|Year : 2016 | Volume
| Issue : 1 | Page : 51-58
Application of GIS in public health in India: A literature-based review, analysis, and recommendations
Marilyn O'Hara Ruiz1, Arun Kumar Sharma2
1 Director, Department of Pathobiology, Geographical Information Systems and Spatial Epidemiology Lab, University of Illinois, Urbana-Champaign, USA
2 Visiting Scholar, Department of Pathobiology, Geographical Information Systems and Spatial Epidemiology Lab, University of Illinois, Urbana-Champaign, USA
|Date of Web Publication||23-Feb-2016|
Arun Kumar Sharma
Department of Community Medicine, University College of Medical Sciences, Dilshad Garden, Delhi - 110 095
Source of Support: None, Conflict of Interest: None
| Abstract|| |
The implementation of geospatial technologies and methods for improving health has become widespread in many nations, but India's adoption of these approaches has been fairly slow. With a large population, ongoing public health challenges, and a growing economy with an emphasis on innovative technologies, the adoption of spatial approaches to disease surveillance, spatial epidemiology, and implementation of health policies in India has great potential for both success and efficacy. Through our evaluation of scientific papers selected through a structured key phrase review of the National Center for Biotechnology Information on the database PubMed, we found that current spatial approaches to health research in India are fairly descriptive in nature, but the use of more complex models and statistics is increasing. The institutional home of the authors is skewed regionally, with Delhi and South India more likely to show evidence of use. The need for scientists engaged in spatial health analysis to first digitize basic data, such as maps of road networks, hydrological features, and land use, is a strong impediment to efficiency, and their work would certainly advance more quickly without this requirement.
Keywords: Implementation of Geographic Information Systems (GIS), India, innovative technology, spatial data infrastructure, spatial epidemiology
|How to cite this article:|
Ruiz MO, Sharma AK. Application of GIS in public health in India: A literature-based review, analysis, and recommendations. Indian J Public Health 2016;60:51-8
|How to cite this URL:|
Ruiz MO, Sharma AK. Application of GIS in public health in India: A literature-based review, analysis, and recommendations. Indian J Public Health [serial online] 2016 [cited 2021 Mar 2];60:51-8. Available from: https://www.ijph.in/text.asp?2016/60/1/51/177308
| Introduction|| |
Geographic Information Systems (GIS) and the accompanying methods and data have been adopted increasingly in diverse health-related domains and national settings with the goal of improved response to public health problems. Complexities in understanding public health issues require interdisciplinary efforts. The spatial perspective can be of particular importance because the spatial patterns of distribution of diseases, both infectious and noninfectious, help us understand the dynamics of transmission and spatial determinants of the diseases. The focus of this review is to examine how the uses of geographic methods to address public health issues in India have developed over time, to assess the current status of their use, and to evaluate the path forward.
About 17% of the world's people resided in India in 2012, and public health challenges are commensurate with its size. India bears the world's largest burden of dengue,  has a high potential for emerging zoonotic diseases,  and continues to face significant mortality from pneumonia and diarrheal disease.  At the same time, chronic conditions such as cancer and diabetes increasingly affect people of all income levels.  Many of these conditions are closely related to environmental conditions and spatial parameters, so geographic methods including GIS can be central to work related to understanding the spatial and environmental determinants of diseases.
In the past, descriptive geographical epidemiology of diseases was restricted to describing their statewise or regional prevalence, and detailed analysis of epidemiological data at a local level was rarely carried out. For example, the distribution of kala-azar in Bihar  and that of lymphatic filariasis in coastal regions  indicate that spatial factors contribute to their transmission. Similarly, Kyasanur forest disease is found almost exclusively in South India.  Historically, these types of observations were satisfying achievements of medical geography and field epidemiology, but with the increased availability of spatial data and technologies, it is possible to measure and assess scientifically the specific relationships between the environmental factors and health outcomes to better target interventions.
