Delineation of Central Business Districts in mega city regions using remotely sensed data

Remote Sensing of Environment 136 (2013) 386–401

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Delineation of Central Business Districts in mega city regions using remotely sensed data H. Taubenböck a,⁎, M. Klotz a, M. Wurm a, J. Schmieder b, B. Wagner b, M. Wooster c, T. Esch a, S. Dech a a b c

German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, 82234 Wessling, Germany Münchener Rückversicherungs AG (Munich Re), 80802 Munich, Germany Department of Geography, King's College London (KCL), WC2R 2LS London, United Kingdom

a r t i c l e

i n f o

Article history: Received 3 January 2013 Received in revised form 14 May 2013 Accepted 19 May 2013 Available online 20 June 2013 Keywords: Central Business District Digital surface model Cartosat-1 Landsat 3-D city model Megacity Urban structure type Spatial pattern Spatial metrics Classification

a b s t r a c t Central Business Districts (CBDs) are an important urban structural type (UST), and an apparent structural feature of many large cities. CBD locations play a decisive role in the spatial arrangement of functions and exposures within cities. However, while past research underscores the importance of their spatial detection, delineation and cartographic representation, the definitions used are mostly functional and qualitative. Objective pre-defined methods/thresholds for the semi-automated spatial classification of CBDs, based on a quantitative approach, do not yet exist. This paper presents a conceptual framework to define the CBD using physical and morphological parameters, and tests the approach using 3-D city models of three European test sites (Canary Wharf in London, La Defense in Paris, and Levent in Istanbul). From these case studies, we develop a transferable method to detect and delineate CBDs over larger areas from a combination of Cartosat-1 digital surface models and multispectral Landsat ETM+ imagery. Applying this wide-area method to the entire extents of the three European megacities of London, Paris and Istanbul, we detect CBDs with a user accuracy of 75.7% and spatially delineate them with overall accuracies of 82.9%. Finally, we apply spatial metrics to analyze and compare the location and distribution of CBDs across the three mega cities, finding many similarities between London and Paris, but showing that Istanbul features a more complex urban footprint, and a different spatial CBD pattern. © 2013 Elsevier Inc. All rights reserved.

1. Introduction One of the most visible features of global cities, attesting to their supremacy in national economies, is the Central Business District (CBD) (Sassen, 2001). In general, CBDs feature a concentration of high-rise buildings, allowing for the hypothesis that CBDs show significant differences in their physical and morphological characteristics compared to other urban structure types (e. g. industrial or suburban) (e.g. Haggett, 2001; Heineberg, 2001; Waugh, 2000). However, definitions of this mental construct are qualitative and no universal definition or theory on delineating CBD locations and spatial extents in complex urban environments exists (Borruso & Porceddu, 2009; Murphey, 1971). To date, approaches to delineate CBDs are e.g. based on perception of people or land use and respective functional parameters. However, since the urban structure type CBD features an enormous concentration of values, employment, infrastructure and money flows, knowledge on their location, extent and patterns is of great interest for many different stakeholders such as urban planners, global reinsurers,

⁎ Corresponding author. Tel.: +49 8153 28 2480. E-mail address: [email protected] (H. Taubenböck). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.05.019

real estate agents, economic strategists and scientists. Hence, business districts remain an important classification when analyzing the distribution of functional spaces within cities. Historically, CBDs were located in downtown areas. However, recent developments show the decline of existing CBDs in favor of new business districts in peripheral locations of cities (Borruso & Porceddu, 2009). Although it seems to be obvious where these areas are located, for a consistent identification and delineation of business districts in cities across the globe, data sources are often too generalized, outdated or simply not available. Remote Sensing has now developed to a stage where area-wide spatial data are available with the necessary geometric resolution to identify characteristic urban structural types (USTs) (e.g. Angel et al., 2005; Banzhaf & Höfer, 2008; Bochow, 2010; Herold et al., 2002, 2003, 2005; Kuffer & Barros, 2011; Sliuzas et al., 2008; Taubenböck et al., 2009; Wurm & Taubenböck, 2010). However, these studies focus on mapping structural types with physical semantics such as perimeter block development or row houses or with thematic semantics such as slums, high-class residential areas or commercial areas implying physical characteristics. By contrast, in the remote sensing literature only few studies focused on CBDs. Wu and Murray (2003) find physical characteristics

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typical for CBDs, such as high percentages of impervious surfaces and low vegetation fractions compared to other structural types. This also suggests that CBDs feature high spectral complexity, problems of building displacement and shadows (Dare, 2005), giving rise to a more difficult estimation and mapping of impervious surfaces (Hu & Weng, 2011). Beyond this, studies on urban heat islands indirectly refer to CBDs: e.g. Lo (2007) identified employment centers as areas of high surface temperatures, while Pan et al. (2008) referred directly to the physical nature of CBDs; however, these studies remain on the stage of case studies. These studies have in common that the area of the CBDs was not delineated from the remotely sensed data, but rather a pre-defined spatial area assumed to be the CBD was related to the spectral, thermal and/or physical characteristics found in the imagery. By contrast, in the current study, we aim to use remotely sensed data and higherlevel geoinformation products to determine sets of physical characteristics for the delineation of CBDs from the surrounding urban fabric. In this context, we address several specific questions: (1) What morphological features characterize CBDs? From a literature survey we derive physical and morphological parameters suggested to be typical for CBDs. From this, we build a hypothesis stating that there are significant physical and morphological differences for CBDs compared to surrounding urban structures. We use 3-D city models from CBDs in three megacities (London, Paris and Istanbul) to develop a statistical delineation of CBDs from Non-CBDs based on cluster analysis, and empirically derive thresholds to test our hypothesis. (2) How can we detect and delineate CBDs for entire mega city regions based on analysis of physical and morphological characteristics? We use large area Earth observation (EO) data — digital surface models (DSMs) from Cartosat-1, in combination with urban footprint classifications based on Landsat data — for classification of CBDs in entire mega city regions. We further develop an object-based approach and evaluate the results with regard to detection accuracy as well as spatial delineation accuracy. (3) Are there differences and analogies in the spatial configuration of CBDs across European mega cities? We develop and apply a set of spatial metrics to detect similarities and differences of locations, dimensions and patterns of CBDs within and across the three megacities of London, Paris and Istanbul.

