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Geographic Data Science and Sustainable Urban Developments
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Geographic Data Science and Sustainable Urban Developments

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 17727

Special Issue Editors

Dr. Marco Helbich

E-Mail Website
Guest Editor
Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB Utrecht, The Netherlands
Interests: spatial and spatiotemporal analyses; computational urban geography; GIS modeling; real estate economics; active transportation; built and natural environment; health geography
Special Issues, Collections and Topics in MDPI journals
Dr. Hichem Omrani

E-Mail Website
Guest Editor
Department of Urban Development and Mobility, LISER, Luxembourg
Interests: geocomputation; machine learning; big data; cellular models; agent based model; land use change; mobility

Special Issue Information

Dear Colleagues,

Cities are complex environments composed of housing markets, transportation systems, population dynamics, etc., challenging sustainable urban developments. While geographic information system-based urban analyses have reached certain levels of maturity to study cities, the capabilities and limitations of data-driven, geographic data science approaches to support urban theories, as well as policy- and decision-making, have not yet been adequately addressed. Recent technological progress led to increasing amounts of multi-dimensional data, inherently characterized by spatial and temporal dimensions. In this context, it is expected that cutting-edge data science methods linked to novel data sources promote deeper insights into urban processes in order to support urban environments for a more sustainable future.

To cope with these and related challenges, the objective of this Special Issue is to publish original research and review papers in order to synthesize the discussion on the application of the latest advances in geographic data science to understand urban developments, their dynamics, and sustainability, and the underlying key mechanisms. Further, this special issue aims to stimulate the development of novel algorithms to understand cities in the broadest sense in the era of Big Data. We encourage both theoretical as well as application-oriented papers dealing with these emerging issues. Our interest is in papers that cover a wide spectrum of methodological and domain-specific topics.

Assoc. Prof. Marco Helbich
Dr. Hichem Omrani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational methods
  • machine learning
  • data science
  • spatial and spatiotemporal statistics
  • housing
  • mobility
  • natural and built environments

Published Papers (5 papers)

