Defining and Delineating the Central Areas of Towns for Statistical Monitoring Using Continuous Surface Representations

Thurstain-Goodwin, M; Unwin, David

doi:10.1111/1467-9671.00058

Cited by 128 publications

(97 citation statements)

References 8 publications

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“…Spatial KDE has been applied in many geographical studies, but most studies are related to the density of events or population [45,46]. In general, the surface is partitioned into grid cells.…”

Section: A Density-based Methodsmentioning

confidence: 99%

“Voting with Their Feet”: Delineating the Sphere of Influence Using Social Media Data

Wong

Huang

2017

IJGI

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Delineating regional boundaries for places has a long tradition in geography, urban analysis and regional planning. Its theoretical basis may be traced back to the central place theory. The normative approach, using spatial interaction models, has been used, and the empirical approach, using commuting data, is also popular. While gathering commuting data using traditional methodologies (e.g., surveys) is costly, data capturing people's locations and their thoughts, are widely available through social media platforms. This article demonstrates that Twitter data can be used to delineate boundaries among competing places. A generic approach based on the density of place names mentioned in geo-tagged tweets was proposed to reflect the sphere of influence or dominance of places. Locations with the same levels of influence from competing places constitute the boundaries delineating the regions dominated by the respective places. The method was tested to determine the boundaries between two metropolitan regions, two local cities, and two neighborhoods or communities. Results from these simple case studies demonstrated the validity of the general approach for evaluating existing place boundaries and determining boundaries if they have not been delineated. The method is applicable to different levels of the place hierarchy and has practical values for planning of places of different sizes.

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Section: A Density-based Methodsmentioning

confidence: 99%

“Voting with Their Feet”: Delineating the Sphere of Influence Using Social Media Data

Wong

Huang

2017

IJGI

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“…Thurstain-Goodwin and Unwin [78] illustrated how to extract intra-urban centers of UK towns using kernel density estimation to transform the data from point or area "objects" into continuous surfaces of spatial densities. Following this method, we attempt to extract working, shopping and neighborhood centers of 286 Chinese cities at the prefectural level and above, based on different types of points of interest (POI) obtained from Open Street Map [79].…”

Section: Extraction Of Intra-urban Centers At Each Levelmentioning

confidence: 99%

Optimal Intra-Urban Hierarchy of Activity Centers—A Minimized Household Travel Energy Consumption Approach

Zhang

Xie

2015

Sustainability

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An intra-urban hierarchy of activity centers interconnected by non-motorized and public transportation is broadly believed to be the ideal urban spatial structure for sustainable cities. However, the proper hinterland area for centers at each level lacks empirical study. Based on the concentric structure of everyday travel distances, working centers, shopping centers, and neighborhood centers are extracted from corresponding types of POIs in 286 Chinese cities at the prefectural level and above. A U-shaped curve between Household Transportation Energy Consumption (HTEC) per capita and center density at each of the three levels has been found through regression analysis. An optimal intra-urban hierarchy of activity centers is suggested to construct energy-efficient cities.

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“…Kernel density estimation (KDE) methods, on the other hand, do explicitly take this uncertainty into account. They have been used in several areas of geographic information analysis such as crime and traffic investigations [5], but also for the automated definition of city centres [75] as well as geographic information retrieval [54]. The main principle behind KDE is based on determining a weighted average of data points within a moving window centred on a grid of points.…”

Section: Approximating Region Boundariesmentioning

confidence: 99%

“…Different KDE representations can be combined using an indexed overlay, which allows to join different variables when modelling a region or phenomenon. Thurstain-Goodwin and Unwin [75] modelled city centres by overlaying kernel density representations of indices for property, economy, diversity and visitor attractions of a town. In order to derive an actual polygon for a region from the three-dimensional kernel density surface, an appropriate threshold needs to be chosen, which can be interpreted as a level of confidence.…”

Section: Approximating Region Boundariesmentioning

confidence: 99%

Generating approximate region boundaries from heterogeneous spatial information: An evolutionary approach

Schockaert¹,

Smart²,

Twaroch³

2011

Information Sciences

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Spatial information takes different forms in different applications, ranging from accurate coordinates in geographic information systems to the qualitative abstractions that are used in artificial intelligence and spatial cognition. As a result, existing spatial information processing techniques tend to be tailored towards one type of spatial information, and cannot readily be extended to cope with the heterogeneity of spatial information that often arises in practice. In applications such as geographic information retrieval, on the other hand, approximate boundaries of spatial regions need to be constructed, using whatever spatial information that can be obtained. Motivated by this observation, we propose a novel methodology for generating spatial scenarios that are compatible with available knowledge. By suitably discretizing space, this task is translated to a combinatorial optimization problem, which is solved using a hybridization of two well-known meta-heuristics: genetic algorithms and ant colony optimization. What results is a flexible method that can cope with both quantitative and qualitative information, and can easily be adapted to the specific needs of specific applications. Experiments with geographic data demonstrate the potential of the approach.

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Defining and Delineating the Central Areas of Towns for Statistical Monitoring Using Continuous Surface Representations

Cited by 128 publications

References 8 publications

“Voting with Their Feet”: Delineating the Sphere of Influence Using Social Media Data

“Voting with Their Feet”: Delineating the Sphere of Influence Using Social Media Data

Optimal Intra-Urban Hierarchy of Activity Centers—A Minimized Household Travel Energy Consumption Approach

Generating approximate region boundaries from heterogeneous spatial information: An evolutionary approach

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