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Fig. 1 schematically illustrates the workflow, the interim steps taken, and the input data used for the above analysis. 2. The Central Business District — from a conceptual to a morphological approach The Central Business District (CBD) as a concept of Urban Geography has been examined in various contexts. Originating in industrial America, the term CBD was used to describe the downtown of American cities in the 19th century (Pitzl, 2004), but was diffused to the rest of the western world in the following decades. The original concept of CBDs is rather descriptive and approaches to define this mental construct are qualitative such as the CBD is “the nucleus […] of an urban area that contains the main concentration of commercial land use” (McColl, 2005) or a “unique area of massive concentration of activities and focus for the polarization of capital, economic and financial activities in cities” (Drozdz & Appert, 2010). Several authors describe CBDs as areas marked by various qualitative indicators relative to the surrounding urban environment (e.g. Haggett, 2001; Heineberg, 2001; Murphey & Vance, 1954a; Waugh, 2000), making it a relative concept. First studies on the delineation and cartographic representation of CBDs were based on observation and perception (Murphey, 1971). By contrast, Murphey and Vance (1954b) aimed to quantify activities being central to the urban environment using indices of central business height (CBHI) and intensity (CBII). Defining a set of typical central business land uses, they calculated the two metrics on block level (Eqs. 1 and 2) and spatially delimited CBDs using distinct thresholds. In a comparative study (Murphey & Vance, 1954b) they applied this Central-Business-Index-Technique to nine American CBDs. CBHI ¼

total floor area in central business use total groundfloor area

CBII ð% Þ ¼

total floor area in central business use  100: total floor area

ð1Þ

ð2Þ

In the following years, various indicators of urban centrality have been used for CBD delimitation in case studies: Carol (1960) emphasized the significant difference between day and night-time population and Ning (1984) as well as Erteking (2008) analyzed spatial pattern and hierarchical structure of shopping malls. Furthermore, Guillain (2006) and Marguilos (2007) used spatial distributions of employment and real estate to measure centrality. Thurstain-Goodwin and Unwin (2000)

Fig. 1. Workflow for physical and morphological Central Business District (CBD) delineation, area-wide classification and cross city CBD comparison.

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calculated centrality based on a continuous density transformation of spatial variables such as building density and residential population from geocoded unit postcode data. Although a certain amount of research towards CBD delineation has been carried out, no universal method exists. All of the aforementioned methods rely on geocoded socio-economic variables from surveys and are therefore often limited to small geographic regions, if available at all. In this context, we suggest that remote sensing should be reconsidered as a tool for large-scale CBD detection and localization. The characteristics of remote sensing data limit the approach to a physical perspective on USTs such as (central) business districts. However, the EO-data enable to approach the topic with consistent and large area data. But, physical definitions in literature are rather vague. Nevertheless, the aforementioned approaches and definitions provide suggestions for characteristic physical and morphological indicators typical for CBDs within qualitative statements: The CBD is described as the part of the city which features peak land values and employment densities and thus, the highest buildings (McColl, 2005; Pacione, 2005). In this context, the maximum building height presents a distinct indicator for the CBD and can be combined with the maximum building volume, to which it is often highly correlated. Further qualitative statements imply that the CBD is an aerial unit formed by a group of buildings, not by an individual object (Murphey & Vance, 1954a). Thus, average values of the aforementioned building height and volume measures present a logical addition to the parameter set. Another parameter commonly used for urban structuring is building density (e.g. Taubenböck & Kraff, 2013; Wurm & Taubenböck, 2010). Although CBDs are generally not recognized to be more densely builtup than other parts of the city, they do feature a unique density of high-rise buildings, known as the skyline (Ford, 1976). Finally, high floor space densities have been found to be a typical physical feature of CBDs (Pan et al., 2008). Thus, we state the hypothesis that the physical and morphological parameters maximum building height, maximum building volume, average building height, average building volume, density of high-rise buildings and floor space densities allow us to delineate CBDs from the surrounding urban fabric. 3. Experiment: statistical physical delineation of CBDs from Non-CBDs 3.1. Selected CBDs — Canary Wharf (London), La Defense (Paris) and Levent (Istanbul) For verification of our hypotheses we selected three areas in three different cities: The first requirement was that each of the selected cities needed to feature a clearly designated CBD based on the literature. The second requirement was that in each of the selected cities more than one CBD exists in order to later test the area-wide classification of CBDs. According to McColl (2005), most large cities exhibit CBDs, especially global cities where international financial business centers can be found. Therefore, we opted to use CBDs in three European mega cities: Canary Wharf, London; La Defense, Paris and Levent, Istanbul. Representing its international economic competiveness and significance as a global center for the FIRE (finance/insurance/real estate) sector (Frug & Barron, 2008), the CBD at Canary Wharf, 8 km east of London's historic center, Trafalgar Square, was selected as a test site. It — until recently — featured the UK's three highest buildings (Shin, 2008). La Defense, located about 8 km west of the historic city center of Paris (Hall & Pain, 2006), was developed to protect central Paris from modern office development and embrace the demands of international business in 2000 (Trip, 2007). Thus, La Defense today presents an internal edge city and is — along with Canary Wharf — one of the most concentrated areas of high-rise buildings in Europe (Shin, 2008). Istanbul's main business activities developed during

