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Geospatial Methods in Social and Behavioral Sciences
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Geospatial Methods in Social and Behavioral Sciences

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 74267

Special Issue Editor

Prof. Dr. Mei-Po Kwan

grade E-Mail Website
Guest Editor
Institute of Space and Earth Information Science & Department of Geography and Resource Management, The Chinese University of Hong Kong, New Territories, Sha Tin, Hong Kong
Interests: environmental health; human mobility; healthy cities; social justice; GIScience
Special Issues, Collections and Topics in MDPI journals

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

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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 1700 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

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Published Papers (17 papers)

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17 pages, 5513 KiB  
Article
The Influence of Spatial Grid Division on the Layout Analysis of Urban Functional Areas
by Shaohua Luo, Yang Liu, Mingyi Du, Siyan Gao, Pengfei Wang and Xiaoyu Liu
ISPRS Int. J. Geo-Inf. 2021, 10(3), 189; https://doi.org/10.3390/ijgi10030189 - 22 Mar 2021
Cited by 22 | Viewed by 4207
Abstract
The identification of urban functional areas is essential for urban planning and sustainable development. Spatial grids are the basic units for the implementation of urban plans and management by cities or development zones. The emergence of internet “big data” provides new ideas for [...] Read more.
The identification of urban functional areas is essential for urban planning and sustainable development. Spatial grids are the basic units for the implementation of urban plans and management by cities or development zones. The emergence of internet “big data” provides new ideas for the identification of urban functional areas. Based on point of interest (POI) data from Baidu Maps, the Xicheng District of Beijing was divided into grids with side lengths of 200, 500, and 1000 m in this study. The kernel density method was used to analyze the spatial structure of POI data. Two indicators, that is, the frequency density and category ratio, were then used to identify single- and mixed-functional areas. The results show that (1) commercial and financial areas are concentrated in the city center and multiple business centers have not developed; (2) scenic areas account for the largest proportion of single-functional areas in the Xicheng District of Beijing, followed by education and training, residence, and party and government organizations areas; and (3) the 200 × 200 m and 500 × 500 m grids are the most suitable for the identification of single- and mixed-functional areas, respectively. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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15 pages, 6948 KiB  
Article
Interpersonal and Intrapersonal Variabilities in Daily Activity-Travel Patterns: A Networked Spatiotemporal Analysis
by Wenjia Zhang, Chunhan Ji, Hao Yu, Yi Zhao and Yanwei Chai
ISPRS Int. J. Geo-Inf. 2021, 10(3), 148; https://doi.org/10.3390/ijgi10030148 - 8 Mar 2021
Cited by 11 | Viewed by 3382
Abstract
Interpersonal and intrapersonal variabilities are two important perspectives to understand daily travel behaviors, while only a small number of studies incorporate them for understanding human dynamics. This paper employed a network analysis approach to detecting daily activity-travel patterns of 680 Beijing’s residents within [...] Read more.
Interpersonal and intrapersonal variabilities are two important perspectives to understand daily travel behaviors, while only a small number of studies incorporate them for understanding human dynamics. This paper employed a network analysis approach to detecting daily activity-travel patterns of 680 Beijing’s residents within a week and then used a multilevel multinomial logit model to analyze the intrapersonal variability in patterns and the socioeconomic linkages behind them. Results suggest that most activity-travel patterns have significant day-to-day intrapersonal and interpersonal variabilities. This suggests that the application of a typical day of activity-travel behaviors to measure and represent a week’s or even longer-term behaviors may be biased, due to the existence of day-to-day intrapersonal variability. This study also provides a hint for the selection of days of a week to conduct a diary survey for activity pattern mining or travel demand modeling. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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21 pages, 2574 KiB  
Article
COVID-19 Infection and Mortality: Association with PM2.5 Concentration and Population Density—An Exploratory Study
by Xinyu Yu, Man Sing Wong, Mei Po Kwan, Janet Elizabeth Nichol, Rui Zhu, Joon Heo, Pak Wai Chan, David C. W. Chin, Coco Yin Tung Kwok and Zihan Kan
ISPRS Int. J. Geo-Inf. 2021, 10(3), 123; https://doi.org/10.3390/ijgi10030123 - 1 Mar 2021
Cited by 16 | Viewed by 4359
Abstract
The novel coronavirus disease (COVID-19) has become a public health problem at a global scale because of its high infection and mortality rate. It has affected most countries in the world, and the number of confirmed cases and death toll is still growing [...] Read more.
