This study employed Geographic Information System (GIS) and remote sensing approach to analyze the flood vulnerability areas in the Okoko basin area of Osogbo in Osun state, Southwestern Nigeria. High-resolution imageries, a Topographic map of the study area and a TCX software program (Version 2.0) were integrated using ArcGis (10.7). Some of the causative factors for flooding in the watershed were taken into account which are: Land use, Distance of buildings to drainage, Digital Elevation Model, and Slope. This study aimed at mapping the flood-vulnerable areas along the Okoko basin of Osogbo. In developing a flood risk/flood hazard map of the study area, and determining the level of expected disaster, a multi-criteria analysis was utilized. The factors considered were ranked in five classes with the highly vulnerable areas having the highest score of “5”. These factors were weighed according to the estimated significance of causing flooding. The study revealed that the study area has an estimated area of 17.85 km2 of which 14.2 km2 falls within the vulnerable areas while 3.6 km2 is on the least vulnerable areas. Moreover, out of 16,829 buildings in the study area, 8204 buildings were found susceptible to flood disasters. This research attempts to equip decision-makers to make accurate decisions and also serves as a mitigation measure for flood disaster management.
{"title":"Flood Vulnerability Mapping: A Case Study of Okoko Basin, Osogbo","authors":"Toyosi Beatrice Adedoja, Oladimeji Samuel Popoola, Taofeek Abayaomi Alaga, Adesioye E.A. Adesola","doi":"10.4236/jgis.2023.155029","DOIUrl":"https://doi.org/10.4236/jgis.2023.155029","url":null,"abstract":"This study employed Geographic Information System (GIS) and remote sensing approach to analyze the flood vulnerability areas in the Okoko basin area of Osogbo in Osun state, Southwestern Nigeria. High-resolution imageries, a Topographic map of the study area and a TCX software program (Version 2.0) were integrated using ArcGis (10.7). Some of the causative factors for flooding in the watershed were taken into account which are: Land use, Distance of buildings to drainage, Digital Elevation Model, and Slope. This study aimed at mapping the flood-vulnerable areas along the Okoko basin of Osogbo. In developing a flood risk/flood hazard map of the study area, and determining the level of expected disaster, a multi-criteria analysis was utilized. The factors considered were ranked in five classes with the highly vulnerable areas having the highest score of “5”. These factors were weighed according to the estimated significance of causing flooding. The study revealed that the study area has an estimated area of 17.85 km2 of which 14.2 km2 falls within the vulnerable areas while 3.6 km2 is on the least vulnerable areas. Moreover, out of 16,829 buildings in the study area, 8204 buildings were found susceptible to flood disasters. This research attempts to equip decision-makers to make accurate decisions and also serves as a mitigation measure for flood disaster management.","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135261428","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: 10.4236/jgis.2023.155028
Seong Nam Hwang, Kayla Meier
{"title":"Tornado Impacts in the US from 1950-2015: A GIS-Based Analysis of Vulnerability and Evolving Risk Zones for Human Casualties","authors":"Seong Nam Hwang, Kayla Meier","doi":"10.4236/jgis.2023.155028","DOIUrl":"https://doi.org/10.4236/jgis.2023.155028","url":null,"abstract":"","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134981339","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: 10.4236/jgis.2023.155021
Yitian Liu, Guangping Chen
{"title":"Assessment of the Retail Food Environment Using Integrated GIS and Modified Measures in Wuhan, China","authors":"Yitian Liu, Guangping Chen","doi":"10.4236/jgis.2023.155021","DOIUrl":"https://doi.org/10.4236/jgis.2023.155021","url":null,"abstract":"","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135400214","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: 10.4236/jgis.2023.155023
Priscilla M. Loh, Yaw A. Twumasi, Zhu H. Ning, Matilda Anokye, Diana B. Frimpong, Judith Oppong, Abena B. Asare-Ansah, Recheal N. D. Armah, Caroline Y. Apraku
The COVID-19 pandemic posed a serious threat to life on the entire planet, necessitating the imposition of a lockdown mechanism that restricted people’s movements to stop the disease’s spread. This period experienced a decline in air pollution emissions and some environmental changes, offering a rare opportunity to understand the effects of fewer human activities on the earth’s temperature. Hence, this study compares the changes in Land Surface Temperature (LST) that were observed prior to the pandemic (March & April 2019) and during the pandemic lockdown (March & April 2020) of three parishes in Louisiana. The data for this study was acquired using Landsat 8 Thermal Infrared Sensor (TIRS) Level 2, Collection 2, Tier 2 from the Google Earth Engine Catalog. For better visualization, the images that were derived had a cloud cover of less than 10%. Also, images