh

Number of bldg.

0-40 40-80 80-120 120-160 160-200

(o) Office bldg.

0-40 40-80 80-120 120-160 160-200

(p) Other bldg.

Fig. 19. Formation of the environmental planning information map of the research area of interest.

distributions and sizes so that they may be used for reference by planning agents. 3.2.2. Formation of E-GIS mesh DB The urban GIS mesh DB constructed in Section 3.2.1 is transferred into UCSS as the input dataset. The E-GIS DB is finally constructed by integrating the urban microclimate and heating and cooling load DBs into a mesh unit deduced from UCSS calculations, with the urban GIS mesh joined by the mesh ID. Urban geometry, which is shaped by urban geography and building compositions, forms micro-scale local climates in urban areas, so urban climates are characterized differently than are rural areas. In an urban block, buildings and street geometries, such as

the building composition from gross value of BAR, building height, and width, complicate the heat balance mechanism linked with wind corridors and solar position. From this perspective, UCSS is used in this study to analyze urban planning information that links urban climate and energy information into a climate model that simulates the urban microclimate for an environmentally friendly and low-energy city establishment, while predicting urban energy consumption by applying building conditions, such as insulation, structure, and cooling system type, as input conditions [27]. The UCSS conducts coupled calculations between a 3D atmospheric turbulence model and an urban canopy model. In the urban canopy model, the surface temperature and heat generation through mutual radiation from the 4 walls of the buildings, ground, and sky

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Fig. 20. Formation of the environment and energy planning information map for the research area of interest.

are calculated using sunny area ratio and view factors. The cooling, heat generation, and energy consumption are determined by calculating the indoor heat load due to the heat transmission from the building exterior given the indoor conditions. The amount of sensible heat and latent heat generation calculated in the urban canopy model is applied to the coupled calculations using the atmospheric turbulence model from the urban climate simulation [28–30]. The concept of heat balance in an urban canopy model for calculation in UCSS is shown in Fig. 11. In an urban block, the sunny area ratio and view factors between buildings and the ground are calculated assuming that the 4 walls of each building are parallel to the facing walls of adjacent buildings and the buildings are equally arranged in all directions. The heat balance in an urban block is processed by calculating the 3-dimensional sunny area of the buildings and ground from their relation to the hourly solar position, urban block geometry, mutual radiation of long and short waves from the sunny area ratios and view factors, evaporation from land cover, and artificial heat exhaustion from buildings and HVAC systems. Calculating the heat balance between urban blocks allows one to deduce urban temperature, humidity, wind velocity, heat exhaustion (including convective sensible heat, evaporative latent heat, artificial sensible heat, and artificial latent heat), and heating and cooling loads. Table 6 shows the input conditions for each mesh that is used to calculate the cooling energy from the indoor heat load. Table 7 shows the hourly urban climate and urban energy category from the E-GIS DB, with the UCSS feedback, and the calculation results added to the urban planning information. The final E-GIS DB has urban planning information and environmental and energy information included in its properties, and thus, it is possible to review urban climate and energy consumption together with the urban planning elements. Fig. 12 shows the climate element and the quantitative energy distribution characteristics of the 3D GIS for the city by section and by mesh unit as a line plot. Using this plot, planning agents can compare and review spatial distributions of environmental and energy characteristics with respect to the urban planning elements.

4. Applicability of the E-GIS construction model 4.1. Outline of the research subject area The study selected the Gwang-myung/Si-heung public housing district as the subject area from the domestic cities of Korea with urban planning currently in progress. The area of the city of Gwang-myung and Si-heung is 17.4 km2, and it is a low-density, middle-stratum, planned residence district. It includes 64,620 public residences and 30,717 private residences that can accommodate a population of 276,000. The research area is a rectangular region 8 km wide and 12 km long, and includes a development project district (Fig. 13). The current urban planning status, as of December, 2010, is the stage where the densities for each building facility and the location and scale of the natural land cover, such as the green area and water area, have been sketched.

4.2. Obtaining integrated GIS data of the cities According to the urban GIS integration model proposed in Section 3, 2D/3D urban GIS data for the research area were produced. Fig. 14 shows the 2D integrated building data that were created from the building information on the existing cities in the surrounding area and the results from the 3D building and altitude values. Fig. 15 shows the results from the land cover layer, excluding the building polygon areas from the 2D land cover map of the existing city, and the results of assigning altitude values to the ground surface in 3D. Fig. 16 provides the building group development results for the planned city in 2D and the data obtained by retrieving the height values from the number of stories in 3D. Fig. 17 provides land use information, excluding the building group areas for the planned city in 2D, and the result of assigning altitude values in 3D. Fig. 18 shows the total 3D GIS development results for the research subject area. With the 3D GIS development of the city, the

