Functional Structure of the Tokyo Metropolitan Area Based on the Analysis of Commuting and Consuming Activities

Komaki, Nobuhiko

doi:10.5996/newgeo.52.1

Cited by 6 publications

(7 citation statements)

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“…Expert survey methods and remote sensing classification methods have high accuracy in identifying urban single functional areas [21][22][23][24]. Still, there are uncertainties in the identification of urban mixed functional areas.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have formed multidimensional classification standards and rules by applying the differences in data sources, research methods, and purposes [20,21]. Traditional urban functional area identification is mainly based on statistical investigation techniques [22,23], expert inspection and evaluation, and remote sensing land use extraction [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…ese methods have played a significant role in early urban planning and industrial layout [5,8,10,22,23]. With the advent of the informatization big data era, multisource emerging data such as mobile phone signaling, taxi trajectory, check-in data, and POI (points of interests) have promoted quantitative research urban functional area identification further [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Urban Functional Area by Using Multisource Geographic Data: A Case Study of Zhengzhou, China

Xie

Zhao

et al. 2021

Complexity

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The rational allocation of functional areas is the foundation for addressing the sustainable development of cities. Efficient and accurate identification methods of urban functional areas are of great significance to the adjustment and testing of urban planning and industrial layout optimization. Firstly, by employing multisource geographic data, an identification method of urban functional areas was developed. A quantitative measurement approach of the urban functional area was then established considering the comprehensive effects of human-land, space-time, and thematic information to present the covering area of ground objects, public awareness, and empirical research. Finally, the Zhengzhou city, which locates in Henan province of central China, was used to test the method. The results show that the developed method is efficient, accurate, and universal and can identify urban functional areas quickly and accurately. We found that the overall distribution of Zhengzhou’s functional areas presents a spatial pattern of single and multimixed coordinated development. The city’s commercial functional areas and commercial-based mixed functional areas are located in the city’s central area. The green square’s function area occupies relatively low and is mainly distributed in the city’s fringe.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Urban Functional Area by Using Multisource Geographic Data: A Case Study of Zhengzhou, China

Xie

Zhao

et al. 2021

Complexity

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“…Our laboratory is authorized to use this data by the Center for Spatial Information Science, University of Tokyo (CSIS Tokyo). Many previous studies by Japanese scholars had proved the academic value of this PTS data [8,9]. Regardless, PTS can be regarded as an excellent geo-tagged big data appropriate for this study.…”

Section: Technical Difficulty On Geo-tagged Big Data and The Applicatmentioning

confidence: 94%

Spatiotemporal Analysis of Human Mobility in Manila Metropolitan Area with Person-Trip Data

Liu

2018

Urban Science

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Abstract:The metropolitan area can be regarded as a multi-functional structure consisting of plural coordinated urban nucleuses. This study aims to clarify the characteristics of urban nucleuses and a spatiotemporal pattern of human mobility in the Manila metropolitan area. Hourly density of human mobility from 00:00 to 24:00 in the whole study area is quantitatively studied. Urban nucleuses with six types: central city, business city, commuter town, south suburb, north suburb, and subcenter city, are identified. Differences of human mobility owing to different human behaviors or properties are also analyzed in 10 typical areas with different urban functions. Results prove that pattern of human mobility in each area depends on its human social division, population composition, infrastructure condition, and functional structure. This study provides an effective thinking on handling geo-tagged big data supported by MATLAB programming and GIS technology. Moreover, spatiotemporal analysis of human mobility also possesses a meaningful academic value for transport geography.

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“…This finding supports crime pattern theory, but it is unclear what types of noncriminal trips or daily mobility of the general population can explain criminal trips more clearly. Komaki [41] analyzed data on Japanese noncriminal trips (i.e., daily trips) and found that the patterns of daily spatial movements differed between daily commuting trips and daily consuming (leisure) trips. Using data from a person trip (PT) survey, Komaki [41] investigated the functional structure of the Tokyo metropolitan area and showed that consuming (leisure) trips tended to be dispersed in the study area because the suburbs contained several small-scale consumer areas.…”

Section: Daily Tripsmentioning

confidence: 99%

Investigating Factors Related to Criminal Trips of Residential Burglars Using Spatial Interaction Modeling

Hirama

Yokota

Otsuka

et al. 2022

IJGI

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This study used spatial interaction modeling to examine whether origin-specific and destination-specific factors, distance decay effects, and spatial structures explain the criminal trips of residential burglars. In total, 4041 criminal trips committed by 892 individual offenders who lived and committed residential burglary in Tokyo were analyzed. Each criminal trip was allocated to an origin–destination pair created from the combination of potential departure and arrival zones. The following explanatory variables were created from an external dataset and used: residential population, density of residential burglaries, and mobility patterns of the general population. The origin-specific factors served as indices of not only the production of criminal trips, but also the opportunity to commit crimes in the origin zones. Moreover, the criminal trips were related to the mobility patterns of the general population representing daily leisure (noncriminal) trips, and relatively large origin- and destination-based spatial spillover effects were estimated. It was shown that considering not only destination-specific but also origin-specific factors, spatial structures are important for investigating the criminal trips of residential burglars. The current findings could be applicable to future research on geographical profiling by incorporating neighborhood-level factors into existing models.

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Functional Structure of the Tokyo Metropolitan Area Based on the Analysis of Commuting and Consuming Activities

Cited by 6 publications

References 0 publications

Identification of Urban Functional Area by Using Multisource Geographic Data: A Case Study of Zhengzhou, China

Identification of Urban Functional Area by Using Multisource Geographic Data: A Case Study of Zhengzhou, China

Spatiotemporal Analysis of Human Mobility in Manila Metropolitan Area with Person-Trip Data

Investigating Factors Related to Criminal Trips of Residential Burglars Using Spatial Interaction Modeling

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