the 1980s along the Levent–Maslak axis (Yigitcanlar et al., 2008), 8 km north-east of Istanbul's historic center. The Levent CBD was chosen as a test site as it exhibits the headquarters of Turkey's major banks with a high vertical dimension of buildings (Seger, 2012). Figs. 2 and 10 provide visual impressions of these three test sites. With respect to the overall goal of this study — the city-wide classification of CBD areas — the selection of the three mega cities London, Paris and Istanbul allow systematic testing of the methods in environments of varying structural urban types (long-established, planned vs. highly dynamic organic sprawling) and different orographic situations (flat vs. hilly).

3.2. 3-D city models While CBDs may basically have characteristic physical and morphological features, they are not consistent or uniform within or across cities. For a systematic and at the same time feasible approach to find characteristic features with respect to the variance in reality, we produce three geometrically highly resolved 3-D city models — one per test site. For model generation, the spatial extents of the 3-D models were chosen to cover not only the CBDs but also the surrounding urban fabric. For the test sites in London and Paris, rich building inventories from OpenStreetMap (OSM) (OSM, 2012) exist. However, these are not complete or fully consistent. Therefore, the associated building footprints were randomly checked against up-to-date Google Earth© imagery resulting in the elimination of incorrect footprints and digitalization of missing buildings. In the case of Istanbul's test site Levent, building footprints were extracted from high resolution optical Ikonos satellite imagery using manual digitizing and the cognitive perception of the interpreter. To supplement the described building footprints, systematic height estimation was conducted for buildings where height information was otherwise unavailable. Herein, Google StreetView©, which provides pictures of building facades on street level was employed. The indirect derivation of the building height was based on counting numbers of floors observable in the Google StreetView© imagery. In case of total occlusion of the buildings of interest, building footprints were attributed with the rounded mean floor count of the particular block (for the definition of the reference block units refer to Section 3.3) in which they are located. In the following, floor counts were converted to values of absolute building height using the empirical correlation found by Wurm et al. (2011) for housing inventories of two European cities. Fig. 2 visualizes the 3-D city models for the three study areas. The La Defense (Paris) CBD shows the highest number of high rise buildings, while Canary Wharf's (London) skyline appears to be the most spatially clustered. Levent (Istanbul) features by far the lowest density of high rise buildings. Based on subjective perception used by former studies (e. g. Murphey, 1971), all three CBDs show physically significant visual differences from the surrounding urban structures. However, although perception suggests the location of the CBDs, a distinct spatial delineation of CBDs to surrounding urban fabric is not obvious. In addition to the visual impression of Fig. 2 and the statement, that CBDs are not consistent or uniform within or across cities, a bird's eye view on the reference block units superimposed on the building stock including histograms for the three test sites with respect to building height are displayed in Fig. 3. The maps and histograms reflect quantitatively the high number of high-rise buildings in La Defense with a high variance regarding building heights within and between building blocks, while Canary Wharf appears to have a lower number of highrise buildings with a spatially clustered distribution of high-rise buildings and thus, a more significant difference to other USTs of the test site. Levent (Istanbul) features the lowest number and spatial concentration of high-rise buildings.

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Fig. 2. 3-D city models for Central Business Districts and their surrounding urban fabric for La Defense (Paris), Canary Wharf (London) and Levent (Istanbul), derived from a combination of OpenStreetMap (OSM, 2012), Google Earth, Google StreetView© and Ikonos satellite data.

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Fig. 3. Reference block units overlaid with building outlines and respective building heights as well as the particular histograms of building heights (in logarithmic scale) for Central Business Districts and their surrounding urban fabric (La Defense (Paris), Canary Wharf (London) and Levent (Istanbul)).

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3.3. Statistical designation and spatial delineation of CBDs vs. Non-CBS The CBD is defined not only by physical characteristics of one individual building, but by the physical characteristics and spatial alignment of a number of buildings. Thus, delineation needs an appropriate reference unit to identify the large-scale changes and heterogeneities in urban morphology. While the spatial unit of an entire district is artificial and thus incapable to spatially capture the varying urban morphology in reality, the individual building level fails to capture information on spatial alignment and pattern. Thus, the block level is a structural unit in between both spatial entities: the close meshed street network of a city generally delineates blocks typically with homogeneous physical features (Couclelis, 1992; Wurm et al., 2009, 2011). We use the street network of OSM and, if incomplete, we digitize missing streets to derive meaningful reference units (blocks) for the three test sites as shown in Figs. 2, 3, 4 and 5. This designation is based on the concept of homogeneous land-use regions that are made up from the arrangement of individual buildings and open spaces (land cover objects) presenting a specific land use type (Herold et al., 2002, 2003). To allow for a statistical designation and systematic structural comparison of the USTs CBD vs. Non-CBD, the physical parameters on building level in the 3-D city model are aggregated on the spatial level of statistical reference units — the blocks defined by the close meshed street network. Based on the designated reference unit of