The novel coronavirus disease (COVID-19) has become a public health problem at a global scale because of its high infection and mortality rate. It has affected most countries in the world, and the number of confirmed cases and death toll is still growing rapidly. Susceptibility studies have been conducted in specific countries, where COVID-19 infection and mortality rates were highly related to demographics and air pollution, especially PM2.5, but there are few studies on a global scale. This paper is an exploratory study of the relationship between confirmed COVID-19 cases and death toll per million population, population density, and PM2.5 concentration on a worldwide basis. A multivariate linear regression based on Moran eigenvector spatial filtering model and Geographically weighted regression model were undertaken to analyze the relationship between population density, PM2.5 concentration, and COVID-19 infection and mortality rate, and a geostatistical method with bivariate local spatial association analysis was adopted to explore their spatial correlations. The results show that there is a statistically significant positive relationship between COVID-19 confirmed cases and death toll per million population, population density, and PM2.5 concentration, but the relationship displays obvious spatial heterogeneity. While some adjacent countries are likely to have similar characteristics, it suggests that the countries with close contacts/sharing borders and similar spatial pattern of population density and PM2.5 concentration tend to have similar patterns of COVID-19 risk. The analysis provides an interpretation of the statistical and spatial association of COVID-19 with population density and PM2.5 concentration, which has implications for the control and abatement of COVID-19 in terms of both infection and mortality. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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17 pages, 3758 KiB  
Article
A Perception Model for Optimizing and Evaluating Evacuation Guidance Systems
by Lin Huang, Jianhua Gong and Wenhang Li
ISPRS Int. J. Geo-Inf. 2021, 10(2), 54; https://doi.org/10.3390/ijgi10020054 - 28 Jan 2021
Cited by 12 | Viewed by 2862
Abstract
To overcome the difficulties of quantitatively optimizing and evaluating evacuation guidance systems, we proposed a perception model based on virtual reality (VR) experiments and the social force model (SFM). We used VR and eye tracking devices to carry out experiments. The VR experiment [...] Read more.
To overcome the difficulties of quantitatively optimizing and evaluating evacuation guidance systems, we proposed a perception model based on virtual reality (VR) experiments and the social force model (SFM). We used VR and eye tracking devices to carry out experiments. The VR experiment data was mainly used for three purposes: to determine the parameter values of the perception model, to optimize the evacuation guidance system by quantitative analysis, and to validate the perception model. Additionally, we compared the VR experimental and model simulation results before and after the optimization to quantitatively assess the improvement in the optimized evacuation guidance system. The results showed that our model can effectively simulate the perception behaviors of evacuees on the evacuation guidance system and it can quantitatively evaluate different evacuation guidance system schemes. The model simulations showed that the optimized evacuation guidance system improved the evacuation efficiency, with the average escape time and distance of the two starting positions reduced by 37% and 28%, respectively. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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18 pages, 2865 KiB  
Article
Visitor Flows at a Large-Scale Cultural Event: GPS Tracking at Dutch Design Week
by Gamze Dane, Aloys Borgers, Deniz Ikiz Kaya and Tao Feng
ISPRS Int. J. Geo-Inf. 2020, 9(11), 661; https://doi.org/10.3390/ijgi9110661 - 3 Nov 2020
Cited by 8 | Viewed by 3476
Abstract
Large-scale cultural events bring many economic, social, and cultural benefits to the hosting cities. Although event producers aim to satisfy the visitors’ needs, they do not usually receive feedback on visitors’ experiences. Moreover, lack of spatial dispersal of visitors might result in less [...] Read more.
