Pub Date : 2025-08-18DOI: 10.1080/19475683.2025.2548205
Muktar H Aliyu, Ibraheem M Karaye, Chelsea van Wyk, Aishatu L Adamu, Fatimah I Tsiga-Ahmed, Hafsah B El Yakubu, Rukayya S Alkassim, Baba M Musa, Mahmoud U Sani, C William Wester
Geographic Information Systems (GIS) have become essential in health sciences for analysing and visualizing health-related spatio-temporal data. This report details the outcomes of a week-long interactive GIS workshop held in Kano, Nigeria, and organized by the Vanderbilt-Nigeria Building Research Capacity in HIV and NCDs (V-BRCH) training programme. The workshop aimed to enhance GIS knowledge and confidence among early-career physician scientists. Thirty-three participants were trained in core GIS competencies, including software selection, data visualization and spatial analysis using Quantum GIS (QGIS). Pre- and post-workshop surveys assessed participants' knowledge and confidence levels across various GIS topics and competency areas. There was a significant improvement in self-reported participant knowledge across all GIS topic areas evaluated, with the highest percentage gains in geocoding health data (149%) and using QGIS software (135%). The percentage increase in post-workshop confidence was greatest for importing spatial data into QGIS (153%), navigating the QGIS interface (150%) and mapping public health data (150%). Participants rated the workshop highly (4.7/5, 1 = 'poor' and 5 = 'excellent'). Recommendations for course improvement included extending the duration of the workshop, using local data in exercises and employing more visual aids. These findings suggest that GIS training opportunities can be beneficial in building GIS knowledge and enhancing the skills of physician scientists in similar low- and middle-income settings.
{"title":"Enhancing physician scientists' skills in Geographic Information Systems: insights from an interactive workshop.","authors":"Muktar H Aliyu, Ibraheem M Karaye, Chelsea van Wyk, Aishatu L Adamu, Fatimah I Tsiga-Ahmed, Hafsah B El Yakubu, Rukayya S Alkassim, Baba M Musa, Mahmoud U Sani, C William Wester","doi":"10.1080/19475683.2025.2548205","DOIUrl":"10.1080/19475683.2025.2548205","url":null,"abstract":"<p><p>Geographic Information Systems (GIS) have become essential in health sciences for analysing and visualizing health-related spatio-temporal data. This report details the outcomes of a week-long interactive GIS workshop held in Kano, Nigeria, and organized by the Vanderbilt-Nigeria Building Research Capacity in HIV and NCDs (V-BRCH) training programme. The workshop aimed to enhance GIS knowledge and confidence among early-career physician scientists. Thirty-three participants were trained in core GIS competencies, including software selection, data visualization and spatial analysis using Quantum GIS (QGIS). Pre- and post-workshop surveys assessed participants' knowledge and confidence levels across various GIS topics and competency areas. There was a significant improvement in self-reported participant knowledge across all GIS topic areas evaluated, with the highest percentage gains in geocoding health data (149%) and using QGIS software (135%). The percentage increase in post-workshop confidence was greatest for importing spatial data into QGIS (153%), navigating the QGIS interface (150%) and mapping public health data (150%). Participants rated the workshop highly (4.7/5, 1 = 'poor' and 5 = 'excellent'). Recommendations for course improvement included extending the duration of the workshop, using local data in exercises and employing more visual aids. These findings suggest that GIS training opportunities can be beneficial in building GIS knowledge and enhancing the skills of physician scientists in similar low- and middle-income settings.</p>","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12525724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-02-17DOI: 10.1080/19475683.2025.2469110
Xinyue Ye, Tan Yigitcanlar, Michael Goodchild, Xiao Huang, Wenwen Li, Shih-Lung Shaw, Yanjie Fu, Wenjing Gong, Galen Newman
Urban science aims to explain, discover, understand, and generalize (EDUG) complex, human-centric systems, emphasizing societal context and sustainability. However, integrating artificial intelligence (AI) into urban science presents challenges, including data availability, ethical considerations, and the 'black-box' nature of many AI models. Despite these limitations, AI offers significant opportunities for urban management and planning by leveraging vast, multimodal datasets to optimize infrastructure, predict trends, and enhance resilience. Techniques such as explainable AI and knowledge-driven approaches have begun addressing transparency concerns, aligning AI outputs with urban science's emphasis on interpretability. Urban science reciprocally contributes to AI development by embedding contextual awareness and human-centric insights, enhancing AI's ability to navigate urban complexities. Examples include digital twins for real-time urban analysis and generative AI for inclusive urban modelling. This opinion piece advocates for fostering a symbiotic relationship between AI and urban science, emphasizing co-learning and ethical collaboration. By integrating technical innovation with societal needs, the convergence of AI and urban science - termed the 'New Urban Science' - promises smarter, equitable, and sustainable cities. This paradigm underscores the transformative potential of aligning AI advancements with urban science's foundational goals.
