Intra-urban analysis of commercial locations: A GIS-based approach

The urban landscape is an interspersed mixing of residences, shops, theatres, parks, natural areas, and a multitude of other uses. From the early days of the central markets, to the planned downtown, to the heavily planned super-regional shopping complexes, the commercial areas within this urban landscape have evolved. There has been considerable research conducted on analyzing the commercial structure of urban environments in an attempt to better understand the nature of retailing and its resultant impacts on the geography of the city. This paper details the development of a GIS-based semi-automated method to detect commercial structure. The approach generated nearest commercial neighbour statistics as a measure of proximity between commercial locations. These were used as the foundation for clustering commercial operations into commercial areas.https://www.igi-global.com/gateway/article/7521

Intra-Urban Analysis of Commercial Locations: A GIS-Based Approach

The urban landscape is an interspersed mixing of residences, shops, theaters, parks, natural areas, and a multitude of other uses. From the early days of the central markets, to the planned downtown, to the heavily planned super-regional shopping complexes, commercial landscapes evolve. There has been considerable research conducted on analyzing the commercial structure of urban environments in an attempt to better understanding the nature of retailing and its resultant impacts on the geography of the city. This research has three broad goals: a) to develop a technique that makes operational, in a systematized and objective manner, an approach to analyzing the structure of the commercial environment; b) to apply the approach within a GIS environment, and; c) to develop a generalized typology of urban commercial structure. The systematized analysis is a series of guidelines and statistics which can be applied in an objective manner. The development of the nearest commercial neighbor as a statistical measure of proximity to other commercial operations was the foundation of the approach to clustering commercial operations in to retail areas. To achieve the overall goals, three census metropolitan environments (Sudbury, Kitchener and Ottawa) were used as study areas. These cities represent small, medium and large census metropolitan environments, respectively, within Canada. Commercial locations for each city were extracted from a national database of locations and mapped in a GIS environment. For each study area, the nearest commercial neighbor values were generated and the appropriate statistics extracted. Commercial clusters were generated by using the average nearest commercial neighbor value and multiples of the median commercial neighbor value. These nearest neighbor and median values were inputted into a buffering routine as the buffer size. The resulting clusters were then compared to ortho-imagery and in the case of Kitchener, land use planning documents. Two approaches for cluster generation were employed; 1) Point-only where all individual addresses were used on the clustering, and; 2) Point plus Polygon where those commercial operations that existed within polygons (malls and central business districts) were removed from the dataset, the remaining points were then clusters and the polygons added back to the results. Finally the results from both clustering approaches were compared to land use parcels to assess accuracies of the technique. The results indicated that the overall method proposed was effective in determining commercial zones, and that the 2x iteration of the median nearest commercial neighbor technique yielded the most accurate results. Moreover, three main conclusions were drawn. The first was that there was a difference, and in some cases significant differences, between the land use planned commercial areas and areas that have grown larger through agglomeration. Secondly, there are density variations between core and suburban areas that, at times, resulted in a larger definition of a commercial area within the core because the lesser dense suburban areas having an impact on the nearest commercial neighbor values. Thirdly, there was considerable over-capturing of commercial areas when the buffer multiples were greater than 3x. In addition, the point plus polygon clustering technique indicated that while the defined areas were more accurate when the polygons were used, it was only in areas where those polygons were the main commercial cluster. In mixed areas, there was no discernable advantage to using the polygons. Furthermore, the removal of points had a strong impact of the nearest commercial neighbor values generated. Lastly, when dealing with polygons, the geographic arrangement of the commercial type became important. Based on the findings of the commercial zone analysis, a typology of commercial development was detailed. This typology contained three main geographic components, namely the core, suburb and gateway areas of the urban environment. Within each geographic location, a series of commercial forms were identified. This new typology allowed for the inclusion of historical remnants of landscapes and consequently allows for a comparison against older typologies. The typology employed a three part urban classification system which is applicable to any type of urban environment and, finally, the focus on geographic form removes the impact of store changes and the changes in the nature of commercial zones over time. This research has operationalized a systematic and replicable method of examining urban commercial location data for the purpose of determining commercial structure. This technique can be applied to future datasets easily and objectively allowing for a readily updatable typology; thus rendering it less static than previous typologies. It is the use of the technology, namely GIS, that adds this dynamism to the analyses. Furthermore, it has been demonstrated that the potential exists for using GIS to analyze commercial location data. This research has contributed to this evolution by analyzing the geography of commercial development during a snapshot in time. However, by developing a series of operational and repeatable techniques that focus on the geographical organization of commercial locations, it is hoped that the results will function as the conceptual and practical framework for commercial structural analysis of urban environments for future studies

Street centrality and land use intensity in Baton Rouge, Louisiana

This paper examines the relationship between street centrality and land use intensity in Baton Rouge, Louisiana. Street centrality is calibrated in terms of a node's closeness, betweenness and straightness on the road network. Land use intensity is measured by population (residential) and employment (business) densities in census tracts, respectively and combined. Two CIS-based methods are used to transform data sets of centrality (at network nodes) and densities (in census tracts) to one unit for correlation analysis. The kernel density estimation (KDE) converts both measures to raster pixels, and the floating catchment area (FCA) method computes average centrality values around census tracts. Results indicate that population and employment densities are highly correlated with street centrality values. Among the three centrality indices, closeness exhibits the highest correlation with land use densities, straightness the next and betweenness the last. This confirms that street centrality captures location advantage in a city and plays a crucial role in shaping the intraurban variation of land use intensity. (C) 2010 Elsevier Ltd. All rights reserved