The use of geospatial technologies and spatiotemporal epidemiological tools is increasing around the world as a means to understand the dynamics of infectious disease transmission and noncommunicable disease distribution, but its usage in India is relatively restricted in spite of its demonstrated utility. Here, we review the published scientific literature to evaluate a cross section of examples of how geographic technologies and modern spatial statistical methods supported by digital data are being used to address public health concerns in India, to identify challenges faced in the application of GIS in India and to propose some possible solutions.
| Materials and Methods|| |
For the purpose of the current review, our objective was to focus on an assessment of how public health and epidemiological research in India have been approached from a geographic perspective and to consider the impediments to more complete use of these methods and approaches. As such, we considered peer-reviewed literature published between January 1980 and January 2003 that was indexed by the National Center for Biotechnology Information on PubMed.  By focusing on PubMed, we sought information sources where the primary focus of the analysis was on health rather than on the geographical methods that might be used. Through a systematic consideration of the health domain, the spatial techniques, and the institutional affiliations of the authors, we developed a synthesis of examples of and progress and obstacles to the use of geographical approaches to help improve public health conditions in India.
From our initial search of literature, we identified 177 titles written in English for review based on systematic combinations of the terms "India," "Geographic Information System," "spatial epidemiology," "spatial" "geographic," "community health," and "public health." Of those, we eliminated 64 of the papers after review of the content of the titles and abstracts. The thirty-three papers that were eliminated lacked at least one of these attributes: Availability of the full text (7 titles); a clear focus on health (3 titles); the study area was not focused on India (6 titles); and the geographic technique deemed sufficiently central (17 titles). In the remaining 31 excluded articles, GIS or mapping were mentioned as a recommended technique but the paper did not include any detailed description. The 113 remaining titles were subjected to a full text review (see Appendix A for the list of these titles). While this number of titles is not large, our intention here was to review a representative, rather than an exhaustive, set of papers.
| Results and Discussion|| |
Upon evaluation of the general content of each paper, we determined that some papers were more empirically oriented, while others were focused on the benefits of adoption of GIS or issues related to implementation. The empirically oriented papers, in which a spatial technique was used to analyze data, were classified further into two groups according to the complexity of the analysis and deemed either "descriptive" or "complex" [Table 1]. Fifty-four of the 113 papers that had a clear original analysis component were deemed to be of lower complexity (descriptive) from a computational or empirical perspective, while 44 papers were more complex. The descriptive papers included examples of comparing place attributes, mapping a spatial distribution, simple interpolation to visualize data, and incorporating the concept of proximity. More complex papers used multiple data overlays and multivariate statistical analysis. When authors presented multiple analytical approaches, the most complex aspect guided placement in the specific category. The third group comprised 15 papers that included reviews of spatial technology or suggested examples of use in specific domain areas.
Temporally, 96 of the 113 papers reviewed (85%) were published between January 1980 and January 2003. The average number of publications per year increased from 2008 onward [Figure 1]. Of the three groups, more descriptive papers dominated the earlier years, while more computationally complex analyses were seen from 2003 onward. Papers that focused on general principles related to adoption and implementation of digital spatial technologies were published only after 1998, but were less represented after 2012.
The papers were found to be focused on three primary types of public health domains. These included infectious diseases, noninfectious diseases, and other ancillary health topics. Papers on infectious disease comprised 64% of the total, while noninfectious disease made up 14%, and other health-related issues such as access to health facilities represented 22% of the papers [Table 2]. While infectious disease studies were dominant overall, the study of noninfectious conditions has increased in recent years, and it is an area where continued growth is anticipated. Similarly, the health care facility access and health management domains were gradually added over the years.
|Table 2: The number of papers for each of the public health domains represented|
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Examples by type of geographical applications
Empirical papers of lower complexity
Among the descriptive papers, Kumar and Quinn's is a good example.  It focused on the importance of place variability for preparedness planning for a potential influenza pandemic. They argued that effective planning should be sensitive to the many different public health contexts and populations of different places. The authors did not include maps in their analysis, but they provided a well-conceived model of health inequalities and with the proper data, it could be operationalized in maps and extended to statistical analysis for planning and evaluation purposes. In other examples of more descriptive analyses, the use of a map to reveal the distribution of novel data or the colocation of two or more distributions represented the most frequent topic of all paper types, with 29 papers in this class. In Ujjain district in Madhya Pradesh, for example, Deshpande et al. demonstrated the benefit of geocoding and mapping the locations of both public and private health care providers to assess regional access to health care.  While a database was available that identified public providers, the location of private health care providers was only possible from an original survey of over 2000 private providers. If the providers from the public health system had been the only group considered, the ubiquity and availability of the private providers would have been overlooked. However, among private providers, there was considerable variation with respect to their qualifications, approach to treatment, and spatial distribution. For another example, Kauhl et al. used emergency call data of acute undifferentiated fever for syndromic surveillance to detect potential outbreaks in India. 