{"title":"Artificial intelligence in urban science: why does it matter?","authors":"Xinyue Ye, Tan Yigitcanlar, Michael Goodchild, Xiao Huang, Wenwen Li, Shih-Lung Shaw, Yanjie Fu, Wenjing Gong, Galen Newman","doi":"10.1080/19475683.2025.2469110","DOIUrl":"10.1080/19475683.2025.2469110","url":null,"abstract":"<p><p>Urban science aims to explain, discover, understand, and generalize (EDUG) complex, human-centric systems, emphasizing societal context and sustainability. However, integrating artificial intelligence (AI) into urban science presents challenges, including data availability, ethical considerations, and the 'black-box' nature of many AI models. Despite these limitations, AI offers significant opportunities for urban management and planning by leveraging vast, multimodal datasets to optimize infrastructure, predict trends, and enhance resilience. Techniques such as explainable AI and knowledge-driven approaches have begun addressing transparency concerns, aligning AI outputs with urban science's emphasis on interpretability. Urban science reciprocally contributes to AI development by embedding contextual awareness and human-centric insights, enhancing AI's ability to navigate urban complexities. Examples include digital twins for real-time urban analysis and generative AI for inclusive urban modelling. This opinion piece advocates for fostering a symbiotic relationship between AI and urban science, emphasizing co-learning and ethical collaboration. By integrating technical innovation with societal needs, the convergence of AI and urban science - termed the 'New Urban Science' - promises smarter, equitable, and sustainable cities. This paradigm underscores the transformative potential of aligning AI advancements with urban science's foundational goals.</p>","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"31 2","pages":"181-189"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12435530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-04DOI: 10.1080/19475683.2023.2298979
Qianwen Zhou, N. Ren, Changqing Zhu, Qifei Zhou
{"title":"Zero watermarking algorithm for BIM data based on distance partitioning and local feature","authors":"Qianwen Zhou, N. Ren, Changqing Zhu, Qifei Zhou","doi":"10.1080/19475683.2023.2298979","DOIUrl":"https://doi.org/10.1080/19475683.2023.2298979","url":null,"abstract":"","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"57 16","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386844","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 : 2023-09-27DOI: 10.1080/19475683.2023.2257788
Tyler D. Hoffman, Peter Kedron
Causal inference is a rapidly growing field of statistics that applies logical reasoning to statistical inference to estimate causal relationships. Spatial data poses several problems in causal inference – namely, spatial confounding and interference – that require different strategies when designing causal models. In order to obtain valid inferences, existing nonspatial causal models must adjust for such spatial problems. Given the blossoming literature on spatial causal inference, this research analyzes the usage of spatial causal models under a priori knowledge and a priori ignorance of the spatial structure of data. We synthesize existing research directions in noncausal spatial modelling and causal nonspatial modelling by assessing the performance of 28 spatial causal models across 16 spatial data scenarios. We used ordinary least squares (OLS) models, conditional autoregressive (CAR) models, and jointly CAR models for outcome and treatment variables as the basis for the tested models, equipping them with a variety of spatial causal adjustments. We compare our results to principles of noncausal spatial modelling and investigate their implications for spatial causal modelling. Specifically, we show that noncausal spatial modelling guidance holds in causal spatial modelling workflows and demonstrate how researchers can leverage noncausal theory to great effect. In parallel, we introduce the spycause Python package of spatial causal models and data simulators to facilitate the widespread use of these models and to enable reproduction and extension of our work.
