Pub Date : 2023-01-01DOI: 10.1553/giscience2023_01_s34
H. C. Dias, D. Hölbling, V. C. Dias, C. Grohmann
Mass movement mapping is essential for susceptibility, vulnerability and risk assessments. Various mapping approaches based on Earth observation (EO) data have been used to identify different types of hazards. Object-based image analysis (OBIA) has been employed for EO-based landslide mapping worldwide. The development and application of efficient methods for recognition and mapping are essential to create standards for landslide inventory mapping, notably in Brazil where landslides are a frequent natural hazard. This study aims to detect landslide features and differentiate them into shallow landslides and debris flows using a semi-automated OBIA approach. RapidEye satellite images (5 m) were analysed and the Normalized Difference Vegetation Index (NDVI) was calculated. A Digital Elevation Model (DEM) (12.5 m) and its derived products were integrated into the analysis to support the OBIA landslide mapping. The results show that the method is suitable for the recognition of this type of hazard and are potentially of use for local stakeholders and decision-makers in disaster management.
{"title":"Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows","authors":"H. C. Dias, D. Hölbling, V. C. Dias, C. Grohmann","doi":"10.1553/giscience2023_01_s34","DOIUrl":"https://doi.org/10.1553/giscience2023_01_s34","url":null,"abstract":"Mass movement mapping is essential for susceptibility, vulnerability and risk assessments. Various mapping approaches based on Earth observation (EO) data have been used to identify different types of hazards. Object-based image analysis (OBIA) has been employed for EO-based landslide mapping worldwide. The development and application of efficient methods for recognition and mapping are essential to create standards for landslide inventory mapping, notably in Brazil where landslides are a frequent natural hazard. This study aims to detect landslide features and differentiate them into shallow landslides and debris flows using a semi-automated OBIA approach. RapidEye satellite images (5 m) were analysed and the Normalized Difference Vegetation Index (NDVI) was calculated. A Digital Elevation Model (DEM) (12.5 m) and its derived products were integrated into the analysis to support the OBIA landslide mapping. The results show that the method is suitable for the recognition of this type of hazard and are potentially of use for local stakeholders and decision-makers in disaster management.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83424788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1553/giscience2022_02_s18
Meng-Chin Tsai, S. van Gasselt
Land cover changes have been mapped for decades to investigate urban expansion patterns. Under the UN Sustainable Development Goals (SDGs), several indices are employed to interpret urban growth trends quantitatively and comprehensively. However, landowners and the interested public usually have limited insights into these types of information as access to data and software is limited. Static maps and the inability to access special file formats increase the difficulty of viewing and investigating the data. This contribution presents a dedicated, interactive, web-based analysis tool for integrating land cover and land use maps as well as urban expansion indices. The tool’s concept, development and functionality are presented, and its general design is reviewed based on an actual implementation case. The setup allows integrating land use and land cover (LULC) change data alongside SDG indicators. The tool’s design aims to enhance user accessibility to information on urban expansion indices and LULC. We demonstrate that such a tool can be used to help disseminate results and to improve communication with the public in the context of other use cases.
