Structural Models of Retail Distribution: Analogies with Settlement and Urban Land-Use Theories

Davies, Ross L.

doi:10.2307/621554

Cited by 31 publications

(17 citation statements)

References 4 publications

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“…In the early stage, most of the urban business distribution researches are discussing the patterns of urban commercial distribution based on modernist urban planning ideas [20], [21]. Later, with the standardization and dataization of urban management, there are studies based on regional statistical data with maps [22], of which the methodology is advanced at that time.…”

Section: Relative Workmentioning

confidence: 99%

Urban Commerce Distribution Analysis Based on Street View and Deep Learning

Wang

Kita

et al. 2019

IEEE Access

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Urban commerce and its distribution have always been an important part of urban research. However, most previous studies were based on statistical data and did not reflect real street experience. Thanks to the Street View image and deep learning technology, researchers are able to carry out large scale studies from real human visual experience. In this article, we aim at sensing the commercial spaces in cities. In order to achieve this ultimate goal, deep learning is applied to process the raw data of Street View image. We disassemble the goal into three tasks: firstly, obtaining all the Street View images in a specific area; then classifying the Street View images according to the commercial facilities in it; and finally creating a visualization of the detected data into a map. For the first task, we get the road network coordinate information from the openstreetmap (OSM) website, set the sampling point on the road, and then download the Street View images of the sampling points' coordinate through the API provided by Baidumap. For the second task, we adopt a two-level learning strategy rather than directly using Deep Convolutional Neural Network for classification. For the final task, we choose the heat map as the expression of the results and draw the map by the existing GIS software. Furthermore, the results from this study can be conveniently combined with other data because of the use of street-network-based data structure. An application of this method combines with street-network data, the calculation of a city's 15-minute commercial service circle coverage is also shown in this study. INDEX TERMS Smart cities, urban commerce, deep learning, urban planning, street view image, object detection.

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Section: Relative Workmentioning

confidence: 99%

Urban Commerce Distribution Analysis Based on Street View and Deep Learning

Wang

Kita

et al. 2019

IEEE Access

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“…Some blamed these limitations on the weakness of the central place theory to adequately account for the behavior of retailers and consumers ( Scott, 1970 ). More importantly, Davies (1972) observed, among others, the need to account for specific local contextual factors in determining the centrality of retail centers. Since the turn of the 21st Century, geographers and urban planners have produced studies that remediate some of these limitations while also pushing forward robust quantitative analysis of retail geography and urban morphology.…”

Section: The Geography Of Retail Spaces: a Review Of The Literaturementioning

confidence: 99%

Spaces of market politics: Retailscapes and modernist planning imaginaries in African cities

2020

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This article interrogates the spatial, economic, and cultural underpinnings of traditional retailscapes in sub-Saharan Africa to understand how they intersect with contemporary urban planning policies. It does so by deploying a multi-step investigation of the issues from four perspectives: transportation corridors, spheres of influence, centrality, and observed spatial patterns – each leading us to connections between retail spaces and planning of African cities. Our analyses of 22 traditional satellite markets in Kumasi are distilled into four key findings. First, these markets emerge along, and at the intersection of, intra- and inter-urban road networks as a means of granting local access to indigenous goods and services. Second, the spatial distribution and spheres of influence of the markets partly support Christaller's hypothesis regarding the willingness of people to travel far distances to access higher-order goods and services. The hypothesis fails, however, to recognize that some traditional markets can still have high spheres of influence without providing higher-order goods and services because they constitute vital nodes in the rural-urban food networks. Third, we find a spatial clustering of these markets, suggesting agglomerative tendencies among the markets. Finally, we argue that the observed spatio-social patterns of Kumasi's retailscape only make sense if they are situated within the city's modernist urban planning imaginaries. Specifically, the city's retailscape embodies ongoing placemaking strategies, which involve the expropriation of urban spaces from traders to modernize the cityscape.

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“…[ 15 – 25 ], while others have suggested more diverse models to describe urban commercial space from the perspective of the market structure and size of the urban center [ 13 , 24 ]. Some recent studies have introduced specific hypotheses to create more dynamic and realistic models, especially considering consumer factors [ 15 , 16 , 19 ] and the changes in commercial center systems based on central place theory [ 17 , 18 , 20 , 21 , 23 , 25 ]. However, almost all the data used in these studies were derived from traditional surveys, which are expensive and require considerable time and effort.…”

Section: Introductionmentioning

confidence: 99%

Detecting urban commercial patterns using a latent semantic information model: A case study of spatial-temporal evolution in Guangzhou, China

Chen

Tao

et al. 2018

PLoS ONE

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With rapid economic growth since the 21st century, cities in China have experienced considerable economic and social reconstruction. Driven by rapid industrialization, urban spatial structures are undergoing evolution and change. Therefore, this paper analyzes the processes and mechanisms associated with the evolution of the commercial spatial structure in Guangzhou after the financial crisis in 2008 based on both theoretical and empirical analyses. We use a Dirichlet multinomial regression (DMR) model to extract latent semantic information and determine urban functional areas from global positioning system (GPS) and point-of-interest (POI) data collected in Guangzhou in 2009 and 2013. In addition, we use movement patterns and POI data to identify the evolution of Guangzhou's commercial zones from 2009 to 2013. The results show that the urban commercial structure in Guangzhou gradually changed from a single-center model to a multi-center model with dispersed clusters and that the distribution of the entire spatial structure changed. Meanwhile, Guangzhou’s commercial structure not only varied over time but also exhibited specific geographical features. This paper demonstrates that the proposed method can clearly identify the boundary of the commercial area in Guangzhou and provides a valid spatial-temporal model of change in the city. Moreover, this study not only expounds the future development trends of the urban spatial structure in Guangzhou from a microcosmic perspective but also provides a scientific basis for clarifying the spatial locations and development advantages of urban functions within the city.

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Structural Models of Retail Distribution: Analogies with Settlement and Urban Land-Use Theories

Cited by 31 publications

References 4 publications

Urban Commerce Distribution Analysis Based on Street View and Deep Learning

Urban Commerce Distribution Analysis Based on Street View and Deep Learning

Spaces of market politics: Retailscapes and modernist planning imaginaries in African cities

Detecting urban commercial patterns using a latent semantic information model: A case study of spatial-temporal evolution in Guangzhou, China

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