Using Exploratory Spatial Analysis to Understand the Patterns of Adolescents’ Active Transport to School and Contributory Factors
Active transport to school (ATS) is a convenient way for adolescents to reach their recommended daily physical activity levels. Most previous ATS research examined the factors that promote or hinder ATS, but this research has been of a global (i.e., non-spatial), statistical nature. Geographical Inf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/8/495 |
| _version_ | 1797523734002663424 |
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| author | Long Chen Antoni B. Moore Sandra Mandic |
| author_facet | Long Chen Antoni B. Moore Sandra Mandic |
| author_sort | Long Chen |
| collection | DOAJ |
| description | Active transport to school (ATS) is a convenient way for adolescents to reach their recommended daily physical activity levels. Most previous ATS research examined the factors that promote or hinder ATS, but this research has been of a global (i.e., non-spatial), statistical nature. Geographical Information Science (GIS) is widely applied in analysing human activities, focusing on local spatial phenomena, such as distribution, autocorrelation, and co-association. This study, therefore, applied exploratory spatial analysis methods to ATS and its factors. Kernel Density Estimation (KDE) was used to derive maps of transport mode and ATS factor distribution patterns. The results of KDE were compared to and verified by Local Indicators of Spatial Association (LISA) outputs. The data used in this study was collected from 12 high schools, including 425 adolescents who lived within walkable distance and used ATS or MTS in Dunedin New Zealand. This study identified clusters and spatial autocorrelation, confirming that the adolescents living in the south of the city, who were female, attended girls-only schools, lived in more deprived neighbourhoods, and lived in neighbourhoods with higher intersection density and residential density used more ATS. On the other hand, adolescents who were male, attended boys-only schools, lived in less deprived neighbourhoods, had more vehicles at home, and lived in neighbourhoods with medium level intersection density and residential density used more ATS in the northwest of the city as well as some part of the city centre and southeast of the city. The co-association between spatial patterns of the ATS factors and the ATS usages that this study detected adds to the evidence for autocorrelation underpinning ATS users across the study area. |
| first_indexed | 2024-03-10T08:46:18Z |
| format | Article |
| id | doaj.art-de7688f3ff8c46efab03f6a604055527 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T08:46:18Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-de7688f3ff8c46efab03f6a6040555272023-11-22T07:52:45ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-07-0110849510.3390/ijgi10080495Using Exploratory Spatial Analysis to Understand the Patterns of Adolescents’ Active Transport to School and Contributory FactorsLong Chen0Antoni B. Moore1Sandra Mandic2School of Surveying, University of Otago, Dunedin 9054, New ZealandSchool of Surveying, University of Otago, Dunedin 9054, New ZealandFaculty of Health and Environmental Sciences, School of Sport and Recreation, Auckland University of Technology, Auckland 1142, New ZealandActive transport to school (ATS) is a convenient way for adolescents to reach their recommended daily physical activity levels. Most previous ATS research examined the factors that promote or hinder ATS, but this research has been of a global (i.e., non-spatial), statistical nature. Geographical Information Science (GIS) is widely applied in analysing human activities, focusing on local spatial phenomena, such as distribution, autocorrelation, and co-association. This study, therefore, applied exploratory spatial analysis methods to ATS and its factors. Kernel Density Estimation (KDE) was used to derive maps of transport mode and ATS factor distribution patterns. The results of KDE were compared to and verified by Local Indicators of Spatial Association (LISA) outputs. The data used in this study was collected from 12 high schools, including 425 adolescents who lived within walkable distance and used ATS or MTS in Dunedin New Zealand. This study identified clusters and spatial autocorrelation, confirming that the adolescents living in the south of the city, who were female, attended girls-only schools, lived in more deprived neighbourhoods, and lived in neighbourhoods with higher intersection density and residential density used more ATS. On the other hand, adolescents who were male, attended boys-only schools, lived in less deprived neighbourhoods, had more vehicles at home, and lived in neighbourhoods with medium level intersection density and residential density used more ATS in the northwest of the city as well as some part of the city centre and southeast of the city. The co-association between spatial patterns of the ATS factors and the ATS usages that this study detected adds to the evidence for autocorrelation underpinning ATS users across the study area.https://www.mdpi.com/2220-9964/10/8/495active transportschoolspatial analysiskernel density estimationlocal indicators of spatial associationdistance |
| spellingShingle | Long Chen Antoni B. Moore Sandra Mandic Using Exploratory Spatial Analysis to Understand the Patterns of Adolescents’ Active Transport to School and Contributory Factors ISPRS International Journal of Geo-Information active transport school spatial analysis kernel density estimation local indicators of spatial association distance |
| title | Using Exploratory Spatial Analysis to Understand the Patterns of Adolescents’ Active Transport to School and Contributory Factors |
| title_full | Using Exploratory Spatial Analysis to Understand the Patterns of Adolescents’ Active Transport to School and Contributory Factors |
| title_fullStr | Using Exploratory Spatial Analysis to Understand the Patterns of Adolescents’ Active Transport to School and Contributory Factors |
| title_full_unstemmed | Using Exploratory Spatial Analysis to Understand the Patterns of Adolescents’ Active Transport to School and Contributory Factors |
| title_short | Using Exploratory Spatial Analysis to Understand the Patterns of Adolescents’ Active Transport to School and Contributory Factors |
| title_sort | using exploratory spatial analysis to understand the patterns of adolescents active transport to school and contributory factors |
| topic | active transport school spatial analysis kernel density estimation local indicators of spatial association distance |
| url | https://www.mdpi.com/2220-9964/10/8/495 |
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