{"title":"Framework and Use Case for a Web-Based Interactive Analysis Tool to Investigate Urban Expansion and Sustainable Development Goal Indicators","authors":"Meng-Chin Tsai, S. van Gasselt","doi":"10.1553/giscience2022_02_s18","DOIUrl":"https://doi.org/10.1553/giscience2022_02_s18","url":null,"abstract":"Land cover changes have been mapped for decades to investigate urban expansion patterns. Under the UN Sustainable Development Goals (SDGs), several indices are employed to interpret urban growth trends quantitatively and comprehensively. However, landowners and the interested public usually have limited insights into these types of information as access to data and software is limited. Static maps and the inability to access special file formats increase the difficulty of viewing and investigating the data. This contribution presents a dedicated, interactive, web-based analysis tool for integrating land cover and land use maps as well as urban expansion indices. The tool’s concept, development and functionality are presented, and its general design is reviewed based on an actual implementation case. The setup allows integrating land use and land cover (LULC) change data alongside SDG indicators. The tool’s design aims to enhance user accessibility to information on urban expansion indices and LULC. We demonstrate that such a tool can be used to help disseminate results and to improve communication with the public in the context of other use cases.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"11 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88681419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1553/giscience2022_02_s32
A. Rolwes, Paul-Bogdan Radu, K. Böhm
{"title":"Analysing and Identifying Geospatial Key Factors in Smart Cities – Model Enhancements in the Use Case of Carpark Occupancy","authors":"A. Rolwes, Paul-Bogdan Radu, K. Böhm","doi":"10.1553/giscience2022_02_s32","DOIUrl":"https://doi.org/10.1553/giscience2022_02_s32","url":null,"abstract":"","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85907565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1553/giscience2023_01_s3
Kristin Stock, K. Wijegunarathna, C. B. Jones, H. Morris, Pragyan Das, D. Medyckyj-Scott, Brandon Whitehead
Vast numbers of biological specimens (e.g. flora, fauna, soils) are stored in collections globally. Many of these have only a natural-language location description, such as ‘ 200ft above and south of main highway, 1.1 miles west of Porters Pass ’, and numerical coordinates are unknown. The BioWhere project is pioneering methods to automatically determine the geographic coordinates (georeferences) of complex location descriptions. Particular challenges are posed by the variable accuracy of recent and historical data that might be used to train models to predict geographic coordinates from the natural-language descriptions; by the presence of historical place names in the descriptions that are not stored in existing gazetteers; and by the vague and context-sensitive nature (e.g. above , on , south of ) of the descriptions. We are addressing these challenges by extending the latest transformer-based deep learning models to parse locality descriptions, and to build models for specific spatial terms that incorporate geographic context and data quality to more accurately predict georeferences. We also describe a gazetteer that contains enriched cultural content to support georeferencing of historical records, and to serve as a store of New Zealand Māori cultural knowledge for future generations.
大量的生物标本(如植物、动物、土壤)储存在全球各地。其中许多只有自然语言的位置描述，比如“在主干道以南200英尺处，波特斯山口以西1.1英里处”，数字坐标是未知的。BioWhere项目是自动确定复杂位置描述的地理坐标(地理参考)方法的先驱。近期和历史数据的不同准确性带来了特殊的挑战，这些数据可能用于训练模型，以从自然语言描述中预测地理坐标;通过在现有地名辞典中没有存储的描述中出现历史地名;并且通过描述的模糊和上下文敏感的性质(例如above, on, south of)。我们正在通过扩展最新的基于转换器的深度学习模型来解决这些挑战，以解析位置描述，并为包含地理背景和数据质量的特定空间术语构建模型，以更准确地预测地理参考。我们还描述了一个包含丰富文化内容的地名辞典，以支持历史记录的地理参考，并为后代提供新西兰Māori文化知识的存储。
{"title":"The BioWhere Project: Unlocking the Potential of Biological Collections Data","authors":"Kristin Stock, K. Wijegunarathna, C. B. Jones, H. Morris, Pragyan Das, D. Medyckyj-Scott, Brandon Whitehead","doi":"10.1553/giscience2023_01_s3","DOIUrl":"https://doi.org/10.1553/giscience2023_01_s3","url":null,"abstract":"Vast numbers of biological specimens (e.g. flora, fauna, soils) are stored in collections globally. Many of these have only a natural-language location description, such as ‘ 200ft above and south of main highway, 1.1 miles west of Porters Pass ’, and numerical coordinates are unknown. The BioWhere project is pioneering methods to automatically determine the geographic coordinates (georeferences) of complex location descriptions. Particular challenges are posed by the variable accuracy of recent and historical data that might be used to train models to predict geographic coordinates from the natural-language descriptions; by the presence of historical place names in the descriptions that are not stored in existing gazetteers; and by the vague and context-sensitive nature (e.g. above , on , south of ) of the descriptions. We are addressing these challenges by extending the latest transformer-based deep learning models to parse locality descriptions, and to build models for specific spatial terms that incorporate geographic context and data quality to more accurately predict georeferences. We also describe a gazetteer that contains enriched cultural content to support georeferencing of historical records, and to serve as a store of New Zealand Māori cultural knowledge for future generations.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84974641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1553/giscience2023_01_s110
H. N. Serere, Umut Nefta Kanilmaz, Sruthi Ketineni, Bernd Resch
Geocoding is a process of converting human-readable addresses into latitude and longitude points. Whilst most geocoders tend to perform well on structured addresses, their performance drops significantly in the presence of unstructured addresses, such as locations written in informal language. In this paper, we make an extensive comparison of geocoder performance on unstructured location mentions within tweets. Using nine geocoders and a worldwide English-language Twitter dataset, we compare the geocoders’ recall, precision, consensus and bias values. As in previous similar studies, Google Maps showed the highest overall performance. However, with the exception of Google Maps, we found that geocoders which use open data have higher performance than those which do not. The open-data geocoders showed the least per-continent bias and the highest consensus with Google Maps. These results suggest the possibility of improving geocoder performance on unstructured locations by extending or enhancing the quality of openly available datasets.
