Special Issue "Geospatial Methods in Social and Behavioral Sciences"

Special Issue Editor

Prof. Dr. Mei-Po Kwan
grade Website
Guest Editor

Special Issue Information

Dear Colleagues,

Geospatial methods have been used in social and behavioral science research to examine a wide range of issues (e.g., residential segregation, commuting behavior, active transportation, accessibility to urban facilities, spatial mismatch between jobs and housing, spatial patterns of crime, activity–travel behavior, spatial inequality in health behaviors and outcomes, political redistricting, substance use behavior, natural disasters, and so on). However, recent advances in and widespread use of geospatial technologies for collecting and analyzing high-resolution space–time data (e.g., real-time sensing, GPS tracking, and LiDAR) provide many opportunities to bring forth new insights to these issues. Further, recent studies have also used geospatial methods (e.g., GeoAI and geovisualization) that go beyond the conventional spatial framework of fixed areal units (e.g., census tracts) and the static temporal framework to examine many issues in social and behavioral science research.

This Special Issue aims to showcase studies that use new geospatial approaches, methods, and data to yield new insights into a wide range of social and behavioral science issues. These studies include but are not limited to works on: the development and application of new analytical frameworks, approaches, and methods; the collection and analysis of individual-level data with geospatial technologies; the development of innovative methods for analyzing complex spatiotemporal data; and the examination of how dynamic geographic contexts influence individuals’  behaviors and social phenomena.  

Prof. Dr. Mei-Po Kwan
Guest Editor

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 papers will be 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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1000 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

  • Social and behavioral sciences
  • Geospatial methods
  • Human mobility
  • Real-time sensing
  • GPS tracking
  • GeoAI
  • Geographic context
  • Environmental exposures

Published Papers (5 papers)

