Table 2.
Summary of the selected articles
| Sl. No. | Authors | Year | Objectives | GIS/Spatial Analysis Methods | Software |
|---|---|---|---|---|---|
| Disease Surveillance (N = 30) | |||||
| 1 | Madhu, B., Srinath, K. M., Rajendran, V., Devi, M. P., Ashok, N. C., & Balasubramanian, S | 2016 | To visualise the spatiotemporal distribution of breast cancer incidences | Thematic mapping and creation of point maps | ArcGIS |
| 2 | Sabesan, S., Palaniyandi, M., Das, P. K., & Michael, E. | 2000 | To map lymphatic filariasis in India | Probability mapping | MapInfo |
| 3 | Mutheneni, S. R., Mopuri, R., Naish, S., Gunti, D., & Upadhyayula, S. M. | 2018 | To understand the epidemiology and spatial distribution of dengue fever | Spatial mapping, Getis-Ord Gi* statistic, SoM | ArcGIS |
| 4 | Mala, S., & Jat, M. K | 2019 | To study the spatiotemporal distribution of dengue fever | Kernel density, directional distribution, Spatial Scan Statistic | ArcGIS |
| 5 | Raghava, M. V., Prabhakaran, V., Jayaraman, T., Muliyil, J., Oommen, A., Dorny, P., Vercruysse, J., & Rajshekhar, V | 2010 | To map spatial clusters of Taenia Solium infections | Spatial mapping, cluster analysis | SaTScan, ArcGIS |
| 6 | Sowmyanarayanan, T. V., Mukhopadhya, A., Gladstone, B. P., Sarkar, R., & Kang, G | 2008 | To investigate the disease pattern of viral hepatitis in children | Spatial mapping, cluster analysis | Arc View GIS, SaTScan |
| 7 | Sarkar, R., Prabhakar, A. T., Manickam, S., Selvapandian, D., Raghava, M. V., Kang, G., & Balraj, V | 2007 | To study outbreaks of acute diarrhoeal disease | Spatial mapping, cluster analysis | Arc View GIS, SaTScan |
| 8 | Mandal, R., Kesari, S., Kumar, V., & Das, P | 2018 | To study spatiotemporal dynamics of visceral leishmaniasis cases | Spatial mapping, overlay analysis Moran’s I Index | ArcGIS |
| 9 | D’Mello, M. K., Badiger, S., Kumar, S., Kumar, N., D’Souza, N., & Purushothama, J. | 2022 | To geospatially analyse hotspots of diarrheal cases | Moran’s I Index, Getis-OrdGi* statistic | QGIS, GeoDa |
| 10 | Felix, C., Kaur, P., Sebastian, I. A., Singh, G., Singla, M., Singh, S., Samuel, C. J., Verma, S. J., & Pandian, J. D. | 2021 | To identify high-incidence areas of transient ischemic attack for stroke prevention interventions | Spatial mapping, Moran’s I Index, Getis-OrdGi* statistic | ArcGIS |
| 11 | Gupta, A. K., & Santhya, K. G. | 2020 | To understand proximal and contextual correlates of childhood stunting | Moran’s I Index, ordinary least square (OLS) regression, spatial error model (SEM) | GeoDa |
| 12 | Krishnamoorthy, Y., Majella, M. G., Rajaa, S., Bharathi, A., & Saya, G. K. | 2021 | To assess the spatial pattern and determinants of HIV | Moran’s I Index | GeoDa |
| 13 | Krishnamoorthy, Y., Rajaa, S., Verma, M., Kakkar, R., & Kalra, S. | 2022 | To find the spatial pattern and determinants of diabetes mellitus | Moran’s I Index, Getis-OrdGi* statistic, ordinary least square (OLS) regression | GeoDa |
| 14 | Kumar, C., Singh, P. K., & Rai, R. K. | 2012 | To study under five mortality rates at the district level | Buffer, Moran’s I Index, Getis-OrdGi* statistic, ordinary least square (OLS) regression, spatial error model (SEM) | GeoDa, ArcGIS |
| 15 | Garg, S., Dewangan, M., & Barman, O. | 2020 | To study malaria amongst symptomatic and asymptomatic pregnant women | Spatial mapping, correlation | Map Window |
| 16 | Nath, M. J., Bora, A. K., Yadav, K., Talukdar, P. K., Dhiman, S., Baruah, I., & Singh, L. | 2013 | To identify the malaria hot spots | GPS survey, spatial mapping | ArcGIS |
| 17 | Yadav, K., Nath, M. J., Talukdar, P. K., Saikia, P. K., Baruah, I., & Singh, L. | 2012 | To understand the geographical distribution of malaria | GPS survey, spatial mapping | ArcGIS |
| 18 | Rai, P. K., Nathawat, M. S., & Rai, S. | 2014 | To malaria-susceptibility modelling for predicting malaria occurrence | GPS survey, thematic mapping, malaria inventory mapping | Ilwis, ArcGIS, ERDAS Imagine |
| 19 | Qayum, A., Arya, R., Kumar, P., & Lynn, A. M. | 2015 | To identify the malaria hot spots | Thematic mapping, IDW, overlay analysis | ArcGIS |
