Applications of Spatial Science and Technology in Health Research

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
International Journal of Environmental Research and Public Health ijerph	-	5.4	2004	29.6 Days	CHF 2500
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
ISPRS International Journal of Geo-Information ijgi	3.4	6.2	2012	35.5 Days	CHF 1700

36 pages, 15617 KiB

Open AccessArticle

Machine-Learning-Based Forest Classification and Regression (FCR) for Spatial Prediction of Liver Fluke Opisthorchis viverrini (OV) Infection in Small Sub-Watersheds

by Benjamabhorn Pumhirunroj, Patiwat Littidej, Thidarut Boonmars, Kanokwan Bootyothee, Atchara Artchayasawat, Phusit Khamphilung and Donald Slack

ISPRS Int. J. Geo-Inf. 2023, 12(12), 503; https://doi.org/10.3390/ijgi12120503 - 14 Dec 2023

Viewed by 1877

Abstract

Infection of liver flukes (Opisthorchis viverrini) is partly due to their suitability for habitats in sub-basin areas, which causes the intermediate host to remain in the watershed system in all seasons. The spatial monitoring of fluke at the small basin scale [...] Read more.

Infection of liver flukes (Opisthorchis viverrini) is partly due to their suitability for habitats in sub-basin areas, which causes the intermediate host to remain in the watershed system in all seasons. The spatial monitoring of fluke at the small basin scale is important because this can enable analysis at the level of the factors involved that influence infections. A spatial mathematical model was weighted by the nine spatial factors X₁ (index of land-use types), X₂ (index of soil drainage properties), X₃ (distance index from the road network, X₄ (distance index from surface water resources), X₅ (distance index from the flow accumulation lines), X₆ (index of average surface temperature), X₇ (average surface moisture index), X₈ (average normalized difference vegetation index), and X₉ (average soil-adjusted vegetation index) by dividing the analysis into two steps: (1) the sub-basin boundary level was analyzed with an ordinary least square (OLS) model used to select the spatial criteria of liver flukes aimed at analyzing the factors related to human liver fluke infection according to sub-watersheds, and (2) we used the infection risk positional analysis level through machine-learning-based forest classification and regression (FCR) to display the predictive results of infection risk locations along stream lines. The analysis results show four prototype models that import different independent variable factors. The results show that Model 1 and Model 2 gave the most AUC (0.964), and the variables that influenced infection risk the most were the distance to stream lines and the distance to water bodies; the NDMI and NDVI factors rarely affected the accuracy. This FCR machine-learning application approach can be applied to the analysis of infection risk areas at the sub-basin level, but independent variables must be screened with a preliminary mathematical model weighted to the spatial units in order to obtain the most accurate predictions. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research)

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19 pages, 8508 KiB

Open AccessArticle

Influence of the Built Environment on Pedestrians’ Route Choice in Leisure Walking

by Yifu Ge, Zhongyu He and Kai Shang

ISPRS Int. J. Geo-Inf. 2023, 12(9), 384; https://doi.org/10.3390/ijgi12090384 - 19 Sep 2023

Viewed by 1147

Abstract

Exploring the relationship between leisure walking and the built environment will provide an improvement in human health and well-being. It is, therefore, necessary to explore the most relevant scale for leisure walking and how the association between the built environment and leisure walking [...] Read more.

Exploring the relationship between leisure walking and the built environment will provide an improvement in human health and well-being. It is, therefore, necessary to explore the most relevant scale for leisure walking and how the association between the built environment and leisure walking varies across scales. Three hundred volunteers were recruited to wear GPS loggers, and a total dataset of 268 tracks from 105 individuals was collected. The shortest possible routes between starting and ending points were generated and compared to the actual routes using the paired T-test. An improved grid-based buffer approach was proposed, and statistics for the grid cells intersecting the paths were calculated. Grid cells were calculated for six scales: 50 m, 100 m, 200 m, 500 m, 800 m, and 1600 m. The results showed that the actual paths were on average 24.97% longer than the shortest path. The mean, standard deviation, and minimum and maximum values of the built environment variables were all significantly associated with leisure walking. The most relevant spatial scale was found to be the 100 m scale. Overall, the smaller the scale, the more significant the association. Participants showed a preference for moderately compact urban forms, diverse options for destinations, and greener landscapes in leisure walking route choice. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research)