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Research

15 pages, 4919 KiB  
Article
Spatiotemporal Change Characteristics of Nodes’ Heterogeneity in the Directed and Weighted Spatial Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China
by Jing Yang, Disheng Yi, Jingjing Liu, Yusi Liu and Jing Zhang
Sustainability 2019, 11(22), 6359; https://doi.org/10.3390/su11226359 - 12 Nov 2019
Cited by 3 | Viewed by 2063
Abstract
Spatial heterogeneity patterns in cities are an essential topic in geographic research and urban planning. This paper analyzes the spatial heterogeneity of places and reflects on the urban structure in cites based on spatial interaction networks. To begin with, we constructed 24 sequentially [...] Read more.
Spatial heterogeneity patterns in cities are an essential topic in geographic research and urban planning. This paper analyzes the spatial heterogeneity of places and reflects on the urban structure in cites based on spatial interaction networks. To begin with, we constructed 24 sequentially directed and weighted spatial interaction networks (DWNs) on the basis of points of interest (POIs) and taxi GPS data in Beijing. Then, we merged 24 sequential networks into four clusters: early morning, morning, afternoon, and evening. Next, we introduced the weighted D-core decomposition method in view of the complex network method and weighted distance in a geographic space in order to obtain the in-coreness/out-coreness of places. Finally, three indices (the entropy index, the node symmetry index, and the t-test) were used to measure the heterogeneity of places from both the strength dimension and the direction dimension. The results showed: (1) For the strength dimension, the spatiotemporal strength characteristics of the nodes in the DWN are uneven on weekdays or on the weekends, and the strength heterogeneity on weekdays is more obvious than on weekends; (2) for the direction dimension, out-flows and in-flows are different in the early morning and evening on weekends. In addition, the direction of the DWN is not obvious. The city networks present flat characteristics. This study used the weighted D-core method to identify the heterogeneity of nodes in the DWN, which has certain theoretical and practical value for the planning of urban and urban systems and the coordinated development of cities. Full article
(This article belongs to the Special Issue Geographic Data Science and Sustainable Urban Developments)
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29 pages, 4846 KiB  
Article
Inter-Metropolitan Land-Price Characteristics and Patterns in the Beijing-Tianjin-Hebei Urban Agglomeration in China
by Can Li, Yu Meng, Yingkui Li, Jingfeng Ge and Chaoran Zhao
Sustainability 2019, 11(17), 4726; https://doi.org/10.3390/su11174726 - 29 Aug 2019
Cited by 7 | Viewed by 2830
Abstract
The continuous expansion of urban areas in China has increased cohesion and synergy among cities. As a result, the land price in an urban area is not only affected by the city’s own factors, but also by its interaction with nearby cities. Understanding [...] Read more.
The continuous expansion of urban areas in China has increased cohesion and synergy among cities. As a result, the land price in an urban area is not only affected by the city’s own factors, but also by its interaction with nearby cities. Understanding the characteristics, types, and patterns of urban interaction is of critical importance in regulating the land market and promoting coordinated regional development. In this study, we integrated a gravity model with an improved Voronoi diagram model to investigate the gravitational characteristics, types of action, gravitational patterns, and problems of land market development in the Beijing-Tianjin-Hebei urban agglomeration region based on social, economic, transportation, and comprehensive land-price data from 2017. The results showed that the gravitational value of land prices for Beijing, Tianjin, Langfang, and Tangshan cities (11.24–63.35) is significantly higher than that for other cities (0–6.09). The gravitational structures are closely connected for cities around Beijing and Tianjin, but loosely connected for peripheral cities. Further, various types of radiation, conduction, and convection actions exist in relation to urban land prices. In terms of gravitational patterns, the range of influence of land prices is not limited to the administrative boundaries of each city. Five clusters of urban land prices can be identified based on the gravitational structure. The land-price gravity value of the city cluster around Beijing accounted for 66.4% of the total. The polarizing effect of land-price levels and influence is clearly evident in Beijing and Tianjin, while a lock-in effect is evident in Xingtai and Handan in the south of the region. Full article
(This article belongs to the Special Issue Geographic Data Science and Sustainable Urban Developments)
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33 pages, 1808 KiB  
Article
Misrecognition in a Sustainability Capital: Race, Representation, and Transportation Survey Response Rates in the Portland Metropolitan Area
by Raoul S. Liévanos, Amy Lubitow and Julius Alexander McGee
Sustainability 2019, 11(16), 4336; https://doi.org/10.3390/su11164336 - 11 Aug 2019
Cited by 2 | Viewed by 4412
Abstract
US household transportation surveys typically have limited coverage of and responses from people of color (POC), which may lead to inaccurate estimation of POC transportation access and behavior. We recast this technocratic understanding of representativeness as a problem of “racial misrecognition” in which [...] Read more.