391

blocks, physical parameters — average and maximum building height, average and maximum building volume, density of high-rise buildings, and floor space densities — are calculated. Fig. 4 shows the results of these calculations for the example of La Defense (Paris) and the surrounding urban fabric. The different physical features show significant physical differences within and across the test sites. However, no pre-defined thresholds are suggested in literature for a delineation of CBDs from Non-CBDs. Thus, we applied a statistical approach to classify CBDs based on the aggregated physical building parameters at the respective block level. Based on the hypothesis, that a significant physical and morphological difference of CBD areas exists when compared to the surrounding urban fabric, we applied a two class cluster analysis. This analysis is based on the assignment of a set of observations to groups, called clusters, so that observations in the same cluster show a high degree of similarity (Richards & Jia 2006). We use a standard unsupervised hierarchical clustering algorithm based on Euclidean distances to differentiate two clusters within the set of observations. These clusters can be thematically described as the classes CBD and Non-CBD on block level and allow for the identification of typical thresholds between these classes for each of the physical input parameters. Partitioning around medoids (PAM) was used to differentiate the two classes CBD and Non-CBD at the block level. This hierarchical clustering method is advantageous compared to other algorithms since it aims to minimize the sum of dissimilarities within clusters based on

Fig. 4. Physical parameters defining the urban structure at the level of statistical reference units (block level, which is defined by the street network) for La Defense and its surrounding urban fabric, Paris.

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Fig. 5. Spatial delineation of Central Business Districts at the reference units (block level, which is defined by the street network) for Canary Wharf (London), La Defense (Paris) and Levent (Istanbul), based on cluster analysis of the parameters shown in Fig. 4, which themselves are derived from the 3-D city model shown in Fig. 2.

the structure of the data (Reynolds et al., 2006). To obtain comprehensive thresholds between the two thematic classes, the clustering is executed across the whole set of building parameters and test sites. In a first step, the aggregated data are standardized to avoid dependence on the choice of measurement units according to Thomas and Hugget (1980) (Eq. 3): zi ¼

xi −μ σ

ð3Þ

sites and thematic class level. These plots include the median, the interquartile range and whiskers as defined by Tukey (1970): Maximum/average height (Fig. 6): The statistical analysis shows that these two physical features are by no means homogeneous across cities for CBDs, but their between-group variability to Non-CBDs is higher than their within-group variability. Although CBDs exhibit a high vertical variability with peak values of average and maximum height occurring at Canary Wharf (London), the building heights

where zi is the new value of any sample observation xi, μ is the sample mean, and σ is the standard deviation of the sampling distribution. Subsequently, a dissimilarity matrix is generated which presents the distances between all pairs of observations i and j with the coordinates (zi1…,zip) and (zj1…,zjp) in the 6-dimensional feature space (p = 6) based on the Euclidean norm (Eq. 4) (Kaufman & Rousseeuw, 1990):

dði; jÞ ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2  2  2ffi zi1 −zj1 þ zi2 −zj2 þ ⋯ þ zip −zjp :

ð4Þ

In the following, the PAM algorithm iteratively determines representative objects for the thematic classes. These so-called medoids are meant to present the optimal configuration of objects in the feature space by minimizing the total dissimilarity within groups. Thus, two clusters are designated by assigning each object (block) to the nearest medoid (UST), creating groups which can be thematically described as CBD and Non-CBD. The spatial delineation of CBD areas based on this two class clustering algorithm using the pre-determined physical parameter set is presented for the three study areas (Fig. 5). 3.4. Validation of the hypothesis: physical and morphological characteristics of CBDs Based on the two class cluster algorithm described above, spatial delineation of CBDs from Non-CBDs becomes possible at the block level. To test our central hypothesis, i.e. if the designated CBD areas show compliance with the hypothesis for every individual physical parameter used, the available information allows for a systematic structural comparison of the within- and between-group variance. For this reason, the parameters are displayed as boxplots for the test

Fig. 6. Boxplots illustrating a) average and b) maximum building height on class and test site level.

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are consistently significantly higher than the urban surrounding, and thus confirm the hypothesis. The within-group variability is lower among Non-CBD areas revealing a higher degree of homogeneity of surrounding urban morphology and underscoring the variance between the two classes. Maximum/average volume (Fig. 7): Due to high correlation of building height and volume, maximum and average volume show homogenous distributions similar to those of the aforementioned parameters across test sites. Significantly higher building volumes are found in CBDs of all sites, whereas lower within-group variabilities of Non-CBD areas are significantly exceeded by the variance between the two classes — confirming the hypothesis. Furthermore, the highest variability and absolute values of the average volume are found for Canary Wharf and La Defense owing to the high spatial accumulation of voluminous buildings within these CBDs. Floor space density/density of high-rise buildings (Fig. 8): Both parameters confirm the hypothesis that CBDs show significant physical differences when compared to other urban structure types. Considerable higher values of both parameters are found in the CBDs of all three sites, due to the concentration of high buildings with large floor spaces. The within-group variability of CBDs is high; however, significant differences to Non-CBDs are existent. The distinctly lower within-group variability of Non-CBD areas is clearly exceeded by the between-group variance of these parameters. The lowest values regarding the concentration of high-rise buildings are identified for Levent (Istanbul) due to its' dispersed spatial distribution of high rise buildings.