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26 pages, 8186 KiB

Open AccessArticle

Identification of Risk Areas of Dengue Transmission in Culiacan, Mexico

by Susana Román-Pérez, Raúl Aguirre-Gómez, Juan Eugenio Hernández-Ávila, Luisa Basilia Íñiguez-Rojas, René Santos-Luna and Fabián Correa-Morales

ISPRS Int. J. Geo-Inf. 2023, 12(6), 221; https://doi.org/10.3390/ijgi12060221 - 29 May 2023

Viewed by 1622

Abstract

Dengue is a public health problem in more than 100 countries around the world and in virtually the entire region of the Americas, including Mexico. Mosquitoes of the genus Aedes aegypti transmit dengue; its reproduction requires certain geographical, epidemiological, demographic and socioeconomic conditions. [...] Read more.

Dengue is a public health problem in more than 100 countries around the world and in virtually the entire region of the Americas, including Mexico. Mosquitoes of the genus Aedes aegypti transmit dengue; its reproduction requires certain geographical, epidemiological, demographic and socioeconomic conditions. Detailed information on socioeconomic, epidemiological and entomological data is available, but detailed meteorological information is not. The objective of this study was to identify the areas of risk of dengue transmission for each month of the year based on environmental, social, entomological and epidemiological information from 2010 to 2020, in Culiacan, Mexico. LST, NDVI and NDMI were calculated from Landsat 8 satellite images with remote sensing techniques. Additional variables were human population density and overcrowding; mosquito egg density from positive ovitraps; and probable cases of dengue. A descriptive analysis of the study variables and a multiple linear regression analysis were performed to determine the significant variables. In addition, a multicriteria spatial analysis was applied through the AHP technique to identify areas at risk of dengue transmission. The results revealed that the variables NDVI, NDMI and overcrowding were not significant; however, the LST, population density, egg density per positive ovitrap and probable cases were. The highest population in the transmission risk areas was in November, and the highest transmission area was identified in October. In conclusion, it was possible to identify which of the study variables were significant; in addition, monthly maps of risk areas of dengue transmission for Culiacan were obtained. Each geographical area had its own characteristics that influenced, in one way or another, the incidence of dengue, highlighting that the strategies for control of dengue must be specific to each region. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research)

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15 pages, 2718 KiB

Open AccessArticle

The Non-Linear Influence of Built Environment on the School Commuting Metro Ridership: The Case in Wuhan, China

by Jinming Yan, Qiuyu Wan, Jingyi Feng, Jianjun Wang, Yiwen Hu and Xuexin Yan

ISPRS Int. J. Geo-Inf. 2023, 12(5), 193; https://doi.org/10.3390/ijgi12050193 - 06 May 2023

Cited by 2 | Viewed by 1374

Abstract

Although many studies have investigated the non-linear relationship between the built environment and rail patronage, it remains unclear whether this influence is equally applicable to primary and secondary school students due to their physiological characteristics and cognitive limitations. This study applies the GBDT [...] Read more.

Although many studies have investigated the non-linear relationship between the built environment and rail patronage, it remains unclear whether this influence is equally applicable to primary and secondary school students due to their physiological characteristics and cognitive limitations. This study applies the GBDT model to Wuhan student metro swipe data in order to investigate the relative importance and non-linear association of the built environment on the school-commuting metro ridership. The results show that the variable with the greatest predictive power is the number of living service facilities followed by the number of intersections, and the degree of land-use mixture. All of the built environment variables had non-linear associations with the school-commuting ridership, and the greatest attraction to the school-commuting metro ridership occurred when the number of living service facilities was 500, the number of intersections was 36, and the degree of land-use mixture was 0.8. These findings can help planners to prioritize land-use optimization and the effective range of land-use indicators when developing child-friendly rail transport policies. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research)