{"title":"Controlling for spatial confounding and spatial interference in causal inference: modelling insights from a computational experiment","authors":"Tyler D. Hoffman, Peter Kedron","doi":"10.1080/19475683.2023.2257788","DOIUrl":"https://doi.org/10.1080/19475683.2023.2257788","url":null,"abstract":"Causal inference is a rapidly growing field of statistics that applies logical reasoning to statistical inference to estimate causal relationships. Spatial data poses several problems in causal inference – namely, spatial confounding and interference – that require different strategies when designing causal models. In order to obtain valid inferences, existing nonspatial causal models must adjust for such spatial problems. Given the blossoming literature on spatial causal inference, this research analyzes the usage of spatial causal models under a priori knowledge and a priori ignorance of the spatial structure of data. We synthesize existing research directions in noncausal spatial modelling and causal nonspatial modelling by assessing the performance of 28 spatial causal models across 16 spatial data scenarios. We used ordinary least squares (OLS) models, conditional autoregressive (CAR) models, and jointly CAR models for outcome and treatment variables as the basis for the tested models, equipping them with a variety of spatial causal adjustments. We compare our results to principles of noncausal spatial modelling and investigate their implications for spatial causal modelling. Specifically, we show that noncausal spatial modelling guidance holds in causal spatial modelling workflows and demonstrate how researchers can leverage noncausal theory to great effect. In parallel, we introduce the spycause Python package of spatial causal models and data simulators to facilitate the widespread use of these models and to enable reproduction and extension of our work.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135581604","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 : 2023-09-06DOI: 10.1080/19475683.2023.2255072
Tarunamulia, J. Sammut
ABSTRACT The rapid expansion of extensive brackish water aquaculture (BA) in Indonesia has created an urgent need to develop effective and reliable methods to evaluate and select sites suitable for aquaculture development. The lack of supporting spatial data at appropriate scales has limited the application of GIS-based multi-criteria evaluation methods (MCEs) in Indonesia. This study presents alternative fuzzy-based Geographic Information System (GIS) methods to evaluate and select sites suitable for extensive brackish water aquaculture. This study successfully produced fuzzy set maps from a water availability sub-model, a land conversion sub-model and a green belt buffer zone sub-model. With grades of membership, the fuzzy set maps provide smooth class boundary representation, which creates more options for decision-making than a map classified with crisp logic. Combining these sub-models produced an overall site suitability map at the scale of 1:50,000 for the study region. This final suitability map effectively excluded more than 95% of the unsuitable area for BA. This broad-scale site suitability assessment approach is a helpful planning tool to promote Indonesia’s sustainable development of extensive brackish water aquaculture. It identifies possible conflicts in land uses and considers conservation issues early in the planning process. The output can be used to scope research areas for more detailed investigations in countries where BA is a vital livelihood.