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Fig. 8. Boxplots illustrating a) floor space density and b) density of high-rise buildings on class and test site level.

These structural analyses support the central hypothesis of this study that CBDs and Non-CBD show significant physical and morphological differences. Although CBDs are by no means homogenous due to high within-class variabilities measured across the three test sites, constantly

higher between-group variances compared to the within-group variance of the surrounding urban fabric are obvious.

4. Classification of CBDs for entire megacities 4.1. Remote sensing data sets

Fig. 7. Boxplots illustrating a) average and b) maximum building volume on class and test site level.

The experiment for CBD vs. Non-CBD designation has been carried out for three areas with limited spatial extents at block level. Geometrically and thematically highly resolved spatial data sets such as the complied 3-D city models are mostly not available for such large areas as entire megacities. Thus, for area-wide classification of CBDs different spatial data sets are necessary. For a transfer of the suggested physical thresholds from the experiment for large area application, the choice of data is predominantly determined by technical aspects: 1) the large extent of the mega city test sites and 2) the need for geometric capabilities to differentiate the targeted land cover types and suggested morphological parameters identified. Hence, digital surface models for the entire mega city regions are required. The Indian satellite Cartosat-1 carries a sensor system which allows for the photogrammetric derivation of high resolution (HR) DSMs from stereo scenes. For this purpose Cartosat-1 is equipped with two simultaneously recording panchromatic cameras sensitive in the 500–850 nm wavelength regions and featuring a spatial resolution of 2.5 m. Due to the relatively large swath width of the imagery of 26 km, large urban agglomerations can be fully captured during only one or two paths — or in the case of our large area mega cities by relatively few paths. With these specifications, Cartosat-1 provides HR stereo imagery which is derived from fully automated

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Fig. 9. Cartosat-1 DSM and photo impression of the three test sites used in this study; Canary Wharf (London), La Defense (Paris) and Levent (Istanbul) (IPHS — International Planning History Society, 2010).

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Table 1 Upper and lower thresholds of the transition range derived from building and pixel level. Building level

Average height (m) Maximum height (m) Average volume (m3) Maximum volume (m3) Floor space density Density of high-rise buildings (%)

Pixel level

Lower

Upper

Lower

Upper

Threshold

Threshold

Threshold

Threshold

25.78 36.00 20,996 74,948 1.77 2.30

42.00 126.00 93,635 541,422 4.53 17.94

24.37 36.00 609 900 1.75 2.30

59.82 126.00 1,495 3,150 4.02 17.94

semi-global matching (D'Angelo et al., 2010), resulting in surface representations having a 5 m grid spacing. Of course, these geometric capabilities do not allow for the highly detailed classification of 3-D city models such as can be acquired from laserscan data or from highest resolution stereo imagery such as provided e.g. by the WorldView sensor. However, the unique advantage of Cartosat-1 stereo scenes is the large-area coverage at a relatively high spatial resolution, which is very useful in the current context. Fig. 9 displays a representation of the Cartosat-1 DSMs for the three test sites, as well as for the larger areas of the mega cities. The geometric resolution of 5 m produces a blurring effect, but still allows for a reasonable representation of the 3-dimensional morphology of urban structures. The data sets also show, especially for Istanbul, that hilly terrain somewhat blurs the representation of the height of artificial objects such as buildings. To reduce the terrain-induced blurring effect in the surface height data, we applied a morphological filtering module to obtain a measure of the above-ground building volume. The filtering process is based on a morphological opening (Haralick et al., 1987) via the sequential execution of a kernel-based minimum (erosion) and maximum (dilatation) filter at the pixel level of the DSM to derive a digital terrain model (DTM). Subsequently, the normalized DSM (nDSM) is calculated as the difference between the DSM and the DTM, i.e. a surface representation of the building volume above-terrain is achieved. Hence, DTMs present the decisive subproducts of the morphological filtering process and were exemplarily assessed using a 5 m digital terrain model of 1.5 m vertical accuracy (Landmap, 2012) for the test site Canary Wharf, London. In this context, measures of error from the difference image (Filtered DTM vs. Reference DTM) including the root-mean-square-error (RMSE) were calculated with regard to different kernel sizes. The results from systematic testing show the minimum deviations for a kernel window size of 10 × 10 pixels. Although minimum and maximum deviations range between −14.0 m and 12.4 m, the RMSE of 2.81 in combination with a close-to-zero mean error of 0.23 m and a standard deviation of 2.48 m reveal that the filtering results are sufficient for nDSM generation. Furthermore, urban footprint classifications derived from 30 m spatial resolution optical Landsat Thematic Mapper (TM) data (Taubenböck et al., 2012) are used to reduce the area of interest for CBD detection to the relevant urbanized areas, excluding non-urbanized land cover types like vegetation, etc. at the three megacities.