{"title":"A Comparative Study of Geocoder Performance on Unstructured Tweet Locations","authors":"H. N. Serere, Umut Nefta Kanilmaz, Sruthi Ketineni, Bernd Resch","doi":"10.1553/giscience2023_01_s110","DOIUrl":"https://doi.org/10.1553/giscience2023_01_s110","url":null,"abstract":"Geocoding is a process of converting human-readable addresses into latitude and longitude points. Whilst most geocoders tend to perform well on structured addresses, their performance drops significantly in the presence of unstructured addresses, such as locations written in informal language. In this paper, we make an extensive comparison of geocoder performance on unstructured location mentions within tweets. Using nine geocoders and a worldwide English-language Twitter dataset, we compare the geocoders’ recall, precision, consensus and bias values. As in previous similar studies, Google Maps showed the highest overall performance. However, with the exception of Google Maps, we found that geocoders which use open data have higher performance than those which do not. The open-data geocoders showed the least per-continent bias and the highest consensus with Google Maps. These results suggest the possibility of improving geocoder performance on unstructured locations by extending or enhancing the quality of openly available datasets.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"102 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79698066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1553/giscience2023_01_s101
Yingwen Deng, Dagmar Lahnsteiner, Thomas Prinz
Active and sustainable commuting has a positive impact not only on the environment but also on individuals’ health. A shift from using unsustainable motorized transport modes to active and sustainable alternatives (cycling, walking and public transport) is desirable. To enable such a shift, it is important to raise public awareness and to call for joint efforts by individuals, employers, planning practitioners and decision-makers. In the ActNow research project, a tool was developed which provides location-specific information vital for promoting active and sustainable commuting. Applying GIS methods, heterogeneous data were analysed and integrated into a 500-metre raster. This raster is embedded in a web application, which provides users with a holistic view of commuter traffic, the accessibility of infrastructure, as well as the potentials, strengths and weaknesses at locations of interest for active and sustainable forms of commuting. The tool provides planners, traffic associations and mobility consultants with evidence that can support them to achieve improvements in traffic, the environment and public health.
{"title":"Promoting Active and Sustainable Commuting: A Tool for Analysing Location-specific Conditions and Potentials for Walking, Cycling and Public Transport","authors":"Yingwen Deng, Dagmar Lahnsteiner, Thomas Prinz","doi":"10.1553/giscience2023_01_s101","DOIUrl":"https://doi.org/10.1553/giscience2023_01_s101","url":null,"abstract":"Active and sustainable commuting has a positive impact not only on the environment but also on individuals’ health. A shift from using unsustainable motorized transport modes to active and sustainable alternatives (cycling, walking and public transport) is desirable. To enable such a shift, it is important to raise public awareness and to call for joint efforts by individuals, employers, planning practitioners and decision-makers. In the ActNow research project, a tool was developed which provides location-specific information vital for promoting active and sustainable commuting. Applying GIS methods, heterogeneous data were analysed and integrated into a 500-metre raster. This raster is embedded in a web application, which provides users with a holistic view of commuter traffic, the accessibility of infrastructure, as well as the potentials, strengths and weaknesses at locations of interest for active and sustainable forms of commuting. The tool provides planners, traffic associations and mobility consultants with evidence that can support them to achieve improvements in traffic, the environment and public health.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90698357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1553/giscience2022_02_s73
A. Graser
Gaining insights from trajectory datasets is a challenging task that requires suitable visual data representations. There is a considerable gap between the state-of-the-art cartographic techniques presented in the literature and currently available spatial data science toolboxes. This review paper presents the current state of geospatial visualization tools for trajectory data, focusing on the Python and Jupyter notebooks ecosystem. The shortcomings identified provide pointers for further scientific software development, as well as a reference for data scientists in choosing the best-fitting tool for a specific job.