Spatial dependence and heterogeneity in patterns of urban deprivation

Developments in the provision and quality of digital data are creating possibilities for finer resolution spatial and temporal measurement of the properties of socio-economic systems. We suggest that the ?lifestyles? datasets collected by private sector organisations provide one such prospect for better inferring the structure, composition and heterogeneity of urban areas. Clearly, deprivation and hardship are inextricably linked to incomes from earnings and transfer payments. In many countries (e.g. the UK) no small area income measures are collected at all, and this forces reliance upon commercial sources. Yet, the use of such data in academic research is not without considerable problems. In the same spirit as Gordon and Pantazis (1995) we thus think it necessary to retain some linkage to population census data ? but in a way which is much more sensitive to spatial context. A critical issue is thus to understand the scales at which both income, and the variables that are used to predict it, vary (see also Rees, 1998; Harris and Longley, 2002). We address some of these issues in the context of the debate about the intra-urban geography of hardship and social exclusion. Low income fundamentally restricts the abilities of people to participate actively in society (Harris and Longley, 2002), yet reliable, up-to-date income measures at fine spatial scales are rarely available from conventional sources. As a consequence, many indicators of deprivation are reliant upon data sources that are out of date and/or entail use of crude surrogate measures. Some measures bear little clear correspondence with hardship at all. Other widely-used indicators are spatially variable in their operation. The broader issue concerns the scale and extent of ?pockets? of hardship and the scale ranges at which difference is deemed manifests. The problems are further compounded if each of the range of surrogate measures used to specify a concept operates at different scales. Taken together, it remains unclear whether meaningful indicators of social conditions can ever be adequately specified, or whether generalised representations can be sufficiently sensitive to place. Using a case study of Bristol, UK, we compare the patterns of spatial dependence and spatial heterogeneity observed for a small area (?lifestyles?) income measure with those of the census indicators that are commonly used as surrogates for it. This leads to specification of spatial dependence using a spatially autoregressive model, and accommodation of local heterogeneity using geographically weighted regression (GWR). This analysis begins to extend our understanding of the determinants of hardship and poverty in urban areas: urban policy has hitherto used aggregate, outdated or proxy measures of income in a less critical manner; and techniques for measuring spatial dependence and heterogeneity have usually been applied at the regional, rather than intra urban, scales. The consequence is a limited understanding of the geography and dynamics of income variations within urban areas. The advantages and limitations of the data used here are explored in the light of the results of our statistical analysis, and we discuss our results as part of a research agenda for exploring dependence and heterogeneity in spatial distributions.

Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment

Land use regression (LUR) modelling is increasingly used in epidemiological studies to predict air pollution exposure. The use of stationary measurements at a limited number of locations to build a LUR model, however, can lead to an overestimation of its predictive abilities. We use opportunistic mobile monitoring to gather data at a high spatial resolution to build LUR models to predict annual average concentrations of black carbon (BC). The models explain a significant part of the variance in BC concentrations. However, the overall predictive performance remains low, due to input uncertainty and lack of predictive variables that can properly capture the complex characteristics of local concentrations. We stress the importance of using an appropriate cross-validation scheme to estimate the predictive performance of the model. By using independent data for the validation and excluding those data also during variable selection in the model building procedure, overly optimistic performance estimates are avoided. (C) 2017 Elsevier Ltd. All rights reserved

How Polycentric is a Monocentric City? The Role of Agglomeration Economies

Can the demise of the monocentric economy across cities during the 20th century be explained by decreasing transport costs to the city center or are other fundamental forces at work? Taking a hybrid perspec¬tive of classical bid-rent theory and a world where clustering of economic activity is driven by (knowledge) spillovers, Berlin, Germany, from 1890 to 1936 serves as a case in point. We assess the extent to which firms in an environment of decreasing transport costs and industrial transformation face a trade-off between distance to the CBD and land rents and how agglomeration economies come into play in shaping their location deci¬sions. Our results suggest that an observable flattening of the traditional distance to the CBD gradient may mask the emergence of significant agglomeration economies, especially within predominantly service-based inner city districts.Transport Innovations, Land Values, Location Productivity, Agglomeration Economies, Economic History, Berlin.

How Polycentric is a Monocentric City? The Role of Agglomeration Economies

Can the demise of the monocentric economy across cities during the 20th century be explained by decreasing transport costs to the city center or are other fundamental forces at work? Taking a hybrid perspective of classical bid-rent theory and a world where clustering of economic activity is driven by (knowledge) spillovers, Berlin, Germany, from 1890 to 1936 serves as a case in point. We assess the extent to which firms in an environment of decreasing transport costs and industrial transformation face a trade-off between distance to the CBD and land rents and how agglomeration economies come into play in shaping their location decisions. Our results suggest that an observable flattening of the traditional distance to the CBD gradient may mask the emergence of significant agglomeration economies, especially within predominantly service-based inner city districts.