Large-scale cultural events bring many economic, social, and cultural benefits to the hosting cities. Although event producers aim to satisfy the visitors’ needs, they do not usually receive feedback on visitors’ experiences. Moreover, lack of spatial dispersal of visitors might result in less visibility for some activities and locations. An understanding of visitors’ spatial and temporal behavior and the factors influencing visitors’ intra-event destination choices is key to efficient and successful event management and future planning. In this article, we examine the relationship between visitors’ spatial and temporal behavior, the spatial structure of the host city, and visitor characteristics. In order to do this, data are collected from 281 event visitors by means of GPS tracking and paper surveys at the Dutch Design Week (DDW) 2017 event in Eindhoven, the Netherlands. Data are used to understand the area of interest locations, visitor flows, visitor clusters and area of interest choices by applying data processing, network analysis, cluster analysis and bivariate analysis. The results show that one of the three dedicated event areas was considerably less popular by the DDW visitors. Moreover, the choice of intra-event destination locations and areas depended mainly on temporal constraints of the visitors. The findings of this study can inform future event planning and management policies in hosting cities. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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22 pages, 5099 KiB  
Article
Exploring Travel Patterns during the Holiday Season—A Case Study of Shenzhen Metro System During the Chinese Spring Festival
by Jianxiao Liu, Wenzhong Shi and Pengfei Chen
ISPRS Int. J. Geo-Inf. 2020, 9(11), 651; https://doi.org/10.3390/ijgi9110651 - 30 Oct 2020
Cited by 18 | Viewed by 4272
Abstract
Research has shown that the growing holiday travel demand in modern society has a significant influence on daily travel patterns. However, few studies have focused on the distinctness of travel patterns during a holiday season and as a specified case, travel behavior studies [...] Read more.
Research has shown that the growing holiday travel demand in modern society has a significant influence on daily travel patterns. However, few studies have focused on the distinctness of travel patterns during a holiday season and as a specified case, travel behavior studies of the Chinese Spring Festival (CSF) at the city level are even rarer. This paper adopts a text-mining model (latent Dirichlet allocation (LDA)) to explore the travel patterns and travel purposes during the CSF season in Shenzhen based on the metro smart card data (MSC) and the points of interest (POIs) data. The study aims to answer two questions—(1) how to use MSC and POIs inferring travel purpose at the metro station level without the socioeconomic backgrounds of the cardholders? (2) What are the overall inner-city mobility patterns and travel activities during the Spring Festival holiday-week? The results show that six features of the CSF travel behavior are found and nine (three broad categories) travel patterns and trip activities are inferred. The activities in which travelers engaged during the CSF season are mainly consumption-oriented events, visiting relatives and friends and traffic-oriented events. This study is beneficial to metro corporations (timetable management), business owners (promotion strategy), researchers (travelers’ social attribute inference) and decision-makers (examine public service). Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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28 pages, 6386 KiB  
Article
Using Flickr Geotagged Photos to Estimate Visitor Trajectories in World Heritage Cities
by Antoni Domènech, Inmaculada Mohino and Borja Moya-Gómez
ISPRS Int. J. Geo-Inf. 2020, 9(11), 646; https://doi.org/10.3390/ijgi9110646 - 29 Oct 2020
Cited by 21 | Viewed by 6441
Abstract
World tourism dynamics are in constant change, as well as they are deeply shaping the trajectories of cities. The “call effect” for having the World Heritage status has boosted tourism in many cities. The large number of visitors and the side effects, such [...] Read more.