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16 pages, 1281 KiB

Open AccessArticle

A Tale of Two Cities: COVID-19 Vaccine Hesitancy as a Result of Racial, Socioeconomic, Digital, and Partisan Divides

by Rui Li, Daniel Erickson, Mareyam Belcaid, Madu Franklin Chinedu and Oluwabukola Olufunke Akanbi

ISPRS Int. J. Geo-Inf. 2023, 12(4), 158; https://doi.org/10.3390/ijgi12040158 - 07 Apr 2023

Viewed by 1467

Abstract

The unprecedented COVID-19 pandemic has drawn great attention to the issue of vaccine hesitancy, as the acceptance of the innovative RNA vaccine is relatively low. Studies have addressed multiple factors, such as socioeconomic, political, and racial backgrounds. These studies, however, rely on survey [...] Read more.

The unprecedented COVID-19 pandemic has drawn great attention to the issue of vaccine hesitancy, as the acceptance of the innovative RNA vaccine is relatively low. Studies have addressed multiple factors, such as socioeconomic, political, and racial backgrounds. These studies, however, rely on survey data from participants as part of the population. This study utilizes the actual data from the U.S. Census Bureau as well as actual 2020 U.S. presidential election results to generate four major category of factors that divide the population: socioeconomic status, race and ethnicity, access to technology, and political identification. This study then selects a region in a traditionally democratic state (Capital Region in New York) and a region in a traditionally republican state (Houston metropolitan area in Texas). Statistical analyses such as correlation and geographically weighted regression reveal that factors such as political identification, education attainment, and non-White Hispanic ethnicity in both regions all impact vaccine acceptance significantly. Other factors, such as poverty and particular minority races, have different influences in each region. These results also highlight the necessity of addressing additional factors to further shed light on vaccine hesitancy and potential solutions according to identified factors. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research)

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22 pages, 8792 KiB

Open AccessArticle

Impact of Environmental Exposure on Chronic Diseases in China and Assessment of Population Health Vulnerability

by Zhibin Huang, Chunxiang Cao, Min Xu and Xinwei Yang

ISPRS Int. J. Geo-Inf. 2023, 12(4), 155; https://doi.org/10.3390/ijgi12040155 - 06 Apr 2023

Cited by 1 | Viewed by 2009

Abstract

Although numerous epidemiological studies have demonstrated a relationship between environmental factors and chronic diseases, there is a lack of comprehensive population health vulnerability assessment studies from the perspective of environmental exposure, population sensitivity and adaptation on a regional scale. To address this gap, [...] Read more.

Although numerous epidemiological studies have demonstrated a relationship between environmental factors and chronic diseases, there is a lack of comprehensive population health vulnerability assessment studies from the perspective of environmental exposure, population sensitivity and adaptation on a regional scale. To address this gap, this study focused on six high-mortality chronic diseases in China and constructed an exposure–sensitivity–adaptability framework-based index system using multivariate data. The constructed system effectively estimated health vulnerability for the chronic diseases. The R-square between vulnerability and mortality rates for respiratory diseases and malignant tumors exceeded 0.7 and was around 0.6 for the other four chronic diseases. In 2020, Chongqing exhibited the highest vulnerability to respiratory diseases. For heart diseases, vulnerability values exceeding 0.5 were observed mainly in northern and northeastern provinces. Vulnerability values above 0.5 were observed in Jiangsu, Shanghai, Tianjin, Shandong and Liaoning for cerebrovascular diseases and malignant tumors. Shanghai had the highest vulnerability to endogenous metabolic diseases, and Tibet exhibited the highest vulnerability to digestive system diseases. The main related factor analysis results show that high temperature and humidity, severe temperature fluctuations, serious air pollution, high proportion of middle-aged and elderly population, as well as high consumption of aquatic products, red meat and eggs increased health vulnerability, while increasing per capita educational resources helped reduce vulnerability. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research)