{"title":"Application of GIS and fuzzy sets to small-scale site suitability assessment for extensive brackish water aquaculture","authors":"Tarunamulia, J. Sammut","doi":"10.1080/19475683.2023.2255072","DOIUrl":"https://doi.org/10.1080/19475683.2023.2255072","url":null,"abstract":"ABSTRACT The rapid expansion of extensive brackish water aquaculture (BA) in Indonesia has created an urgent need to develop effective and reliable methods to evaluate and select sites suitable for aquaculture development. The lack of supporting spatial data at appropriate scales has limited the application of GIS-based multi-criteria evaluation methods (MCEs) in Indonesia. This study presents alternative fuzzy-based Geographic Information System (GIS) methods to evaluate and select sites suitable for extensive brackish water aquaculture. This study successfully produced fuzzy set maps from a water availability sub-model, a land conversion sub-model and a green belt buffer zone sub-model. With grades of membership, the fuzzy set maps provide smooth class boundary representation, which creates more options for decision-making than a map classified with crisp logic. Combining these sub-models produced an overall site suitability map at the scale of 1:50,000 for the study region. This final suitability map effectively excluded more than 95% of the unsuitable area for BA. This broad-scale site suitability assessment approach is a helpful planning tool to promote Indonesia’s sustainable development of extensive brackish water aquaculture. It identifies possible conflicts in land uses and considers conservation issues early in the planning process. The output can be used to scope research areas for more detailed investigations in countries where BA is a vital livelihood.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"87 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80919604","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 : 2023-07-30DOI: 10.1080/19475683.2023.2241526
Sheng Hu, Song Gao, W. Luo, Liang Wu, Tianqi Li, Yongyang Xu, Ziwei Zhang
ABSTRACT The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban hierarchical spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management.
{"title":"Revealing intra-urban hierarchical spatial structure through representation learning by combining road network abstraction model and taxi trajectory data","authors":"Sheng Hu, Song Gao, W. Luo, Liang Wu, Tianqi Li, Yongyang Xu, Ziwei Zhang","doi":"10.1080/19475683.2023.2241526","DOIUrl":"https://doi.org/10.1080/19475683.2023.2241526","url":null,"abstract":"ABSTRACT The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban hierarchical spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"60 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84648804","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 : 2023-07-17DOI: 10.1080/19475683.2023.2236678
Mackenzie Kottwitz, Guiming Zhang, Jin Xu
ABSTRACT Hurricane activity has been increasing in frequency and severity in recent years. This has serious implications for coastal and nearby communities who, when recovering from hurricanes, seek outside assistance from relevant government, non-governmental agencies, and nearby communities. The ever-increasing popularity of social media offers a new medium through which such social relevancy can be derived to inform targeted assistance-seeking efforts. This study utilizes Twitter to develop an understanding of disaster relevancy across space and time to establish a clearer context for impacted communities as to when and where assistance may be derived. Tweets were collected for three hurricanes within the contiguous United States (Hurricane Harvey in 2017, Florence in 2018 and Laura in 2020) and examined over a 12-week period following hurricane landfalls. The relationships between tweets and time and between tweets and distance were examined through correlation analysis. Results show statistically significant time- and distance-decay effects of hurricane relevancy on social media, though the time-decay effect was stronger. Most tweets occurred during the first week following hurricane landfall within the states wherein the hurricanes made landfall as well as around large cities. These findings could inform aid-seeking efforts in the event of hurricanes and other disasters.
{"title":"The time- and distance-decay effects of hurricane relevancy on social media: an empirical study of three hurricanes in the United States","authors":"Mackenzie Kottwitz, Guiming Zhang, Jin Xu","doi":"10.1080/19475683.2023.2236678","DOIUrl":"https://doi.org/10.1080/19475683.2023.2236678","url":null,"abstract":"ABSTRACT Hurricane activity has been increasing in frequency and severity in recent years. This has serious implications for coastal and nearby communities who, when recovering from hurricanes, seek outside assistance from relevant government, non-governmental agencies, and nearby communities. The ever-increasing popularity of social media offers a new medium through which such social relevancy can be derived to inform targeted assistance-seeking efforts. This study utilizes Twitter to develop an understanding of disaster relevancy across space and time to establish a clearer context for impacted communities as to when and where assistance may be derived. Tweets were collected for three hurricanes within the contiguous United States (Hurricane Harvey in 2017, Florence in 2018 and Laura in 2020) and examined over a 12-week period following hurricane landfalls. The relationships between tweets and time and between tweets and distance were examined through correlation analysis. Results show statistically significant time- and distance-decay effects of hurricane relevancy on social media, though the time-decay effect was stronger. Most tweets occurred during the first week following hurricane landfall within the states wherein the hurricanes made landfall as well as around large cities. These findings could inform aid-seeking efforts in the event of hurricanes and other disasters.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"19 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84784247","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 : 2023-07-03DOI: 10.1080/19475683.2023.2226196