for the three study areas were processed and categorized into four main classes: water, vegetation, built-up areas, and bare lands using a Random Forest Supervised Classification Algorithm. To improve the accuracy of the image classifications, three Normalized Difference Indices namely the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-Up Index (NDBI) were employed using the Near Infrared (NIR), Red, Green and SWIR bands for the calculations. After, these images were processed in Google Earth Engine to generate the LST products gridded at 30 m with a higher spatial resolution of 100 m according to the pre-pandemic (2019) and lockdown (2020) periods for the three study areas. Results of this study showed a decrease in LST values of the land cover classes from 2019 to 2020, with LST values in East Baton Parish decreasing from 44°C to 38°C, 42°C to 38°C in Lafayette Parish, and 43°C to 38°C in Orleans Parish. The variations in the LST values therefore indicate the impact of fewer anthropogenic factors on the earth’s temperature which requires regulatory and mitigative measures to continually reduce LST and control microclimate, especially in urban areas.
{"title":"Spatiotemporal Analysis of COVID-19 Lockdown Impact on the Land Surface Temperatures of Different Land Cover Types in Louisiana","authors":"Priscilla M. Loh, Yaw A. Twumasi, Zhu H. Ning, Matilda Anokye, Diana B. Frimpong, Judith Oppong, Abena B. Asare-Ansah, Recheal N. D. Armah, Caroline Y. Apraku","doi":"10.4236/jgis.2023.155023","DOIUrl":"https://doi.org/10.4236/jgis.2023.155023","url":null,"abstract":"The COVID-19 pandemic posed a serious threat to life on the entire planet, necessitating the imposition of a lockdown mechanism that restricted people’s movements to stop the disease’s spread. This period experienced a decline in air pollution emissions and some environmental changes, offering a rare opportunity to understand the effects of fewer human activities on the earth’s temperature. Hence, this study compares the changes in Land Surface Temperature (LST) that were observed prior to the pandemic (March & April 2019) and during the pandemic lockdown (March & April 2020) of three parishes in Louisiana. The data for this study was acquired using Landsat 8 Thermal Infrared Sensor (TIRS) Level 2, Collection 2, Tier 2 from the Google Earth Engine Catalog. For better visualization, the images that were derived had a cloud cover of less than 10%. Also, images for the three study areas were processed and categorized into four main classes: water, vegetation, built-up areas, and bare lands using a Random Forest Supervised Classification Algorithm. To improve the accuracy of the image classifications, three Normalized Difference Indices namely the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-Up Index (NDBI) were employed using the Near Infrared (NIR), Red, Green and SWIR bands for the calculations. After, these images were processed in Google Earth Engine to generate the LST products gridded at 30 m with a higher spatial resolution of 100 m according to the pre-pandemic (2019) and lockdown (2020) periods for the three study areas. Results of this study showed a decrease in LST values of the land cover classes from 2019 to 2020, with LST values in East Baton Parish decreasing from 44°C to 38°C, 42°C to 38°C in Lafayette Parish, and 43°C to 38°C in Orleans Parish. The variations in the LST values therefore indicate the impact of fewer anthropogenic factors on the earth’s temperature which requires regulatory and mitigative measures to continually reduce LST and control microclimate, especially in urban areas.","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136204739","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: 10.4236/jgis.2023.155027
Muditha K. Heenkenda
Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.
{"title":"A Spatial Epidemiology Case Study of Coronavirus (COVID-19) Disease and Geospatial Technologies","authors":"Muditha K. Heenkenda","doi":"10.4236/jgis.2023.155027","DOIUrl":"https://doi.org/10.4236/jgis.2023.155027","url":null,"abstract":"Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"63 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135158204","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: 10.4236/jgis.2023.155024
Festina Sadiku
This study proposes a detailed concept of how landscape changes can be transferred and communicated in a 3D environment using the storytelling approach. In 2018, Mocnik and Fairbairn argued that maps are good for representing a geographic space but texts have a better benefit than maps for telling a story [1]. A framework is presented on how a landscape change can be retrieved out of textual descriptions. A prototype of a 3D model with a projection on top of it was developed. The case study tells the story of the landscape change in the city of Pristina. The key element of the visualization is a timeline. Several media like cadastral maps, orthophotos, texts, graphics, and background sound are used and combined in an animated light show and the visualization was evaluated within a user study.