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17 pages, 2360 KiB

Open AccessArticle

Comprehensive Dynamic Influence of Multiple Meteorological Factors on the Detection Rate of Bacterial Foodborne Diseases under Spatio-Temporal Heterogeneity

by Xiaojuan Qi, Jingxian Guo, Shenjun Yao, Ting Liu, Hao Hou and Huan Ren

Int. J. Environ. Res. Public Health 2023, 20(5), 4321; https://doi.org/10.3390/ijerph20054321 - 28 Feb 2023

Cited by 1 | Viewed by 1372

Abstract

Foodborne diseases are a critical public health problem worldwide and significantly impact human health, economic losses, and social dynamics. Understanding the dynamic relationship between the detection rate of bacterial foodborne diseases and a variety of meteorological factors is crucial for predicting outbreaks of [...] Read more.

Foodborne diseases are a critical public health problem worldwide and significantly impact human health, economic losses, and social dynamics. Understanding the dynamic relationship between the detection rate of bacterial foodborne diseases and a variety of meteorological factors is crucial for predicting outbreaks of bacterial foodborne diseases. This study analyzed the spatio-temporal patterns of vibriosis in Zhejiang Province from 2014 to 2018 at regional and weekly scales, investigating the dynamic effects of various meteorological factors. Vibriosis had a significant temporal and spatial pattern of aggregation, and a high incidence period occurred in the summer seasons from June to August. The detection rate of Vibrio parahaemolyticus in foodborne diseases was relatively high in the eastern coastal areas and northwestern Zhejiang Plain. Meteorological factors had lagging effects on the detection rate of V. parahaemolyticus (3 weeks for temperature, 8 weeks for relative humidity, 8 weeks for precipitation, and 2 weeks for sunlight hours), and the lag period varied in different spatial agglomeration regions. Therefore, disease control departments should launch vibriosis prevention and response programs that are two to eight weeks in advance of the current climate characteristics at different spatio-temporal clustering regions. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research)

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14 pages, 1641 KiB

Open AccessArticle

Spatiotemporal Patterns of Adverse Pregnancy Outcomes in Rural Areas of Henan, China

by Jian Chai, Junxi Zhang, Yuanyuan Shi, Panpan Sun, Yuhong Wang, Dezhuan Zhou, Wei Dong, Lifang Jiang and Peng Jia

Int. J. Environ. Res. Public Health 2022, 19(23), 15966; https://doi.org/10.3390/ijerph192315966 - 30 Nov 2022

Viewed by 1173

Abstract

The spatial patterns of adverse pregnancy outcomes (APOs) are complex, vary by place, and remain not entirely clear. This study investigated spatiotemporal patterns of APOs in rural areas of Henan, China. We used data from 1,315,327 singleton pregnancies during 2013–2016 in rural areas [...] Read more.

The spatial patterns of adverse pregnancy outcomes (APOs) are complex, vary by place, and remain not entirely clear. This study investigated spatiotemporal patterns of APOs in rural areas of Henan, China. We used data from 1,315,327 singleton pregnancies during 2013–2016 in rural areas of Henan, China, from the National Free Pre-pregnancy Checkup Program (NFPCP). A spatiotemporal analysis of APOs was conducted based on the time of conception and current address. Results of seasonality decomposed showed a slight decline in the incidence rate of APOs (12.93% to 11.27% in the compound trend) among the participants from 2013 to 2016 and also variation in annual periodicity (peaking in autumn at 12.66% and hitting bottom in spring at 11.16%). Spatial clusters of APOs were concentrated in an intersection band of northwestern to southeastern Henan Province (with a relative risk ratio ranging from 3.66 to 1.20), the northwestern and northern portion for temporal variation (having a trend in the cluster ranged from −6.25% to 83.93). This study provides an overall picture of APOs that presented downward trends over time, seasonal fluctuation, and clustered patterns across space and over time in Henan Province—the most populated province in China. The findings of this study warrant future studies to investigate underlying influential factors of spatial variation of APOs. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research)

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