J. O'Mahony, A. Vanmechelen, P. Holloway
ABSTRACT Invasive species are ranked as one of the leading drivers of global biodiversity loss. To mitigate their impact, we must understand the future risks caused by invasive species, particularly to flora of conservation concern. Here we used species distribution modelling (SDM) to project the current and future (RCP45 and RCP85 2050) distributions of four deer species and 13 plant species of conservation concern for the island of Ireland, quantifying changes in distributions and overlap. Large areas of suitable habitat for the deer species were predicted with high accuracy across all counties, with future climate scenarios identifying an expansion in sika deer distributions and a decrease in muntjac and fallow deer distributions. Red deer declined under the moderate climate change scenario but increased under the worst-case projection. Future projections predicted the (local) extinction of six (out of 13) endangered and vulnerable plant species. An expansion in distributions was observed for four plant species; however, these areas had large overlap with the future predictions of deer, placing further pressures on these plant species. These findings suggest that targeted conservation and management measures are required to alleviate the pressures on ‘at-risk’ plant species due to grazing from native and non-native deer.
{"title":"Quantifying the distribution and potential biotic interactions between deer and flora using species distribution modelling","authors":"J. O'Mahony, A. Vanmechelen, P. Holloway","doi":"10.1080/19475683.2023.2226196","DOIUrl":"https://doi.org/10.1080/19475683.2023.2226196","url":null,"abstract":"ABSTRACT Invasive species are ranked as one of the leading drivers of global biodiversity loss. To mitigate their impact, we must understand the future risks caused by invasive species, particularly to flora of conservation concern. Here we used species distribution modelling (SDM) to project the current and future (RCP45 and RCP85 2050) distributions of four deer species and 13 plant species of conservation concern for the island of Ireland, quantifying changes in distributions and overlap. Large areas of suitable habitat for the deer species were predicted with high accuracy across all counties, with future climate scenarios identifying an expansion in sika deer distributions and a decrease in muntjac and fallow deer distributions. Red deer declined under the moderate climate change scenario but increased under the worst-case projection. Future projections predicted the (local) extinction of six (out of 13) endangered and vulnerable plant species. An expansion in distributions was observed for four plant species; however, these areas had large overlap with the future predictions of deer, placing further pressures on these plant species. These findings suggest that targeted conservation and management measures are required to alleviate the pressures on ‘at-risk’ plant species due to grazing from native and non-native deer.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"190 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74180906","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 : 2023-06-29DOI: 10.1080/19475683.2023.2226189
M. Arunachalam, J. Saravanavel, Ajith Joseph Kochuparampil
ABSTRACT Social vulnerability shows the lack of capacities of a person or groups across space and time to prepare for, respond to, and recover from the impact of natural hazards. It involves a combination of socioeconomic and demographic factors that determine the degree to which a (human) system is susceptible to, or unable to cope with, the adverse effects of a disastrous event. Social Vulnerability Index (SoVI) is an effective tool to measure the social vulnerability of an area. Though SoVI has successfully applied in many different contexts and places for socioeconomic development and disaster risk reduction, most societies still lack awareness of how social differences within their population play a role during disastrous events. To address this gap, the present study aims to map the social vulnerability and identify the locations of a socially vulnerable community in the Chennai Metropolitan Area (CMA) through an inductive approach (e.g. factor analysis) using demographic and built-environment data in ArcGIS and SPSS environment. We analysed twenty-three individual variables from five different vulnerability components, such as population, housing, economics, healthcare service, and exposed elements using Principal Component Analysis, that reduced to a smaller set of multidimensional components that explained 71.2% of the total variance and calculated the final SoVI score by adding all five-factor scores. The resultant SoVI map identifies the most vulnerable areas in the highly populated and tightly packed residential areas of Chennai city and the least vulnerable areas on the outskirts of Chennai city. The constructed SoVI could assist planners and policymakers at the national, state, and local government level in making appropriate decisions at all phases of the disaster management cycle and help prioritize the implementation of Government welfare schemes.
