Calculating Intraurban Agglomeration of Economic Units with Planar and Network<i>K</i>-Functions: A Comparative Analysis

Garrocho-Rangel, Carlos Félix; Lobato, José Antonio; Chávez, Tania

doi:10.1080/02723638.2013.778655

Cited by 16 publications

(9 citation statements)

References 32 publications

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“…We constructed a set of parsimonious models that regress street network link-based cluster descriptors on link-based centrality using sDNA+, a plug-in for ArcGIS (Chiaradia et al, 2019; Cooper et al, 2019), with controls for housing density, other choice dimensions and land price. Measuring the clustering tendencies of different kinds of economic units in urban space has been a major challenge in assessing agglomeration patterns, scale and intensity of facilities, and there is a need to diversify the methodology to accurately measure spatial concentration or dispersion of economic units in an area (Garracho-Rangel et al, 2013; Law, 2017; Scoppa and Peponis, 2015). Our study follows and extends other recent applications of network urban geometry, reviewed in the following sections, to gain deeper understanding and predictive power in modelling the clustering of urban functions.…”

Section: Introductionmentioning

confidence: 99%

Associating street-network centrality with spontaneous and planned subcentres

et al. 2020

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Scientific studies have long demonstrated how economic activities regularly distribute themselves within a city in response to geographical centrality. Following the growing interest in network geography in understanding urban dynamics, rather than measuring centrality (accessibility) by a priori knowledge of central business district (CBD) locations, in this article we measure the centrality of each link in a city’s street network, modelled as a topological graph. We use this to understand clustering behaviour of firms by industrial classification in the city of Ankara, Turkey. Our underlying hypothesis rests on the assumption that the geometry and topology of an urban grid contains accessibility information about the distribution of agglomeration economies and diseconomies, and that different types of enterprises are sensitive to this distribution in various ways. Among other things, the results of the study allow us to predict the evolution of what we call candidate centres (locations that could, by virtue of their connectivity footprint, become subcentres), actual subcentres and CBD functions in response to changes in a city’s street network. Decoding how commercial cluster locations interact with the detailed pattern of street-network-based centralities will be helpful for urban planning policy, in particular for commercial zoning decisions such as expanding CBDs and identifying locations for new subcentres that have an acceptable chance of success.

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Section: Introductionmentioning

confidence: 99%

Associating street-network centrality with spontaneous and planned subcentres

et al. 2020

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“…These three generations of methods can further be separated into two groups from geographical perspectives (Garrocho-Rangel et al., 2013): discrete-space methods vs. continuous-space methods. In the former, the region being analyzed is divided into sub-units with an arbitrarily chosen level (e.g.…”

Section: Related Workmentioning

confidence: 99%

Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China

Liu

Chen

et al. 2021

Environment and Planning B: Urban Analytics and City Science

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Industry cluster development is important to stimulate regional economy. Conventional spatial methods for detecting industry clusters use a pairwise manner to infer the co-location relationships of multiple industrial types or instances, which increases the difficulty of interpreting the results. This study proposes to use co-location patterns (CPs) mining method to directly capture the co-location of multiple industrial types from the bottom up without any conditions of data relations defined a priori. The method is applied in Dongguan, China, to investigate the industry cluster patterns at an intra-urban scale. At the city level, the results show prevalent CPs of information communication and technology industry and other associated sectors. At the sub-regional level, however, approximately 41% of the industrial CPs are different from those obtained at the city level. The local features of sub-regional industry clusters are associated with productions of, for instance, sporting goods and toys, digital instrument and office equipment, machine parts and woodware, and textile-related products.

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“…Ripley's K has been widely used to measure the aggregation of spatial units [45,46]. We acquire Expected K and Observed K through Ripley's K function analysis on the data of running track points.…”

Section: Spatial Aggregation Analysis Of Running Track Pointsmentioning

confidence: 99%

Predict the Suitable Places to Run in the Urban Area of Beijing by Using the Maximum Entropy Model

Song

Zhang

2021

IJGI

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Many people in the world do not have enough physical activities to maintain good health, which has recently become a threat to public health. In addition to individual genetic and social factors, we considered the geographical environment of the city as a factor that affects these healthy physical activities. We used the location-based data in social media combined with the open geographic data to explore the impact mechanism of urban environmental factors on human running behaviors. This study collected nine urban environmental variables and preference tracks in Beijing’s main urban area. We used the Maximum Entropy Model (MaxEnt) to analyze the relationship between running behaviors and environmental variables and identify suitable areas for running in Beijing. The results showed that: firstly, the variables of attractions, sports and sidewalk density contributed the most to running suitability. Secondly, 47.5% of the main urban areas in Beijing are suitable for running, mainly in the main urban areas with better economic development. Thirdly, the distribution of suitable places for running is unfair in that some places with large populations do not have a matching running environment.

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Calculating Intraurban Agglomeration of Economic Units with Planar and NetworkK-Functions: A Comparative Analysis

Cited by 16 publications

References 32 publications

Associating street-network centrality with spontaneous and planned subcentres

Associating street-network centrality with spontaneous and planned subcentres

Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China

Predict the Suitable Places to Run in the Urban Area of Beijing by Using the Maximum Entropy Model

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