4.2. Wide-area classification approach For area-wide classification of CBDs beyond the limited areas of the 3-D city models, the thresholds identified for CBD delineation in the experiment using 3-D city models need to be adapted. This is necessary due to different characteristics of the available large

area EO data sets when extending the approach across entire mega city regions. Thus, problems encountered include that the identified thresholds have been derived at the block level for aggregated building information. But the classification of individual buildings from Cartosat-1 data is beyond the data sets geometric capability. Furthermore, the spatial entities used in the experiment using the 3-D city models — the blocks defined by the street network — are substructures close to the natural morphological change-overs in cities, and thus, appropriate to delineate morphological changes at large scale. However, in contrast to the 3-D city models, the problem encountered in this wide area approach is that OSM street networks are not always available or complete. Thus, due to the manual editing tasks involved beyond the test site level, the street network does not present a feasible data set for the designation of reference units in such large areas of mega cities and thus do not support a transferable approach. Based on these considerations, for area-wide classification of CBDs proxies appropriate for the capabilities and limitations of Cartosat-1 are required. These proxies need to substitute (1) the physical features identified for CBD classification in the highly detailed 3-D city model experiment, and (2) they need to substitute the used reference units of blocks.

1) The Cartosat-1 DSM pixel level contains information on physical features of the environment, which can substitute for the detailed knowledge at the object (building) level. For transformation of the gained knowledge for characteristic physical features for CBD delineation from the 3-D city model experiment, we apply the following procedure to identify thresholds at pixel level as proxies to be applied to the Cartosat-1 data: We overlay a rasterized version of the high detailed 3-D models (to simulate the pixel level) with the thematic clustering results (CBD and non-CBD areas), and derive a range of values for every parameter at the synthetic pixel level (e.g. average height, volume) for the spatial entities of the thematic classes CBD and Non-CBD. Due to the complexity of urban morphology and our approach of using multiple parameters, it is clear that the between-class transition of parameter values is not entirely disjunct. Thus, the overlapping range (transition range) between CBD and non-CBD values allow for the identification of upper and lower thresholds at the synthetic pixel level based on the 3-D city model. Deviations between the object and pixel levels predominantly exist for measures that depend on the particular reference unit considered (building objects vs. pixels), i.e. for all parameters but maximum height and building density of high-rise buildings. These thresholds serve as input for the area-wide classification of CBDs using the pixel level of Cartosat-1 data. Table 1 summarizes the lower and upper values of the transition range for each building parameter identified for CBD detection

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resulting from the 3-D city model experiment — at building as well as at the synthetic pixel level. Except for physical volumes, only minor deviations occur between values derived at the pixel and building level for the lower thresholds that are decisive for the area-wide classification of CBDs. Overall, it appears as if the uniform criteria derived from the analysis of 3D city models should allow for a transferable and thus, comparable localization of CBDs at the urban footprint level. 2) For global transferability we used a statistical reference unit as proxy for the block level defined by the street network. Chessboard segmentation was chosen to create square reference units having a 200 m cell size. This cell size is based on two considerations: First, the mean area calculated for all blocks at all three test sites in the experiment yields about 55,000 m2. This would result, if all blocks in reality would theoretically be squares, in a square with 235 m edge length. Second, we base our decision on the idea that the spatial entity applied should be larger than individual buildings to overcome the problem of classifying a CBD due to only one building fulfilling the physical criteria — as CBDs are seen as an areal unit formed by a group of buildings, not by an individual object. However, at the same time the spatial entity applied should be capable of capturing the urban alignment and structure, which are often heterogeneous at large scales in cities. From the experience and the logical consideration we apply an edge length of 200 m.

The multi-level substitutes — pixel and square units — allow for the aggregation of physical parameters at the square unit level from individual pixel values by the use of relational features between those two geometric levels. The integration of the urban footprint classification ensures that the area of interest is reduced to urbanized areas, and thus, other land cover types are excluded. In this connection, the area classified as built-up presents the reference for area-dependent measures such as floor space density, whereas sub-object terrain pixels present the physical above-ground building volume. Although some of these substitutes only present proxies and are associated with a certain information loss, they do reflect the typical physical features of CBDs. For classification, an approach combining all available morphological parameters was preferred instead of considering individual parameters that illuminate only a part of the complete picture of CBDs. As a consequence, the between-class transition of parameter values was not considered entirely appropriate, and encouraged the use of a fuzzy-based classification approach. The fuzzy-logic approach is therefore used to classify CBDs, based on the defined characteristic physical parameters. Fuzzy sets, i.e. classes with continuous grades of membership, combine membership values to derive the final classification result (Yager, 1987). For this purpose, we refer to the class thresholds identified at the building level and its substitute pixel level as these values represent the comprehensive physical differences of the structure type CBD compared to the surrounding's urban morphology. Here, it is pre-empted that class change-overs are not distinct, but exhibit a fuzzy transition range between the two classes CBD and Non-CBD defined by distinct lower and upper thresholds. The basic scheme of fuzzy-logic classification applied consists of two rules combined by a logical minimum (AND) operator: (1) The a priori knowledge about the urban footprint extent is employed as a criterion of exclusion by using the existence of sub-objects classified as built-up on urban footprint level as a hard thresholding rule. (2) Physical parameters aggregated on square units from pixel level are combined via a second minimum operator. In between the identified lower and upper thresholds of the transition range, the CBD membership value increases according to a fuzzy sigmoidal membership function from zero to one; thus, returning an individual membership value for each parameter. To combine these values, again, hard thresholding is used based on the logical decision that all reference