| 20 | Singh, P. S., & Chaturvedi, H. K | 2021 | To map dengue cases and to identify the hotspots | IDW, Getis-OrdGi* statistic | ArcGIS |
| 21 | Mopuri, R., Mutheneni, S. R., Kumaraswamy, S., Kadiri, M. R., Upadhyayula, S. M., & Naish, S. | 2019 | To study the spatiotemporal distribution of malaria | Moran’s, I Index, Getis-OrdGi* statistic, cluster analysis | ArcGIS, SaTScan |
| 22 | Oinam, B., Anand, V., & Kajal, R. | 2021 | To detect the hotspot regions of HIV | Getis-OrdGi* statistic, ordinary least square (OLS) | ArcGIS |
| 23 | Tyagi, N., & Sahoo, S. | 2019 | To identify areas of encephalitis and to create an encephalitis risk model | Thematic mapping, Map algebra | ArcGIS |
| 24 | Sabesan, S., Raju, K. H. K., Subramanian, S., Srivastava, P. K., & Jambulingam, P. | 2013 | To find areas with lymphatic filariasis transmission risk | Geo-Environmental Risk model (GERM) & Standardised Filariasis Transmission Risk Index (SFTRI) based mapping, cluster analysis | STATA |
| 25 | Joseph, P. V., Balan, B., Rajendran, V., Prashanthi, D., & Somnathan, B. | 2015 | To study small area clustering of diseases and to find probable chances of high disease prevalence | Probability mapping | ArcGIS |
| 26 | Das, A., Ghosh, S., Das, K., Basu, T., Dutta, I., & Das, M. | 2021 | To examine the impact of living environment deprivation on COVID-19 hotspots | Getis-OrdGi* statistic, Geographically Weighted Principal Component Analysis (GWPCA), data regression models | ArcGIS, R |
| 27 | Das, S. K., & Bebortta, S. | 2022 | To understand the role of GIS in tracking the spread of COVID-19 | Choropleth maps, heatmap analysis, cluster analysis, Getis-OrdGi* statistic | QGIS |
| 28 | Murugesan, M., Venkatesan, P., Kumar, S., Thangavelu, P., Dash, N., John, J., Castro, M., Manivannan, T., Mohan, V. R., & Rupali, P. | 2022 | To analyse patterns of spread and hotspots of COVID-19 | Spatial mapping, density map | ArcGIS |
| 29 | Soni, P., Gupta, I., Singh, P., Porte, D. S., & Kumar, D. | 2022 | To evaluate the occurrence and distribution pattern of COVID-19 | Analytical Hierarchy Process (AHP), overlay analysis | ArcGIS |
| 30 | Wani, M. A., Kawoosa, W., & Mayer, I. A. | 2019 | To conduct a block-level analysis of respiratory diseases | Zonation map, hotspot mapping | ArcGIS |
| Risk Assessment (N = 14) | |||||
| 31 | Bohra & Andrianasolo | 2001 | To evaluate spatial relationships between sociocultural practices and the incidences of dengue fever | IDW, spatial modelling | GIS |
| 32 | Ali, S., Ali, H., Pakdel, M., Ghale Askari, S., Mohammadi, A. A., & Rezania, S. | 2021 | To assess the risk due to exposure to fluoride concentration in drinking water | IDW, spatial mapping | ArcGIS |
| 33 | Shukla, S., Saxena, A., Khan, R., & Liu, P. | 2021 | To assess the overall groundwater quality and the spatial distribution of the physicochemical parameters | IDW, spatial mapping | ArcGIS |
| 34 | Singh, Rani., Upreti, P., Allemailem, K. S., Almatroudi, A., Rahmani, A. H., & Albalawi, G. M. | 2022 | To assess the quality of groundwater for drinking purposes | IDW, spatial mapping | ArcGIS |
| 35 | Vikrma, A., & Sandhu, H. A. S. | 2022 | To conduct a risk assessment based on the quality of groundwater | IDW, spatial mapping | ArcGIS |
| 36 | Gugulothu, S., Rao, N. S., Das, R., Duvva, L. K., & Dhakate, R | 2022 | To assess the sources of low groundwater quality and to understand the human health risk | IDW, spatial mapping | ArcGIS |
| 37 | Singh, A., Raju, A., Chandniha, S. K., Singh, L., Tyagi, I., Karri, R. R., & Kumar, A. | 2022 | To assess the human risk exposure due to consumption of groundwater | Overlay analysis, mapping | ArcGIS |
| 38 | Ananth, M., Rajesh, R., Amjith, R., A L, A., Valamparampil, M. J., Harikrishnan, M., Resmi, M. S., Sreekanth, K. B., Sara, V., Sethulekshmi, S., Prasannakumar, V., Deepthi, S. K., Jemin, A. J., Krishna, D. S., Anish, T. S., Insija, I. S., & Nujum, Z. T. | 2018 | To assess the sanitary condition and water quality of household wells | DGPS survey, IDW | ArcGIS |
| 39 | Ravindra, K., & Mor, S. | 2019 | To assess risk due to distribution of arsenic and selected heavy metals in groundwater | Kriging interpolation | ArcGIS |