{"title":"Visualization of Landscape Changes in a 3D Environment Using the Storytelling Approach—The Example of the City of Pristina","authors":"Festina Sadiku","doi":"10.4236/jgis.2023.155024","DOIUrl":"https://doi.org/10.4236/jgis.2023.155024","url":null,"abstract":"This study proposes a detailed concept of how landscape changes can be transferred and communicated in a 3D environment using the storytelling approach. In 2018, Mocnik and Fairbairn argued that maps are good for representing a geographic space but texts have a better benefit than maps for telling a story [1]. A framework is presented on how a landscape change can be retrieved out of textual descriptions. A prototype of a 3D model with a projection on top of it was developed. The case study tells the story of the landscape change in the city of Pristina. The key element of the visualization is a timeline. Several media like cadastral maps, orthophotos, texts, graphics, and background sound are used and combined in an animated light show and the visualization was evaluated within a user study.","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136304495","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: 10.4236/jgis.2023.155026
Susan A. Okeyo, Galcano C. Mulaku, Collins M. Mwange
According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there; their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions; but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit; and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral.
{"title":"Leveraging Geospatial Technology for Smallholder Farmer Credit Scoring","authors":"Susan A. Okeyo, Galcano C. Mulaku, Collins M. Mwange","doi":"10.4236/jgis.2023.155026","DOIUrl":"https://doi.org/10.4236/jgis.2023.155026","url":null,"abstract":"According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there; their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions; but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit; and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral.","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136366295","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: 10.4236/jgis.2023.155022
Kyongil Woo, Adrian Onsen, WonSok Kim
Needs for real-time interactive visualization of 3D Tiles for massive 3D content on the web-based virtual globe is rapidly increasing, and to achieve this goal, 3D Tiles needs to be correctly geo-referenced to other geospatial data on a web-based virtual globe. It is possible to generate 3D Tiles from different kinds of spatial data through various software tools. However, due to various factors the 3D Tile datasets are often poorly or not at all geo-referenced. To tackle this issue, we propose a new 3D WebGIS framework that facilitates dynamic geo-referencing 3D Tiles on the CesiumJS virtual globe.
{"title":"Implementation of a 3D WebGIS for Dynamic Geo-Referencing of 3D Tiles on the Virtual Globe","authors":"Kyongil Woo, Adrian Onsen, WonSok Kim","doi":"10.4236/jgis.2023.155022","DOIUrl":"https://doi.org/10.4236/jgis.2023.155022","url":null,"abstract":"Needs for real-time interactive visualization of 3D Tiles for massive 3D content on the web-based virtual globe is rapidly increasing, and to achieve this goal, 3D Tiles needs to be correctly geo-referenced to other geospatial data on a web-based virtual globe. It is possible to generate 3D Tiles from different kinds of spatial data through various software tools. However, due to various factors the 3D Tile datasets are often poorly or not at all geo-referenced. To tackle this issue, we propose a new 3D WebGIS framework that facilitates dynamic geo-referencing 3D Tiles on the CesiumJS virtual globe.","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136204743","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: 10.4236/jgis.2023.155025
Kang Kuk, Woo Kyongil, Yun Cholnam, Kim Wonsok
Essentially, CesiumJS-which can be accessed through the link, http://cesiumjs.org, is an open-source JavaScript library for creating virtual globe environment in performance effective, high quality of rendering, precision, and user friendly. It is a wonderful tool for 3D-themed visualizations of earth. CesiumJS has a number of data sources, but none of them supports vector tile format. This article explains how to visualize Mapbox vector tiles in a CesiumJS virtual globe environment. CartoDB/BigQuery hosts vector tiles, and a process for producing vector tiles from massive vector data using the BigQuery tiler of CartoDB has been provided.