In several of the analyses where mapping was a primary function, the authors used a more GIS-intensive but still relatively descriptive approach by interpolating values from points to better visualize the spatial distribution of some phenomenon. The paper by Rejith et al. illustrates this, with inverse distance-weighted interpolation applied to water quality values of pH, cadmium, and fecal coliform.  Though a GIS was used in this paper, the analysis method was considered more descriptive in our review because the outcome was a form of visualization rather than a more model-oriented approach.
A third group of descriptive papers was made up of the 11 papers related to proximity to or distance from some event, facility, or other item. A study published in 1983 by V. Kumaran was an example.  The analysis assessed location-based efficiency of health facilities considering both travel time based on distance and optimizing the size of the population served. The analysis predated most use of geospatial digital techniques and would have benefited from having access to those. A more recent application of distance analysis can be found in a publication by Dwibedi et al. where the focus was on chikungunya, a mosquito-borne virus.  The written description of rapid diffusion of the virus emphasized geography and would have been more effective with a map of the region.
Empirical papers of higher complexity
Among the more computationally and analytically complex papers, 11 of them used weighted overlays to determine areas with the joint probability of having a characteristic. An early example published in 1999 by Srivastava et al. considered areas with a high annual parasite index that would indicate risk for malaria compared to a landscape-based overlay of water, soil, and elevation characteristics.  In a similar manner, Avtar et al. collected data on elevation, rainfall, soils, and vegetation to create areas of potential for insufficient water.  A number of these overlay studies used remotely sensed sources to create data. From the imagery, authors derived special features such as salt-affected soils  or dynamic and changing features such as floods and land cover.  Five of these papers used empirical spatial cluster analysis. All five of these investigations of spatial or temporal disease clustering made use of the freely available SaTScan program  and four of the five papers were led by authors at the Christian Medical College in Tamil Nadu, indicating both a narrow adoption of this approach and its potential usefulness when scientists have training in the technology.
A final set of papers that relied on more complex spatial analysis were the 23 papers that used a multivariate statistical model, including generalized estimating equations, spatial regression, negative binomial regression, or other statistical approaches. Multilevel modeling was used by Ackerson et al. to examine the relationship between neighborhood characteristics and rates of overweight and underweight persons in India.  Higher-income neighborhoods were found to have both more overweight and fewer underweight individuals. This paper also demonstrated the usefulness of a multilevel approach, which has been promoted as appropriate for the geographical analysis of data at multiple spatial scales.  Sur et al. also addressed the analysis of individuals and their environment, but they developed two separate models, with generalized estimating equations serving to fit both the individual data and neighborhood-level characteristics for the separate models of typhoid and paratyphoid fever at different scales in a Kolkata slum area.  In this same study, the authors described the extensive data collection required to determine detailed disease rates needed for the Kolkata analysis, revealing the level of effort needed to carry out infectious disease studies in the absence of surveillance records. Sabesan et al. used logistic regression analysis to identify the relationship between Standardized Filarial Transmission Risk Index (SFTRI) and the endemic status of an area. An empirical semivariogram of the residuals from the logistic regression model was used to examine whether there was any spatial correlation that could explain the remaining variation. However, they concluded that no spatial correlation existed and it was attributed to large distances between different locations in their study.  As a final example, Ranga et al. measured spatial access to inpatient health care in northern rural India using a three-step floating catchment area (3SFCA) method, based on discrete distance decay. 
Papers focused on GIS adoption
Here we examined papers that stated the need for or demonstrated the utility of adopting GIS techniques in health-related research. Authors from particular domain areas have summarized the kinds of spatial data and technologies helpful to better understand disease transmission or prevention from the perspective of that domain. Malaria research, for example, may benefit from maps of the many aspects of malaria epidemiology, including vector locations, case locations, control program outreach areas, or estimates of larval habitat.  Najafabadi and Pourhassan (2011) illustrated the use of visualization, data integration, spatial analysis, and spatial modeling for cancer research.  The value of integrating GIS with other information technology was also emphasized with examples from disaster response,  disease surveillance systems,  and medical records systems. 