units must at least meet the lower threshold of the transition range for each parameter to be classified as CBD. The classification shows the urban footprint as outline of urbanized areas in the particular mega city (Fig. 10). Within the urban footprint the classification result shows the location, dimension and patterns of CBDs across the entire mega city regions of London, Paris and Istanbul. 4.3. Evaluation of detection and delineation accuracy The accuracy of CBD classification was assessed using two different approaches: (1) detection accuracy and (2) spatial delineation accuracy: 1) The spatial detection accuracy evaluates the capability of the developed algorithm to detect CBDs, based on the Cartosat-1 DSMs and the urban footprint for the entire mega city regions. For this purpose, all blocks classified as CBDs were visually compared to the urban structures presented by Google Earth© imagery and 3-D models, in order to quantify the user accuracy (Fig. 10). However, this procedure does not allow for the calculation of the producer accuracy, as no spatial reference data set covering all CBD areas in reality in the particular cities is available for the derivation of the error of omission. Overall, 134 of 177 blocks detected as CBDs across the three mega cities reflect structures in line with the defined physical features of CBDs. This represents a user accuracy of 76%. Examples of correctly detected blocks from this aerial visual evaluation include the financial district in the City (1) and St George Wharf (2) for London, the commercial center (3) and the national library (4) for Paris, as well as the high-rise residential district Atasehir (5) and the Maslak CBD (6) for Istanbul (Fig. 10). Between cities, constantly high accuracies are evident in London and Paris whereas Istanbul features a higher error of commission of 33%. This can be attributed to the more complex urban terrain in Istanbul inducing morphological errors. However, the overall high detection accuracies confirm the appropriateness of data, the applicability of the algorithm as well as the defined and applied thresholds across cities and the transferability of the presented method. 2) The spatial delineation accuracy is assessed to determine the spatial precision of the final CBD classification for the area-wide approach, compared to the statistical delineation of CBDs by dissimilarity clustering using the 3-D city models at the selected test sites. The accuracy is assessed by standard pixel-based confusion matrices for the two thematic classes (CBD/Non-CBD) in comparison to the statistically designated building blocks from the analysis of 3-D city models. Across the three test sites, the overall accuracies range between 83% and 86%. Furthermore, the Kappa-Index constantly exceeds a value 0.38 in comparison to a chance agreement. These numbers indicate a high accuracy of the overall classification result on test site level. However, delineation results feature increased errors of omission resulting in considerable lower producer accuracies ranging between 63% and 77%. Furthermore, relatively high errors of commission across test sites lead to low user accuracies between 31% and 67%. These shortcomings in terms of spatial precision result predominantly from the multi-scale approach: the choice for square units as analytical spatial entities instead of the street network as spatially structuring element — to ensure independence from external data sets — generates a distinct problem of scale as these artificial units for parameter aggregation inherit deviation from the true urban morphology. Thus, the delineated CBD areas are not found to be overly precise regarding their spatial form but are generally correctly delineated from the surrounding urban environment.

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397

Fig. 10. Maps of Central Business District locations across the mega city regions of London, Paris and Istanbul classified from Cartosat-1 imagery and urban footprint classifications from Landsat imagery as outlined in Section 4.2, along with visual impressions of sample areas taken (Google Inc., 2012).

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Fig. 11. Calculation scheme for the spatial configuration metrics for analysis of locations and patterns of CBDs with respect to the entire urban footprint.

5. Cross city spatial CBD comparison for mega cities The study shows the capability of remote sensing to consistently detect CBDs across entire mega city regions. With it, the data basis for deriving new findings from an analytical spatial urban pattern perspective is given. In the following, we try to exemplify a possible

value-adding by spatially measuring, analyzing, comparing and interpreting urban patterns to find characteristics of city configurations. Our comparative analysis of location, dimension and pattern configurations of CBDs in the three European mega cities aims to detect similarities or differences in this respect. Therefore, we apply a set of spatial metrics. These are quantitative, aggregated measures

Table 2 Overview of spatial configuration metrics. Metric Relative mean CBD-to-CBD distance (a)

Abbrev. (unit) Equation MD (%)

n

¼ Relative mean CBD-to-CBD nearest neighbor distance (b)

Relative mean CBD-to-center distance (d)

∑i¼1

∑j¼1 dij Dmax = Max. urban footprint extent; n

n

Dmax n

MNND (%)

¼

CBD density in relation to the built-up area in a CBD-D5 (%) 5 km circle around the historic center (c)

Normalization variables n

∑i¼1 dmini

Dmax ACBD−5km ¼ AUFP−5km n

MDC (%)

¼

Dmax = Max. urban footprint extent;

n

∑i¼1 dci n

Dc−max

AUFP-5km = Built-up area within the 5 km radius; Dc-max = Max. center-to-urban footprint edge distance;

Further variables ACBD = Total area covered by CBDs; n = number of CBDs detected; dmin = Nearest Neighbor CBD-to-CBD distance; dc = CBD-to-center distance; dij = distance between CBD i and CBD j; i/j = 1,…,n CBDs

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399

Fig. 12. Spider-charts presenting the spatial metrics used in the cross-city comparison of London, Paris and Istanbul developed herein.