{"title":"The State of Trajectory Visualization in Notebook Environments","authors":"A. Graser","doi":"10.1553/giscience2022_02_s73","DOIUrl":"https://doi.org/10.1553/giscience2022_02_s73","url":null,"abstract":"Gaining insights from trajectory datasets is a challenging task that requires suitable visual data representations. There is a considerable gap between the state-of-the-art cartographic techniques presented in the literature and currently available spatial data science toolboxes. This review paper presents the current state of geospatial visualization tools for trajectory data, focusing on the Python and Jupyter notebooks ecosystem. The shortcomings identified provide pointers for further scientific software development, as well as a reference for data scientists in choosing the best-fitting tool for a specific job.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75285207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1553/giscience2022_01_s135
Christina Zorenböhmer, Eva Missoni-Steinbacher, P. Jeremias, U. Öttl, Bernd Resch
The ever-increasing digitalization of our everyday lives repeatedly and prominently sparks discussion about the need for STEAM (Science, Technology, Engineering, Arts, Mathematics) skills. In terms of education, STEAM initiatives focus on skills development and the use of digital technologies to stimulate students’ engagement with societal issues. This paper introduces an iterative co-creation-based approach for enabling and enriching STEAM learning experiences. Our goal is to foster young citizen scientists by having them share and discuss stories of personal interest and experience, while practising and improving their STEAM skills through local engagement. The young citizen scientists contribute to all phases of a story, including its co-creation and conception, data collection, discussion in workshops, and generating outputs relevant to local and regional policymakers. The particular spatial focus of the approach is in the places where the stories take place. The stories are uploaded to a map-based platform, which is used for data collection and visualization, and as a focus for discussion. Through personal involvement, the young citizen scientists are motivated, which fosters local ownership, sustainable use of the platform, and effective capacity building in digital skills.
{"title":"STEAM Stories: A Co-creation Approach to Building STEAM Skills through Stories of Personal Interest","authors":"Christina Zorenböhmer, Eva Missoni-Steinbacher, P. Jeremias, U. Öttl, Bernd Resch","doi":"10.1553/giscience2022_01_s135","DOIUrl":"https://doi.org/10.1553/giscience2022_01_s135","url":null,"abstract":"The ever-increasing digitalization of our everyday lives repeatedly and prominently sparks discussion about the need for STEAM (Science, Technology, Engineering, Arts, Mathematics) skills. In terms of education, STEAM initiatives focus on skills development and the use of digital technologies to stimulate students’ engagement with societal issues. This paper introduces an iterative co-creation-based approach for enabling and enriching STEAM learning experiences. Our goal is to foster young citizen scientists by having them share and discuss stories of personal interest and experience, while practising and improving their STEAM skills through local engagement. The young citizen scientists contribute to all phases of a story, including its co-creation and conception, data collection, discussion in workshops, and generating outputs relevant to local and regional policymakers. The particular spatial focus of the approach is in the places where the stories take place. The stories are uploaded to a map-based platform, which is used for data collection and visualization, and as a focus for discussion. Through personal involvement, the young citizen scientists are motivated, which fosters local ownership, sustainable use of the platform, and effective capacity building in digital skills.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82926167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1553/giscience2022_01_s58
H. Kemper
Drought is among the most common but least understood phenomena that affect an increasing number of people in the context of climate change. To understand underlying drought dynamics affecting the local agricultural production in Botswana, a broad database comprising climatic and remote-sensing data together with socioeconomic indicators was set up. A data science approach that includes statistical and machine learning methods was chosen to retrieve information applicable in a drought early-warning system. The aim of the study was to examine how data science can contribute to the understanding of drought risk through the integration of various data sources. Different regression models (including linear and OLS) were applied. Naïve Bayes classification and Random Forest regression were included, as was a change point analysis. The impacts of two variables in particular, the Standardized Precipitation Index (SPI) and the Southern Oscillation Index (SOI), on crop productivity could be observed, highlighting possible national and regional thresholds. Further development of the early warning system, including validation, should be accompanied by ground-truth information and work with local partners.