US household transportation surveys typically have limited coverage of and responses from people of color (POC), which may lead to inaccurate estimation of POC transportation access and behavior. We recast this technocratic understanding of representativeness as a problem of “racial misrecognition” in which racial group difference is obscured yet foundational for distributive transportation inequities and unsustainability. We linked 2008–2012 population and housing data to an apparent stratified random sample of 6107 household responses to the 2011 Oregon Household Activity Survey (OHAS) in a “sustainability capital”: the Portland, Oregon metropolitan area. We detailed how the 2011 OHAS consistently overrepresented White households and underrepresented Latinx/Nonwhite households in aggregate and at the tract-level. We conducted tract-level spatial pattern and bivariate correlation analyses of our key variables of interest. As expected, our subsequent tract-level spatial error regression analysis demonstrated that the percent of Latinx/Nonwhite householders had a significant negative association with 2011 OHAS household response rates, net of other statistical controls. Further analyses revealed that the majority of the ten “typical” tracts that best represented the spatial error regression results and racial misrecognition in the OHAS exhibited historical and contemporary patterns of racial exclusion and socially unsustainable development in our study area. Full article
(This article belongs to the Special Issue Geographic Data Science and Sustainable Urban Developments)
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24 pages, 5743 KiB  
Article
Coupling Activity-Based Modeling and Life Cycle Assessment—A Proof-of-Concept Study on Cross-Border Commuting in Luxembourg
by Paul Baustert, Tomás Navarrete Gutiérrez, Thomas Gibon, Laurent Chion, Tai-Yu Ma, Gabriel Leite Mariante, Sylvain Klein, Philippe Gerber and Enrico Benetto
Sustainability 2019, 11(15), 4067; https://doi.org/10.3390/su11154067 - 27 Jul 2019
Cited by 9 | Viewed by 3237
Abstract
According to the Intergovernmental Panel on Climate Change (IPCC), in 2010 the transport sector was responsible for 23% of the total energy-related CO2 emissions (6.7 GtCO2) worldwide. Policy makers in Luxembourg are well-aware of the challenges and are setting ambitious [...] Read more.
According to the Intergovernmental Panel on Climate Change (IPCC), in 2010 the transport sector was responsible for 23% of the total energy-related CO2 emissions (6.7 GtCO2) worldwide. Policy makers in Luxembourg are well-aware of the challenges and are setting ambitious objectives at country level for the mid and long term. However, a framework to assess environmental impacts from a life cycle perspective on the scale of transport policy scenarios, rather than individual vehicles, is lacking. We present a novel framework linking activity-based modeling with life cycle assessment (LCA) and a proof-of-concept case study for the French cross-border commuters working in Luxembourg. Our framework allows for the evaluation of specific policies formulated on the trip level as well as aggregated evaluation of environmental impacts from a life cycle perspective. The results of our proof-of-concept-based case study suggest that only a combination of: (1) policy measures improving the speed and coverage of the public transport system; (2) policy measures fostering electric mobility; and (3) external factors such as de-carbonizing the electricity mix will allow to counteract the expected increase in impacts due to the increase of mobility needs of the growing commuting population in the long term. Full article
(This article belongs to the Special Issue Geographic Data Science and Sustainable Urban Developments)
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15 pages, 2841 KiB  
Article
Spatial Justice of a Chinese Metropolis: A Perspective on Housing Price-to-Income Ratios in Nanjing, China
by Shanggang Yin, Zhifei Ma, Weixuan Song and Chunhui Liu
Sustainability 2019, 11(6), 1808; https://doi.org/10.3390/su11061808 - 26 Mar 2019
Cited by 12 | Viewed by 4607
Abstract
The housing price-to-income ratio is an important index for measuring the health of real estate, as well as detecting residents’ housing affordability and regional spatial justice. This paper considers 1833 residential districts in one main urban area and three secondary urban areas in [...] Read more.
The housing price-to-income ratio is an important index for measuring the health of real estate, as well as detecting residents’ housing affordability and regional spatial justice. This paper considers 1833 residential districts in one main urban area and three secondary urban areas in Nanjing during the period 2009–2017 as research units. It also simulates and estimates the spatial distribution of the housing price-to-income ratio with the kriging interpolation method of geographic information system (GIS) geostatistical analysis and constructs a housing spatial justice model by using housing price, income, and housing price-to-income ratio. The research results prove that in the one main urban area and the three secondary urban areas considered, the housing price-to-income ratio tended on the whole to rise, presenting a core edge model of a progressive decrease from the Main Urban Area to the secondary urban areas spatially, with high-value areas centered around famous school districts and new town centers. The housing spatial justice degree presented a trend opposite to that of the housing price-to-income ratio pattern; it progressively decreased from the secondary urban areas to the Main Urban Area. Furthermore, the spatial justice degree tended to decrease in the new towns, in the periphery of the Main Urban Area, and in the secondary urban areas, and it tended to rise, relatively, in the inner urban areas. The enhancement of the housing price-to-income ratio has caused the urban housing spatial justice degree to become gradually imbalanced, gradually squeezing out the poor and vulnerable groups to urban fringe areas and leading to a phenomenon of middle class stratification. This has thus aroused social problems such as housing differentiation and class solidification, etc., and has caused inequality in social spaces. Tt is therefore urgently necessary to reflect on urban space production with the value and principle of spatial justice, which is also the only way to obtain urban sustainable development, in mind. Full article
(This article belongs to the Special Issue Geographic Data Science and Sustainable Urban Developments)
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