to describe the spatial pattern of cities (e.g. Herold et al., 2005; Huang et al., 2007; Taubenböck et al., 2009). For straight-forward quantitative comparison, the number of CBDs (n(CBD)), the absolute CBD area (AA) and relative total CBD area (RA) compared to the spatial extent of the particular urban footprint are used as indicators. Furthermore, the largest patch index (LPI) as a measure for the dominance of the largest CBD compared to the entire CBD area is calculated (McGarigal & Marks, 1995). To compare the spatial configuration of CBDs across cities (Table 2), the mean CBD-to-CBD distance (MD) in combination with the nearest neighbor distance (MNND) are analyzed as measures of spatial dispersion. Furthermore, the CBD density within the historic urban core (CBD-D5) and the mean CBD-to-center distance (MDC) reflect geographic centrality of CBD distribution. For reason of across-city comparability, these measures of spatial configuration are normalized by the dimension of the largest urban patch of the particular city (Fig. 11). Comparing quantities, spatial dimensions and patterns of CBDs across cities is complex due to the different perspectives represented by the variety of parameters used. For capturing the quintessential information, we apply a spider chart for comparative analysis. The spider chart is calculated as a relative diagram. The maximum parameter value of each parameter determines the length of the particular axis resulting from a standardization with the pre-determined dimension of the particular urban footprint. The only two exceptions are the two absolute parameters ‘number of CBDs’ and ‘cumulative CBD area’ (n(CBD) and AA). With the spider chart we aim to conjointly capture different spatial indicators defining CBD configuration to paint a characteristic picture of the different spatial aspects across cities. Fig. 12 shows the development of the graphs representing spatial characteristics for the three mega city regions Paris, London and Istanbul in relative comparison.

In general, the spider chart of Fig. 12 clearly reveals that the mega cities London and Paris show very similar locations, dimensions and patterns of their CBDs, while Istanbul shows a different spatial character. In general, the number of detected CBDs across the mega cities is relatively similar: 22 for London, 23 for Paris and 25 for Istanbul. Also, the total CBD area is averaging at around 6 km2 for all cities. Although these parameters are similar, differences in spatial configurations of the CBD landscapes exist: The relative CBD coverage compared to the entire urban footprint is highest for Istanbul for example. However, Istanbul has the lowest LPI of CBDs. This shows that Istanbul does not feature an extensive dominant CBD such as La Defense in Paris or the financial district in the City of London, but rather a more dispersed and fragmented CBD pattern. Comparing the spatial configuration of CBDs, Istanbul exhibits the highest mean CBD-to-CBD distance but the lowest nearest neighbor distance. Thus, Istanbul's urban footprint level is characterized by dispersed spreading of spatially clustered center, whereas London and Paris show a more regular distribution of CBDs. Furthermore, Istanbul exhibits the lowest density by CBDs within the urban core area, and a high mean CBD-to-center distance. In contrast, CBDs in London and Paris are located geographically more central with moderate mean distances to the historic center. Overall, these values reflect the nature of urban growth in the investigated cities. London and Paris regarded as rather monocentric and recently marginally growing metropolitan regions (Halbert, 2006; Taubenböck et al., 2012) exhibit a more regular, planned distribution of CBDs. Their CBDs are found to be in geographically central locations with dominant and specialized centers. In contrast, the sprawling urban nature and the high density of buildings including informal settlements (Seger, 2012) in Istanbul directs construction of CBDs to remaining open spaces leading to a more scattered urban CBD landscape.

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6. Conclusion

References

Definitions of CBDs as a mental construct are fuzzy and mostly qualitative. This study has introduced a conceptual and practical framework to approach CBD detection and delineation from a physical perspective. From an empirical study on three CBD areas in London, Paris and Istanbul distinctive and quantifiable morphological parameters have been investigated and suggested for CBD delineation. In this context, remote sensing has proven to be an up-to-date, consistent and area-wide data provider capable for the capturing of urban structural elements in three dimensions. For CBD delineation based on physical parameters, digital surface models from the Indian satellite Cartosat-1 have proven to be particularly useful, as they feature large aerial coverage and availability at a geometric resolution capable for the extraction of the decisive physical and morphological attributes. The derived and suggested thresholds used in this process are not fixed thresholds for derivation of CBDs in any city across the globe, but rather represent a first empirical approach. With high overall accuracies of 83 to 86%, the area-wide approach proves the appropriateness of the data, the applicability of the algorithm, the transferability of the presented methods and the relevance of the defined and applied thresholds across large and complex urban areas such as mega cities. Overall, these datasets and methods allow for the generation of increased knowledge on the spatial locations, dimensions and pattern configurations of structural types in cities to be generated. However, we have to bear in mind that this approach is physically driven, i.e. the results may also include building structures similar to CBDs, but with differing functions. One example is the classified high-rise district Atasehir in Istanbul, which features many of the characteristic physical parameters of CBDs but is in fact a residential area. Thus, the combination of this suggested physical approach with socio-economic knowledge appears the most promising. Furthermore, it needs to be proven that these EO-data and applied methods allow the delineation of CBDs in cities with limited building heights. As it is assumed in this paper and hinted in literature that CBDs show a significant difference in urban morphology, the presented method needs to be tested for cities where clearly distinctive CBD areas do not show a height difference but e.g. only a morphological difference due to building sizes or volume compared to surrounding urban morphology. The urban geographical results reveal that the basically concentric and compact spatial outline of the mega cities of Paris and London is reflected in a similar pattern for their CBDs. In particular they show a centered pattern with one spatially dominating CBD. In contrast, Istanbul features a more complex basic urban footprint and different spatial development dynamics in recent decades. Thus, the urban landscape features a more dispersed spatial CBD landscape. From the gained knowledge, further research for urban structure classifications beyond a single urban structural type appears warranted, especially because spatial knowledge on USTs such as CBDs, slums or high class residential areas is limited. This is in particular true for very dynamically growing urban areas, which are primarily located in developing countries across the globe. In this context, the combination of Cartosat-1 DSMs and the urban footprint classification from Landsat data are promising components for providing this information at large area coverage and high thematic detail.

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