{"title":"Development of a Drought Early Warning System based on the Prediction of Agricultural Productivity: A Data Science Approach","authors":"H. Kemper","doi":"10.1553/giscience2022_01_s58","DOIUrl":"https://doi.org/10.1553/giscience2022_01_s58","url":null,"abstract":"Drought is among the most common but least understood phenomena that affect an increasing number of people in the context of climate change. To understand underlying drought dynamics affecting the local agricultural production in Botswana, a broad database comprising climatic and remote-sensing data together with socioeconomic indicators was set up. A data science approach that includes statistical and machine learning methods was chosen to retrieve information applicable in a drought early-warning system. The aim of the study was to examine how data science can contribute to the understanding of drought risk through the integration of various data sources. Different regression models (including linear and OLS) were applied. Naïve Bayes classification and Random Forest regression were included, as was a change point analysis. The impacts of two variables in particular, the Standardized Precipitation Index (SPI) and the Southern Oscillation Index (SOI), on crop productivity could be observed, highlighting possible national and regional thresholds. Further development of the early warning system, including validation, should be accompanied by ground-truth information and work with local partners.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76715504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1553/giscience2022_01_s17
H. Hochmair, Eli Brossell, Z. Fu
Inadequate provision of public transportation services can lead to mobility-related social exclusion for disadvantaged population groups (e.g., lower-income families, the elderly), and limited accessibility to jobs, healthy food, and recreational as well as social activities. The aim of this study is to identify areas in Miami-Dade County, Florida, where disadvantaged populations lack transit-based access to these opportunities, and where transit service improvement could benefit these groups especially. This involves developing a transit-based accessibility index which uses timetable data from three public transit agencies. It also entails devising a vulnerability index based on a combination of socioeconomic variables to identify disadvantaged population groups with regards to mobility. Both indices can be combined into a service provision score which quantifies the presence of populations in need of transit service improvements. Results show that the combination of the different index maps and the application of Hotspot analysis can help to identify areas requiring transit service improvement in order to achieve accessibility equity. The analysis and interpretation of accessibility maps and selected demographic layers, such as percentage of households without vehicle, facilitates the identification of areas with above-average rates of users who rely on public transportation.
{"title":"Identification of Transit Service Gaps through Accessibility and Social Vulnerability Mapping in Miami-Dade County","authors":"H. Hochmair, Eli Brossell, Z. Fu","doi":"10.1553/giscience2022_01_s17","DOIUrl":"https://doi.org/10.1553/giscience2022_01_s17","url":null,"abstract":"Inadequate provision of public transportation services can lead to mobility-related social exclusion for disadvantaged population groups (e.g., lower-income families, the elderly), and limited accessibility to jobs, healthy food, and recreational as well as social activities. The aim of this study is to identify areas in Miami-Dade County, Florida, where disadvantaged populations lack transit-based access to these opportunities, and where transit service improvement could benefit these groups especially. This involves developing a transit-based accessibility index which uses timetable data from three public transit agencies. It also entails devising a vulnerability index based on a combination of socioeconomic variables to identify disadvantaged population groups with regards to mobility. Both indices can be combined into a service provision score which quantifies the presence of populations in need of transit service improvements. Results show that the combination of the different index maps and the application of Hotspot analysis can help to identify areas requiring transit service improvement in order to achieve accessibility equity. The analysis and interpretation of accessibility maps and selected demographic layers, such as percentage of households without vehicle, facilitates the identification of areas with above-average rates of users who rely on public transportation.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80732955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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