| 40 | Sargaonkar, A., Nema, S., Gupta, A., & Sengupta, A. | 2010 | To assess the risk of contamination of water in water distribution systems | Spatial and thematic mapping | AutoCAD, ArcGIS |
| 41 | Bidhuri, S., & Jain, P. | 2018 | To analyse the spatial risk of waterborne diseases | GPS, analytical hierarchical process (AHP), IDW, buffer | GIS |
| 42 | Singh, D., Dahiya, M., & Nanda, C. | 2022 | To analyse the variability in air pollutants, AQI and ER% in three scenarios, viz. prelockdown, during lockdown, postlockdown | IDW, spatial mapping | GIS |
| 43 | Kanga, S., Meraj, G., Sudhanshu, Farooq, M., Nathawat, M. S., & Singh, S. K. | 2021 | To analyse the risk of COVID-19 infection | Spatial mapping, overlay analysis, kriging interpolation | ArcGIS |
| 44 | Nath, B., Majumder, S., Sen, J., & Rahman, M. M. | 2021 | To study the degree of risks associated with COVID-19 infections | Cosine Similarity Index (CSI) by similarity search tool | ArcGIS Pro |
| Healthcare Access (N = 14) | |||||
| 45 | Pramod Nayak, P., Mitra, S., Pai, J. B., Vasthare Prabhakar, R., & Kshetrimayum, N. | 2022 | To map distribution and accessibility to oral health care | Geo-coding, spatial mapping, kernel density estimation | QGIS |
| 46 | Ghosh, A., & Mistri, B. | 2020 | To investigate spatial disparities in the provision of rural health facilities | Buffer, Euclidean distance, IDW | ArcGIS |
| 47 | Parvin, F., Ali, S. A., Hashmi, S. N. I., & Khatoon, A. | 2020 | To study accessibility and site suitability for healthcare services | Kernel density, Euclidean distance, buffer, weighted linear combination method, site suitability analysis | ArcGIS |
| 48 | Rekha, R. S., Wajid, S., Radhakrishnan, N., & Mathew, S. | 2017 | To study the distribution of healthcare facilities | Three-step floating catchment area method, GPS survey, Origin-Destination cost function, site suitability analysis | ArcGIS |
| 49 | Ranga, V., & Panda, P. | 2014 | To analyse spatial access to inpatient rural healthcare | Three-step floating catchment area method, Euclidean distance | QGIS, R |
| 50 | Vadrevu, L., & Kanjilal, B. | 2016 | To study spatial access to maternal health services | Enhanced two-step floating catchment area method | ArcGIS, STATA |
| 51 | Roberts, T., Shiode, S., Grundy, C., Patel, V., Shidhaye, R., & Rathod, S. D. | 2019 | To assess the relation between distance to health services and treatment-seeking for depressive symptoms | Network analysis | ArcGIS |
| 52 | Oinam, B., Oinam, J., & Kajal, R. K. | 2020 | To assess health coverage of healthcare facilities | Thematic mapping, spatial catchment analysis, scaling-up analysis | ArcGIS, AccessMod |
| 53 | Dutta, B., Das, M., Roy, U., Das, S., & Rath, S. | 2022 | To analyse the spatial pattern of healthcare facilities and to determine the possible sites for the provision of healthcare facilities | GPS survey, overlay analysis, network analysis, analytical hierarchy process (AHP) and ordinary least square (OLS) | ArcGIS |
| 54 | Singh, N., Patel, R., & Chauhan, S | 2021 | To investigate the utilisation of maternal health care services | Ordinary least square (OLS) and spatial error model (SEM), Moran’s I Index | ArcGIS |
| 55 | Kumar, N. | 2004 | To study access to and locational efficiency of health services | Location-allocation models (LAM), Euclidean distance | ArcInfo |
| 56 | Dare, A. J., Ng-Kamstra, J. S., Patra, J., Fu, S. H., Rodriguez, P. S., Hsiao, M., Jotkar, R. M., Thakur, J. S., Sheth, J., & Jha, P. | 2015 | To analyse access to surgical care regarding deaths from acute abdominal conditions | Geo-coding, Clustering analysis, Getis-OrdGi* statistic, kriging, Euclidean distance | SaTScan |
| 57 | Verma, V. R., & Dash, U. | 2020 | To measure accessibility and model spatial coverage of public healthcare networks. | Thematic mapping, Euclidean distance, overlay analysis, accessibility analysis, geographical coverage analysis | AccessMod, ArcGIS |
| 58 | Sabde, Y., Diwan, V., Mahadik, V. K., Parashar, V., Negandhi, H., Trushna, T., & Zodpey, S. | 2020 | To analyse the geographical distribution of medical schools | Spatial mapping, Near neighbourhood analysis | QGIS, ArcGIS |