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geography, and the land cover status, the spatial distribution of buildings was intuitively visualized. 4.3. Forming an E-GIS DB In Section 4.3, an E-GIS DB sample was analyzed with urban planning information that links air temperature and cooling load on the hottest summer day with climate and energy contents. The E-GIS DB was created for the research area of interest, and it was visualized in 2D to analyze the urban planning status and environmental and energy information. Fig. 19a–h provide information on the land cover and altitude of the existing city, and Fig. 19i–p are the results with the inclusion of building planning information from the planned city. The results from the analysis showed that the subject area has 62.4% green cover that is mostly undeveloped land. It appeared that development is primarily occurring in the lowland at an elevation of 100 m or in an environment with an altitude of less than 200 m. This area has mostly buildings with a BCR of 20% or lower; however, because there are plans for apartment buildings of 15 stories or higher, a FAR between 300% and 500% was applied. Residence area development, or the development of the project district with office buildings, appears to rise slightly when compared to existing cities. Building widths are frequently 30 m or lower, but the buildings with widths of 30 m or greater appear in the central commercial districts, where the BCR is 20% higher than in the surrounding area. This area is being developed as a residential district, and therefore, residential buildings are dominant, while commercial use buildings are the next most common. Fig. 20 shows the UCSS results for the hottest day in summer, which would generate the peak summer cooling load. This figure shows the hourly urban temperature and cooling load. Urban temperatures differ significantly from the project development district. A maximum temperature difference of 3–4 °C occurs between in high temperature area and in low temperature area of the project development district. This difference was more prominent at night, between 20:00 h and 02:00 h. At 14:00 h, the overall highest temperature occurs in the urban area. At 14:00 h, the high temperature distribution characteristics vary according to the building distribution area, and at 02:00 h, the low temperature phenomenon, which centers on the natural land cover area of the existing city, is noticeable. The areas where buildings are developed, within the research subject area, generated cooling loads occur primarily in non-residential areas, such as office or commercial districts, during the day, and in residential areas during the night. During the day, the residential–commercial complex, commercial facilities, and selfsufficient urban facilities generate maximum cooling loads greater than 180 W/m2 h, but during the night, cooling loads were generated from an apartment block with a high FAR.

can ultimately connect and visualize urban planning as well as the environmental and energy DB in a 3D urban space. (3) The E-GIS DB model distributes the urban GIS data into a 200 m  200 m mesh. It performs UCSS calculations to complete the E-GIS DB through category feedback on hourly urban temperature, wind, humidity, and energy values. The E-GIS DB model includes a function to ultimately visualize the 2D and 3D information, which can be utilized in the environment and energy planning of the city. (4) A domestic city in the urban planning process of Korea was selected as a case study subject to validate the applicability of the E-GIS construction model that was suggested in this study. An E-GIS DB of 8 km  12 km from the research subject area was realized in 2D and 3D GIS, and the urban space, climate elements, and energy distribution characteristics were compared to test the function of the PSS that is utilized by planning agents during the environmentally friendly urban planning process. In this study, the future improvements of the E-GIS construction model can be summarized in 2 perspectives. First is the LOD problem of urban microclimate and energy calculations. The E-GIS construction model generates the urban microclimate and heating and cooling loads in mesh units. The calculation of microclimates and energy on a large scale in urban areas, which has practical limitations in the urban planning stage, is solved by setting the urban block unit resolution without considering building resolution in an urban block. This problem should be supplemented with a more precise CFD analysis. Second is validation of the E-GIS construction model reliability. In fact, validation by direct comparison, predicting the E-GIS DB with measured values, is impossible in the urban planning stage because measured climate data and energy consumption data are not acquirable until after the building and land cover plans are realized in the urban operation stage. This problem can be solved in an alternative way by modeling the E-GIS DBs of existing cities and validating the models with real data. Thus, existing city modeling methods were also proposed in this study. In further studies, the quantitative accuracy of this model should be improved by validating errors by comparing the predicted E-GIS DBs of several existing cities with real data. Acknowledgments This work was supported by a National Research Foundation of Korea [NRF] grant funded by the Korean government [MEST] [No. 2011-0028295]. References

5. Conclusion This study proposes a method to create an urban planning support model, applying E-GIS DB through the urban life cycle to reduce energy use for environmentally friendly urban planning. The primary results from this study are as follows: (1) The E-GIS construction model that is proposed in this study is composed of (a) an urban GIS integration model, (b) an EGIS DB model, and (c) a visualization model that can quantitatively and visually provide urban planning and the environment and energy information. (2) The urban GIS integration model can establish an integrated GIS with identical field categories for building, land cover, and altitude information for existing and planned cities. The urban GIS integration model includes a function that

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