Kiki Adhinugraha, Thanh Phan, Richard Beare, Albert Phan, Shiyang Lyu, David Taniar
{"title":"澳大利亚医疗保健道路可达性数据集:基于服务的分组和住宅可达性。","authors":"Kiki Adhinugraha, Thanh Phan, Richard Beare, Albert Phan, Shiyang Lyu, David Taniar","doi":"10.1016/j.dib.2025.112068","DOIUrl":null,"url":null,"abstract":"<p><p>This dataset provides national coverage of healthcare road accessibility in Australia, focusing on five types of facilities: four hospital groups based on clinical service availability and one group of ambulance stations. Hospitals are classified into four groups based on the number of clinical services they offer: Blue (>16 services), Green (11-15), Orange (5-10), and Red (<5). Ambulance stations are treated as a distinct group to support emergency access analysis. For each group, the dataset presents travel distance and estimated driving time from major road points, enabling a consistent and structured view of spatial access to healthcare services across the country. Road accessibility is calculated using shortest-path routing across OpenStreetMap road networks. Estimated travel times are adjusted based on road speed limits and regional driving conditions (urban, regional, or remote). Two accessibility layers are provided: (1) a comprehensive set of road nodes showing distance (in kilometres) and travel time (in hours) to the nearest hospital in each group, and (2) a set of sampled residential address points, representing population perspectives, with linked nearest-healthcare metrics for each group, and aggregated summaries by SA2, LGA, and remoteness levels. Travel time accuracy was validated against Google Maps API, with average discrepancies of 3 min in urban areas, 10 min in regional areas, and 90 min in remote areas. Summary statistics are also available at the Local Government Area (LGA), Statistical Area Level 2 (SA2), and remoteness levels, allowing high-level regional comparisons. The dataset includes vector-based geospatial files representing hospitals, ambulance stations, road nodes, and residential samples. All layers use the GDA2020 datum (EPSG:7844) and are derived from the Australian Institute of Health and Welfare (hospital services), Digital Atlas Australia (ambulance locations), OpenStreetMap (road networks), Australian Bureau of Statistics (boundaries), and Geoscape G-NAF (residential addresses). The dataset supports public health planning by illustrating which facilities are reachable by road, the estimated distance and travel time to reach them, and how hospital service capacity influences spatial accessibility.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112068"},"PeriodicalIF":1.4000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510037/pdf/","citationCount":"0","resultStr":"{\"title\":\"A dataset of healthcare road accessibility in Australia: service-based grouping and residential reachability.\",\"authors\":\"Kiki Adhinugraha, Thanh Phan, Richard Beare, Albert Phan, Shiyang Lyu, David Taniar\",\"doi\":\"10.1016/j.dib.2025.112068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This dataset provides national coverage of healthcare road accessibility in Australia, focusing on five types of facilities: four hospital groups based on clinical service availability and one group of ambulance stations. Hospitals are classified into four groups based on the number of clinical services they offer: Blue (>16 services), Green (11-15), Orange (5-10), and Red (<5). Ambulance stations are treated as a distinct group to support emergency access analysis. For each group, the dataset presents travel distance and estimated driving time from major road points, enabling a consistent and structured view of spatial access to healthcare services across the country. Road accessibility is calculated using shortest-path routing across OpenStreetMap road networks. Estimated travel times are adjusted based on road speed limits and regional driving conditions (urban, regional, or remote). Two accessibility layers are provided: (1) a comprehensive set of road nodes showing distance (in kilometres) and travel time (in hours) to the nearest hospital in each group, and (2) a set of sampled residential address points, representing population perspectives, with linked nearest-healthcare metrics for each group, and aggregated summaries by SA2, LGA, and remoteness levels. Travel time accuracy was validated against Google Maps API, with average discrepancies of 3 min in urban areas, 10 min in regional areas, and 90 min in remote areas. Summary statistics are also available at the Local Government Area (LGA), Statistical Area Level 2 (SA2), and remoteness levels, allowing high-level regional comparisons. The dataset includes vector-based geospatial files representing hospitals, ambulance stations, road nodes, and residential samples. All layers use the GDA2020 datum (EPSG:7844) and are derived from the Australian Institute of Health and Welfare (hospital services), Digital Atlas Australia (ambulance locations), OpenStreetMap (road networks), Australian Bureau of Statistics (boundaries), and Geoscape G-NAF (residential addresses). The dataset supports public health planning by illustrating which facilities are reachable by road, the estimated distance and travel time to reach them, and how hospital service capacity influences spatial accessibility.</p>\",\"PeriodicalId\":10973,\"journal\":{\"name\":\"Data in Brief\",\"volume\":\"62 \",\"pages\":\"112068\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510037/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data in Brief\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.dib.2025.112068\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/10/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.dib.2025.112068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/10/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
A dataset of healthcare road accessibility in Australia: service-based grouping and residential reachability.
This dataset provides national coverage of healthcare road accessibility in Australia, focusing on five types of facilities: four hospital groups based on clinical service availability and one group of ambulance stations. Hospitals are classified into four groups based on the number of clinical services they offer: Blue (>16 services), Green (11-15), Orange (5-10), and Red (<5). Ambulance stations are treated as a distinct group to support emergency access analysis. For each group, the dataset presents travel distance and estimated driving time from major road points, enabling a consistent and structured view of spatial access to healthcare services across the country. Road accessibility is calculated using shortest-path routing across OpenStreetMap road networks. Estimated travel times are adjusted based on road speed limits and regional driving conditions (urban, regional, or remote). Two accessibility layers are provided: (1) a comprehensive set of road nodes showing distance (in kilometres) and travel time (in hours) to the nearest hospital in each group, and (2) a set of sampled residential address points, representing population perspectives, with linked nearest-healthcare metrics for each group, and aggregated summaries by SA2, LGA, and remoteness levels. Travel time accuracy was validated against Google Maps API, with average discrepancies of 3 min in urban areas, 10 min in regional areas, and 90 min in remote areas. Summary statistics are also available at the Local Government Area (LGA), Statistical Area Level 2 (SA2), and remoteness levels, allowing high-level regional comparisons. The dataset includes vector-based geospatial files representing hospitals, ambulance stations, road nodes, and residential samples. All layers use the GDA2020 datum (EPSG:7844) and are derived from the Australian Institute of Health and Welfare (hospital services), Digital Atlas Australia (ambulance locations), OpenStreetMap (road networks), Australian Bureau of Statistics (boundaries), and Geoscape G-NAF (residential addresses). The dataset supports public health planning by illustrating which facilities are reachable by road, the estimated distance and travel time to reach them, and how hospital service capacity influences spatial accessibility.
期刊介绍:
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