Examples from the public health domain
In terms of the health domain tackled by the papers, the majority (63%) were focused on infectious disease and the largest group of these was papers about vector-borne diseases (VBD), including malaria, dengue, filariasis, and Japanese encephalitis [Table 2]. Water-related papers were likewise well represented. Rejith et al., for example, developed a water quality index for a village in the state of Kerala after water samples indicated high levels of cadmium and fecal coliform, as well as pH levels outside the recommendations for drinking water.  The analysis of health care facilities showed the strongest emphasis among papers with a noninfectious disease focus, but they also tended to be more descriptive. Three of the 15 titles in this group of papers were classified as being in the more complex analysis group. One of the papers used a location-allocation model to measure regional inequalities in health facility access.  Noncommunicable diseases and health issues such as maternal and child health were not well represented among the papers, with only 16 of 113 papers in this group. One example is Kumar et al., who used geospatial analysis on secondary data to identify factors responsible for district-level variations in the mortality of children under 5 years of age after controlling for biophysical and geographical variables.  One single paper on cancer was identified, with an emphasis on how GIS could improve cancer research.  Similarly, one paper used geospatial analysis to identify the distribution of tobacco smoking and of alcohol consumption in India. 
Institutions and collaborations
Having determined the general content of the papers, we then considered the characteristics of the authors. In particular, we evaluated the geographical distribution of the institutional affiliations among the authors, with first authors considered representative and coauthors considered in a secondary manner. The institutional home of about three-fourths (81 of 113) of the papers' first authors was located within India. Of the 32 papers that listed first authors with institutional homes outside India, most of the authors were from North America (15 titles) or a European nation (13 titles). Other papers were from Iran, Thailand, and Australia [Table 3]. In about one-third of the papers (37 titles), we found international collaboration as evidenced by the fact that the first author was from a different nation from at least one other coauthor. Evidence of international collaboration was most likely with European or Canadian first authors, with 9 of 10 papers in this group showing such evidence. This compares to 15 of 81 with Indian first authors and 3 of 10 with first authors from the United States of America (USA).
|Table 3: Nation of first authors' institutional home and evidence of international collaboration|
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For those authors with an institution located within India, 30% were from either Tamil Nadu state or the National Capital Territory of Delhi. Overall, the majority of the papers (63 papers) had a first author from an institution that focused on medicine or public health. The next largest group (31 papers) was from a university, but not from a medical sciences focus. Fourteen of the papers were from institutions focused on technology or engineering, and just five titles were from first authors in some other governmental agency. Papers of increasing complexity were published in more recent years as compared to in the past, thereby suggesting that use of GIS systems picked up off late [Table 4].
Challenges and future options
Some specific difficulties in implementation of geospatial technologies were described in the reviewed papers. Many of these difficulties are common to new applications of digital geographic methods regardless of the nation in question, but some may be of particular concern when data from India are required. A main issue is the limited availability of spatial data. Without high-quality spatial data at the temporal and spatial resolution needed, the software and hardware investments for GIS are useless. For Indian scientists who require high-quality spatial data, this is an especially great concern. Many of the empirical studies reviewed here, for example, required that road networks and hydrological features be scanned and georeferenced from paper topographic maps. Authors also noted the need to digitize basic ward, district, or other administrative boundaries prior to carrying out their analysis. In addition, the authors' institutions often lack a means to share the newly digitized data, as the majority of the examples in the published papers were not carried out in a setting that would provide operational support for maintaining and making such datasets available to others.
Admittedly, research goals for small study area studies may require the collection of original data regardless of the general availability of public data. Road networks, however, are particularly important, and not easily developed by individual research groups. Besides being a basic framework for a region, roads are critical measures of access, for example.  The dynamic nature of road features often renders information digitized from paper maps out of date. Paper maps are also limited due to restrictions linked to map scale. The lack of digital street files is thus a particularly important need shared by many groups, but not easily overcome by individual effort.