World tourism dynamics are in constant change, as well as they are deeply shaping the trajectories of cities. The “call effect” for having the World Heritage status has boosted tourism in many cities. The large number of visitors and the side effects, such as the overcrowding of central spaces, are arousing the need to develop and protect heritage assets. Hence, the analysis of tourist spatial behaviour is critical for tackling the needs of touristified cities correctly. In this article, individual visitor spatiotemporal trajectories are reconstructed along with the urban network using thousands of geotagged Flickr photos taken by visitors in the historic centre of the World Heritage City of Toledo (Spain). A process of trajectory reconstruction using advanced GIS techniques has been implemented. The spatial behaviour has been used to classify the tourist sites offered on the city’s official tourist map, as well as to identify the association with the land uses. Results bring new knowledge to understand visitor spatial behaviour and new visions about the influence of the urban environment and its uses on the visitor spatial behaviour. Our findings illustrate how tourist attractions and the location of mixed commercial and recreational uses shape the visitor spatial behaviour. Overflowed streets and shadow areas underexplored by visitors are pinpointed. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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20 pages, 10319 KiB  
Article
Investigating the Relationship between the Built Environment and Relative Risk of COVID-19 in Hong Kong
by Jianwei Huang, Mei-Po Kwan, Zihan Kan, Man Sing Wong, Coco Yin Tung Kwok and Xinyu Yu
ISPRS Int. J. Geo-Inf. 2020, 9(11), 624; https://doi.org/10.3390/ijgi9110624 - 25 Oct 2020
Cited by 69 | Viewed by 8995
Abstract
Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the [...] Read more.
Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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16 pages, 3644 KiB  
Article
How Urban Factors Affect the Spatiotemporal Distribution of Infectious Diseases in Addition to Intercity Population Movement in China
by Xinyi Niu, Yufeng Yue, Xingang Zhou and Xiaohu Zhang
ISPRS Int. J. Geo-Inf. 2020, 9(11), 615; https://doi.org/10.3390/ijgi9110615 - 22 Oct 2020
Cited by 24 | Viewed by 3605
Abstract
The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. During the Chinese New Year holiday, population outflow from Wuhan induced the spread of the epidemic to other cities in China. This study analyzed massive intercity movement data from Baidu and [...] Read more.
The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. During the Chinese New Year holiday, population outflow from Wuhan induced the spread of the epidemic to other cities in China. This study analyzed massive intercity movement data from Baidu and epidemic data to study how intercity population outflows affected the spatiotemporal spread of the epidemic. This study further investigated how urban factors influenced the spatiotemporal spread of COVID-19. The analysis indicates that intercity movement was an important factor in the spread of the epidemic in China, and the impact of intercity movement on the spread was heterogeneous across different classes of cities. The spread of the epidemic also varied among cities and was affected by urban factors including the total population, population density, and gross domestic product (GDP). The findings have implications for public health management. Mega-cities should consider tougher measures to contain the spread of the epidemic compared with other cities. It is of great significance for policymakers in any nation to assess the potential risk of epidemics and make cautious plans ahead of time. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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20 pages, 1967 KiB  
Article
Hierarchical Behavior Model for Multi-Agent System with Evasion Capabilities and Dynamic Memory
by Aydin Cetin and Erhan Bulbul
ISPRS Int. J. Geo-Inf. 2020, 9(4), 279; https://doi.org/10.3390/ijgi9040279 - 23 Apr 2020
Viewed by 2850
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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21 pages, 5516 KiB  
Article
Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model
by Daijun Zhang, Xiaoqi Zhang, Yanqiao Zheng, Xinyue Ye, Shengwen Li and Qiwen Dai
ISPRS Int. J. Geo-Inf. 2020, 9(1), 56; https://doi.org/10.3390/ijgi9010056 - 19 Jan 2020
Cited by 8 | Viewed by 3384
Abstract
This study analyzed the spillovers among intra-urban housing submarkets in Beijing, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is [...] Read more.