{"title":"PCA-based approach for mapping social vulnerability to hazards in the Chennai metropolitan area, east coast of India","authors":"M. Arunachalam, J. Saravanavel, Ajith Joseph Kochuparampil","doi":"10.1080/19475683.2023.2226189","DOIUrl":"https://doi.org/10.1080/19475683.2023.2226189","url":null,"abstract":"ABSTRACT Social vulnerability shows the lack of capacities of a person or groups across space and time to prepare for, respond to, and recover from the impact of natural hazards. It involves a combination of socioeconomic and demographic factors that determine the degree to which a (human) system is susceptible to, or unable to cope with, the adverse effects of a disastrous event. Social Vulnerability Index (SoVI) is an effective tool to measure the social vulnerability of an area. Though SoVI has successfully applied in many different contexts and places for socioeconomic development and disaster risk reduction, most societies still lack awareness of how social differences within their population play a role during disastrous events. To address this gap, the present study aims to map the social vulnerability and identify the locations of a socially vulnerable community in the Chennai Metropolitan Area (CMA) through an inductive approach (e.g. factor analysis) using demographic and built-environment data in ArcGIS and SPSS environment. We analysed twenty-three individual variables from five different vulnerability components, such as population, housing, economics, healthcare service, and exposed elements using Principal Component Analysis, that reduced to a smaller set of multidimensional components that explained 71.2% of the total variance and calculated the final SoVI score by adding all five-factor scores. The resultant SoVI map identifies the most vulnerable areas in the highly populated and tightly packed residential areas of Chennai city and the least vulnerable areas on the outskirts of Chennai city. The constructed SoVI could assist planners and policymakers at the national, state, and local government level in making appropriate decisions at all phases of the disaster management cycle and help prioritize the implementation of Government welfare schemes.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"4 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78429953","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 : 2023-06-19DOI: 10.1080/19475683.2023.2226205
J. Tabora, R. Ancog, Patricia Ann J. Sanchez, M. Arboleda, I. Lit, C. Tiburan
ABSTRACT The Philippine Department of Environment and Natural Resources has protocols for identifying landscapes for conservation but lacks clear guidelines for mapping areas of interest. To augment the procedure, we explored using Multi-Criteria Overlay Raster Analysis that scores the best available data from a watershed to identify potential Critical Habitat or Protected Areas. The algorithm deducts potential areas for wildlife habitation from areas that contribute to conservation conflicts, resulting in a potential ‘safe zone’ for conservation. The framework is applied to a case study in a watershed in the southern Philippines and produces a gradient score to determine the most suitable to least suitable areas for conservation. By using the best available data and local perspectives, the synthesized methodological framework was found to be useful in the decision-making process.
{"title":"Selecting safe zone for threatened species conservation: a case study of a watershed in the southern Philippines","authors":"J. Tabora, R. Ancog, Patricia Ann J. Sanchez, M. Arboleda, I. Lit, C. Tiburan","doi":"10.1080/19475683.2023.2226205","DOIUrl":"https://doi.org/10.1080/19475683.2023.2226205","url":null,"abstract":"ABSTRACT The Philippine Department of Environment and Natural Resources has protocols for identifying landscapes for conservation but lacks clear guidelines for mapping areas of interest. To augment the procedure, we explored using Multi-Criteria Overlay Raster Analysis that scores the best available data from a watershed to identify potential Critical Habitat or Protected Areas. The algorithm deducts potential areas for wildlife habitation from areas that contribute to conservation conflicts, resulting in a potential ‘safe zone’ for conservation. The framework is applied to a case study in a watershed in the southern Philippines and produces a gradient score to determine the most suitable to least suitable areas for conservation. By using the best available data and local perspectives, the synthesized methodological framework was found to be useful in the decision-making process.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"23 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85309098","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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