Access to geographical data has been restricted in India due to concerns about national security, despite a general right to information under the Constitution.  As many other nations move forward to provide more open access to geographical data, India may benefit from legislative and policy models for data use developed in other places. When the health-related studies of an area necessitate creation of these base maps, this takes time away from and may even degrade the quality of analysis of the disease or condition of concern. The digitizing of basic data is a time-consuming effort, and we suggest that additional public funding of such efforts to benefit everybody can be critical in creating an openly available geospatial infrastructure.
The Survey of India (SOI), established in 1767, is a logical partner in making basic digital map layers available, but the SOI has only recently been "mandated to take a leadership role in liberalizing access of spatial data to user groups without jeopardizing national security" (SOI, 2013). The Indian National Atlas More Details and Thematic Mapping Organisation is moving to create digital approaches to mapping, including health indicators, and this may help expand options for digital data.  The use of remotely sensed imagery for land cover and other mapping is a logical solution for creating digital maps, including land cover and land use; but again, this task may often be better done collectively. The Indian Remote Sensing satellites were used in several of the papers under review. ,, The creation of consistently coded land cover products at regular temporal increments would be another digital product that would be a benefit to geospatial health applications. The encouragement of crowd-sourced or citizen science-volunteered geographic data may also help to alleviate some of the need for publicly available data.  The Open Street Map project has been increasingly active, for example, and has galvanized efforts to increase the geospatial infrastructure in areas where it is needed.
The status of academic geography in India might explain the slow adoption of spatial analysis for health and the need for national agencies to take a lead in making geographical data available for scientific use. The 78 university Geography departments in India produced 2399 doctoral-level graduates between 1857 and 2001, but many of those graduates became teachers without a strong research focus.  The SOI is a potential employer of geographers, but it has a preponderance of employees coming from military backgrounds, with surveyors being dominant and very few geographers on staff. A lack of professional geographers and planners who can contribute to promoting GIS in influential government and academic institutions, could be corrected by a stronger scientific geographic tradition. It is also noteworthy that the Indian Institutes of Technology have not generally included geography programs.  The type of combined expertise found in many Geography departments in Europe and the USA would include geographical in,formation science, spatial analysis and both social and physical sciences emphases. This type of intellectual environment is conducive to the development of both data and geographical applications and can be very important in helping other disciplines, such as the medical sciences, to adopt them in a more advanced manner. Such departments are not the norm in India. ,
Several other issues were noted in the papers. The cost of software, hardware, and training remain impediments to progress, with a lack of communications options such as reliable Internet, making data sharing less efficient.  Many of the examples of GIS adoption in the papers reviewed used application examples based on implementation in other nations, which is an indirect indication of such impediments. For example, Kandwal et al. stressed the usefulness of using GIS for better surveillance and control of human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS), but included only examples from Africa and other places.  The lack of good disease surveillance data makes geographic analysis for health difficult in general.  The relatively low number of papers by first authors from Indian institutions in North India with its dense population and numerous health needs reflects a divide in India of technologically adaptive and excluded regions,  but perhaps this difference may also be accounted for due to variability in academic culture. Any effort to increase capacity for geospatial technologies in health should take these various issues into account. The cost of commercial software may be mitigated by the use of open source and free software, including, GeoDa,  SaTScan,  CrimeSat  Google Earth,  and QGIS  for mapping and spatial analysis. Programs for statistical analysis, including R and EpiInfo 7,  also include spatial mapping and analysis features.
| Conclusions|| |
In India, the adoption and use of digital spatial methods for health has lagged somewhat compared to technology and science opportunities in the society at large. The public health and medical community along with the universities and technology-oriented institutes have led the adoption of geospatial technologies and methods to address a broad range of health applications. This group, however, is not well positioned to provide open data access to key spatial features, such as roads, stream networks, and socioeconomic data, and may lack academic training that emphasizes spatial thinking. The provision of these data and a heightened awareness of spatial analysis tools will be especially valuable to resource-poor areas so that rational information and analysis can be more fully included in planning and response to India's health challenges.
Stephanie Mundis provided assistance with collecting and organizing the review papers and entering data into the database for analysis.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al
. The global distribution and burden of dengue. Nature 2013;496:504-7.
Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, et al
. Global trends in emerging infectious diseases. Nature 2008;451:990-3.
Lahariya C, Sudfeld CR, Lahariya D, Tomar SS. Causes of child deaths in India, 1985-2008: A systematic review of literature. Indian J Pediatr 2010;77:1303-11.