This study analyzed the spillovers among intra-urban housing submarkets in Beijing, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is impossible to evaluate the intra-urban spillover by standard time-series models. Instead, we formulated the spillover effect as a Markov chain procedure. The constrained clustering technique was applied to identify the submarkets as the hidden states of Markov chain and estimate the transition matrix. Using a day-by-day transaction dataset of second-hand apartments in Beijing during 2011–2017, we detected 16 submarkets/regions and the spillover effect among these regions. The highest transition probability appeared in the overlapped region of urban core and Tongzhou district. This observation reflects the impact of urban planning proposal initiated since early 2012. In addition to the policy consequences, we analyzed a variety of spillover “types” through regression analysis. The latter showed that the “ripple” form of spillover is not dominant at the intra-urban level. Other types, such as the spillover due to the existence of price depressed regions, play major roles. This observation reveals the complexity of intra-urban spillover dynamics and its distinct driving-force compared to the inter-urban spillover. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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20 pages, 13940 KiB  
Article
Where Urban Youth Work and Live: A Data-Driven Approach to Identify Urban Functional Areas at a Fine Scale
by Yiming Yan, Yuanyuan Wang, Zhenhong Du, Feng Zhang, Renyi Liu and Xinyue Ye
ISPRS Int. J. Geo-Inf. 2020, 9(1), 42; https://doi.org/10.3390/ijgi9010042 - 14 Jan 2020
Cited by 7 | Viewed by 3698
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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18 pages, 15585 KiB  
Article
Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore
by Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng and Bo Huang
ISPRS Int. J. Geo-Inf. 2020, 9(1), 40; https://doi.org/10.3390/ijgi9010040 - 14 Jan 2020
Cited by 23 | Viewed by 5187
Abstract
In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by [...] Read more.
In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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19 pages, 6599 KiB  
Article
Mapping Creative Industries: A Case Study on Supporting Geographical Information Systems in the Olomouc Region, Czech Republic
by Rostislav Netek, Tomas Burian and Jakub Kohn
ISPRS Int. J. Geo-Inf. 2019, 8(12), 524; https://doi.org/10.3390/ijgi8120524 - 25 Nov 2019
Cited by 4 | Viewed by 3137
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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21 pages, 4344 KiB  
Article
The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts
by Kangjae Lee and Mei-Po Kwan
ISPRS Int. J. Geo-Inf. 2019, 8(11), 514; https://doi.org/10.3390/ijgi8110514 - 13 Nov 2019
Cited by 20 | Viewed by 4494
Abstract
To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on [...] Read more.
To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on estimates of individuals’ dynamic exposures along their daily trips recorded as GPS trajectories. Thus, using a 7-day GPS dataset collected in the Chicago Regional Household Travel Inventory (CRHTI) Survey, this study addresses the methodological issue of how the associations between environmental contexts and active travel modes (ATMs) as a subset of physical activity vary with GPS-based buffer size. The results indicate that buffer size influences such associations and the significance levels of the seven environmental factors selected as predictors. Further, the findings on the effects of buffer size on such associations and the significance levels are clearly different between the ATMs of walking and biking. Such evidence of the existence of buffer-size effects for multiple environmental factors not only confirms the importance of the uncertain geographic context problem (UGCoP) but provides a resounding cautionary note to all future research on human mobility involving individuals’ GPS trajectories, including studies on physical activity and travel behaviors, especially on the reliable estimation of individual exposures to environmental factors and their health outcomes. Full article
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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18 pages, 10051 KiB  
Article
Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival
by Jianwei Huang, Xintao Liu, Pengxiang Zhao, Junwei Zhang and Mei-Po Kwan
ISPRS Int. J. Geo-Inf. 2019, 8(10), 445; https://doi.org/10.3390/ijgi8100445 - 10 Oct 2019
Cited by 16 | Viewed by 4402
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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Article
A Multi-Dimensional Analysis of El Niño on Twitter: Spatial, Social, Temporal, and Semantic Perspectives
by Xinyue Ye and Xuebin Wei
ISPRS Int. J. Geo-Inf. 2019, 8(10), 436; https://doi.org/10.3390/ijgi8100436 - 4 Oct 2019
Cited by 10 | Viewed by 3338
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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