{"title":"Visualization of Vector Tiles on CesiumJS Virtual Globe","authors":"Kang Kuk, Woo Kyongil, Yun Cholnam, Kim Wonsok","doi":"10.4236/jgis.2023.155025","DOIUrl":"https://doi.org/10.4236/jgis.2023.155025","url":null,"abstract":"Essentially, CesiumJS-which can be accessed through the link, http://cesiumjs.org, is an open-source JavaScript library for creating virtual globe environment in performance effective, high quality of rendering, precision, and user friendly. It is a wonderful tool for 3D-themed visualizations of earth. CesiumJS has a number of data sources, but none of them supports vector tile format. This article explains how to visualize Mapbox vector tiles in a CesiumJS virtual globe environment. CartoDB/BigQuery hosts vector tiles, and a process for producing vector tiles from massive vector data using the BigQuery tiler of CartoDB has been provided.","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136305456","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 : 2021-06-01Epub Date: 2021-05-12DOI: 10.4236/jgis.2021.133017
Danny L Scerpella, Nicole G Bouranis, Melinda J Webster, Maria Dellapina, Sokha Koeuth, Lauren J Parker, Helen C Kales, Laura N Gitlin
Over 16 million caregivers of people living with dementia require support in a range of issues, including self-care, disease education, and guidance for how to manage behavioral and psychological symptoms of dementia (BPSD). Non-pharmacological interventions are needed to address these areas, and online applications have been shown to be safe and effective. To ensure the efficacy of such interventions, racially, ethnically, geographically, and socioeconomically diverse participants must be recruited to increase the generalizability of study outcomes. This protocol paper describes a recruitment plan using Geographic Information Systems (GIS) to reach a representative sample of caregivers across the United States for a national Phase III clinical study. Using publicly available census data from the American Community Survey (ACS), combined with location data for local aging resources such as Area Agencies on Aging (AAA), recruitment will be derived from data analysis conducted in ESRI ArcGIS v10.7.1. Datasets including age, gender, income, and education will be assessed nationally at the county and census tract spatial scale in a nine-step process to develop recruitment priority areas containing high concentrations of eligible participants living in the community. Overall, the current protocol will demonstrate the value of GIS in tailoring targeted outreach strategies to recruit community-dwelling populations through local resource institutions. This novel approach may have far-reaching implications in future recruitment initiatives and help to secure racially/ethnically diverse samples.
{"title":"Using Geographic Information Systems (GIS) for Targeted National Recruitment of Community-Dwelling Caregivers Managing Dementia-Related Behavioral and Psychological Symptoms: A Recruitment Approach for a Randomized Clinical Trial.","authors":"Danny L Scerpella, Nicole G Bouranis, Melinda J Webster, Maria Dellapina, Sokha Koeuth, Lauren J Parker, Helen C Kales, Laura N Gitlin","doi":"10.4236/jgis.2021.133017","DOIUrl":"10.4236/jgis.2021.133017","url":null,"abstract":"<p><p>Over 16 million caregivers of people living with dementia require support in a range of issues, including self-care, disease education, and guidance for how to manage behavioral and psychological symptoms of dementia (BPSD). Non-pharmacological interventions are needed to address these areas, and online applications have been shown to be safe and effective. To ensure the efficacy of such interventions, racially, ethnically, geographically, and socioeconomically diverse participants must be recruited to increase the generalizability of study outcomes. This protocol paper describes a recruitment plan using Geographic Information Systems (GIS) to reach a representative sample of caregivers across the United States for a national Phase III clinical study. Using publicly available census data from the American Community Survey (ACS), combined with location data for local aging resources such as Area Agencies on Aging (AAA), recruitment will be derived from data analysis conducted in ESRI ArcGIS v10.7.1. Datasets including age, gender, income, and education will be assessed nationally at the county and census tract spatial scale in a nine-step process to develop recruitment priority areas containing high concentrations of eligible participants living in the community. Overall, the current protocol will demonstrate the value of GIS in tailoring targeted outreach strategies to recruit community-dwelling populations through local resource institutions. This novel approach may have far-reaching implications in future recruitment initiatives and help to secure racially/ethnically diverse samples.</p>","PeriodicalId":93313,"journal":{"name":"Journal of geographic information system","volume":"13 3","pages":"302-317"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39387311","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}
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