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Research

Open AccessArticle
Hierarchical Behavior Model for Multi-Agent System with Evasion Capabilities and Dynamic Memory
ISPRS Int. J. Geo-Inf. 2020, 9(4), 279; https://doi.org/10.3390/ijgi9040279 - 23 Apr 2020
Abstract
The behavior of an agent may be simple or complex depending on its role. Behavioral simulation using agents can have multiple approaches that have different advantages and disadvantages. By combining different behaviors in a hierarchical model, situational inefficiencies can be compensated. This paper [...] Read more.
The behavior of an agent may be simple or complex depending on its role. Behavioral simulation using agents can have multiple approaches that have different advantages and disadvantages. By combining different behaviors in a hierarchical model, situational inefficiencies can be compensated. This paper proposes a behavioral hierarchy model that combines different mechanisms in behavior plans. The study simulates the social behavior in an office environment during an emergency using collision avoidance, negotiation, conflict solution, and path-planning mechanisms in the same multi-agent model to find their effects and the efficiency of the combinational setups. Independent agents were designed to have memory expansion, pathfinding, and searching capabilities, and the ability to exchange information among themselves and perform evasive actions to find a way out of congestion and conflict. The designed model allows us to modify the behavioral hierarchy and action order of agents during evacuation scenarios. Moreover, each agent behavior can be enabled or disabled separately. The effects of these capabilities on escape performance were measured in terms of time required for evacuation and evacuation ratio. Test results prove that all mechanisms in the proposed model have characteristics that fit each other well in situations where different hierarchies are needed. Dynamic memory management (DMM), together with a hierarchical behavior plan, achieved a performance improvement of 23.14% in escape time without providing agents with any initial environmental information. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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Open AccessArticle
Where Urban Youth Work and Live: A Data-Driven Approach to Identify Urban Functional Areas at a Fine Scale
ISPRS Int. J. Geo-Inf. 2020, 9(1), 42; https://doi.org/10.3390/ijgi9010042 - 14 Jan 2020
Cited by 2
Abstract
As a major labor force of cities, young people provide a huge driving force for urban innovation and development, and contribute to urban industrial upgrading and restructuring. In addition, with the acceleration of urbanization in China, the young floating population has increased rapidly, [...] Read more.
As a major labor force of cities, young people provide a huge driving force for urban innovation and development, and contribute to urban industrial upgrading and restructuring. In addition, with the acceleration of urbanization in China, the young floating population has increased rapidly, causing over-urbanization and creating certain social problems. It is important to analyze the demand of urban youth and promote their social integration. With the development of the mobile Internet and the improvement of the city express system, ordering food delivery has become a popular and convenient way to dine, especially in China. Food delivery data have a significant user attribute where the ages of most delivery customers are under 35 years old. In this paper, we introduce food delivery data as a new data source in urban functional zone detection and propose a time-series-based clustering approach to discover the urban hotspot areas of young people. The work and living areas were effectively identified according to the human behavioral characteristics of ordering food delivery. Furthermore, we analyzed the relationship between young people and the industry structure of Hangzhou and discovered that the geographical distribution of the identified work areas was similar to that of the Internet and e-commerce companies. The characteristics of the identified living areas were also analyzed in combination with the distribution of subway lines and residential communities, and it was found that the living areas were mainly distributed along subway lines and that urban villages appeared in the living hotspot regions, indicating that transportation and living cost were two important factors in the choice of residential location for young people. The findings of this paper can help urban industrial and residential planning and young population management. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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Open AccessArticle
Mapping Creative Industries: A Case Study on Supporting Geographical Information Systems in the Olomouc Region, Czech Republic
ISPRS Int. J. Geo-Inf. 2019, 8(12), 524; https://doi.org/10.3390/ijgi8120524 - 25 Nov 2019
Cited by 1
Abstract
The article presents an interdisciplinary link between the geospatial and the cultural sector. This is a unique study of Central Europe in visualizing and interpreting the spatial location of elements in cultural and creative industries. The main purpose was to create suitable visualizations [...] Read more.
The article presents an interdisciplinary link between the geospatial and the cultural sector. This is a unique study of Central Europe in visualizing and interpreting the spatial location of elements in cultural and creative industries. The main purpose was to create suitable visualizations and to process the spatial aspects of cultural and creative industries in a cartographical environment. A team of professionals from several fields (geoinformatics, economics, culture, social sciences, cartography) was assembled to map the creative industries in Olomouc Region, Czech Republic. A total of 1,211 subjects were identified which created the conditions for the employment of more than 5,000 people. Their turnover exceeds EUR 190,000,000 annually. This study was based on an initially examined dataset. Seven spatial analyses were applied. Thirty analogue maps and one interactive map application were created. The point character map was the most used one. The price map, as a background layer, was considered very useful for further map reading. The essential phenomena were topics of population density and transport. Based on the generated map outputs, we found that subjects had a tendency to concentrate in the city center or in areas with higher prices and service levels. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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Open AccessArticle
Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival
ISPRS Int. J. Geo-Inf. 2019, 8(10), 445; https://doi.org/10.3390/ijgi8100445 - 10 Oct 2019
Cited by 3
Abstract
Public transport plays an important role in developing sustainable cities. A better understanding of how different public transit modes (bus, metro, and taxi) interact with each other will provide better sustainable strategies to transport and urban planners. However, most existing studies are either [...] Read more.
Public transport plays an important role in developing sustainable cities. A better understanding of how different public transit modes (bus, metro, and taxi) interact with each other will provide better sustainable strategies to transport and urban planners. However, most existing studies are either limited to small-scale surveys or focused on the identification of general interaction patterns during times of regular traffic. Transient demographic changes in a city (i.e., many people moving out and in) can lead to significant changes in such interaction patterns and provide a useful context for better investigating the changes in these patterns. Despite that, little has been done to explore how such interaction patterns change and how they are linked to the built environment from the perspective of transient demographic changes using urban big data. In this paper, the tap-in-tap-out smart card data of bus/metro and taxi GPS trajectory data before and after the Chinese Spring Festival in Shenzhen, China, are used to explore such interaction patterns. A time-series clustering method and an elasticity change index (ECI) are adopted to detect the changing transit mode patterns and the underlying dynamics. The findings indicate that the interactions between different transit modes vary over space and time and are competitive or complementary in different parts of the city. Both ordinary least-squares (OLS) and geographically weighted regression (GWR) models with built environment variables are used to reveal the impact of changes in different transit modes on ECIs and their linkage with the built environment. The results of this study will contribute to the planning and design of multi-modal transport services. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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Open AccessArticle
A Multi-Dimensional Analysis of El Niño on Twitter: Spatial, Social, Temporal, and Semantic Perspectives
ISPRS Int. J. Geo-Inf. 2019, 8(10), 436; https://doi.org/10.3390/ijgi8100436 - 04 Oct 2019
Abstract
Social media platforms have become a critical virtual community where people share information and discuss issues. Their capabilities for fast dissemination and massive participation have placed under scrutiny the way in which they influence people’s perceptions over time and space. This paper investigates [...] Read more.
Social media platforms have become a critical virtual community where people share information and discuss issues. Their capabilities for fast dissemination and massive participation have placed under scrutiny the way in which they influence people’s perceptions over time and space. This paper investigates how El Niño, an extreme recurring weather phenomenon, was discussed on Twitter in the United States from December 2015 to January 2016. A multiple-dimensional analysis, including spatial, social, temporal, and semantic perspectives, is conducted to comprehensively understand Twitter users’ discussion of such weather phenomenon. We argue that such multi-dimensional analysis can reveal complicated patterns of Twitter users’ online discussion and answers questions that cannot be addressed with a single-dimension analysis. For example, a significant increase in tweets about El Niño was noted when a series of rainstorms inundated California in January 2016. Some discussions on natural disasters were influenced by their geographical distances to the disasters and the prevailing geopolitical environment. The popular tweets generally discussing El Niño were overall negative, while tweets talking about how to prepare for the California rainstorms were more positive. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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