Bhojani U, Beerenahalli TS, Devadasan R, Munegowda CM, Devadasan N, Criel B, et al
. No longer diseases of the wealthy: Prevalence and health-seeking for self-reported chronic conditions among urban poor in southern India. BMC Health Serv Res 2013;13:306.
Bora D. Epidemiology of visceral leishmaniasis in India. Natl Med J India 1999;12:62-8.
Sabesan S, Palaniyandi M, Das PK, Michael E. Mapping of lymphatic filariasis in India. Ann Trop Med Parasitol 2000;94:591-606.
Pattnaik P. Kyasanur forest disease: An epidemiological view in India. Rev Med Virol 2006;16:151-65.
Kumar S, Quinn SC. Existing health inequalities in India: Informing preparedness planning for an influenza pandemic. Health Policy Plan 2012;27:516-26.
Deshpande K, RaviShankar, Diwan V, Lonnroth K, Mahadik VK, Chandorkar RK. Spatial pattern of private health care provision in Ujjain, India: A provider survey processed and analysed with a geographical information system. Health Policy 2004;68:211-22.
Kauhl B, Pilot E, Rao R, Gruebner O, Schweikart J, Krafft T. Estimating the spatial distribution of acute undifferentiated fever (AUF) and associated risk factors using emergency call data in India. A symptom-based approach for public health surveillance. Health Place 2015;31:111-9.
Rejith PG, Jeeva SP, Vijith H, Sowmya M, Hatha AA. Determination of groundwater quality index of a highland village of Kerala (India) using geographical information system. J Environ Health 2009;71:51-8.
Kumaran VV. Location, use, and locational efficiency of health facilities in a Madras neighbourhood. Geogr Med 1983;13:53-65.
Dwibedi B, Sabat J, Mahapatra N, Kar SK, Kerketta AS, Hazra RK, et al
. Rapid spread of chikungunya virus infection in Orissa: India. Indian J Med Res 2011;133:316-21.
Srivastava A, Nagpal BN, Saxena R, Sharma VP. Geographic information system as a tool to study malaria receptivity in Nadiad taluka, Kheda district, Gujarat, India. Southeast Asian J Trop Med Public Health 1999;30:650-6.
Avtar R, Singh CK, Shashtri S, Mukherjee S. Identification of erosional and inundation hazard zones in Ken-Betwa river linking area, India, using remote sensing and GIS. Environ Monit Assess 2011;182:341-60.
Singh G, Bundela DS, Sethi M, Lal K, Kamra SK. Remote sensing and geographic information system for appraisal of salt-affected soils in India. J Environ Qual 2010;39:5-15.
Pfeiffer C, Glaser S, Vencatesan J, Schliermann-Kraus E, Drescher A, Glaser R. Facilitating participatory multilevel decision-making by using interactive mental maps. Geospat Health 2008,3:103-12.
Ackerson LK, Kawachi I, Barbeau EM, Subramanian SV. Geography of underweight and overweight among women in India: A multilevel analysis of 3204 neighborhoods in 26 states. Econ Hum Biol 2008;6:264-80.
Diez-Roux AV. Multilevel analysis in public health research. Annu Rev Public Health 2000;21:171-92.
Sur D, Ali M, von Seidlein L, Manna B, Deen JL, Acosta CJ, et al
. Comparisons of predictors for typhoid and paratyphoid fever in Kolkata, India. BMC Public Health 2007;7:289.
Sabesan S, Raju KH, Subramanian S, Srivastava PK, Jambulingam P. Lymphatic filariasis transmission risk map of India, based on a geo-environmental risk model. Vector Borne Zoonotic Dis 2013;13:657-65.
Rnga V, Panda P. Spatial access to inpatient health care in northern rural India. Geospat Health 2014;8:545-56.
Saxena R, Nagpal BN, Srivastava A, Gupta SK, Dash AP. Application of spatial technology in malaria research and control: Some new insights. Indian J Med Res 2009;130:125-32.
Najafabadi AT, Pourhassan M. Integrating the geographic information system into cancer research. Indian J Cancer 2011;48:105-9.
Mathew D. Information technology and public health management of disasters - A model for South Asian countries. Prehosp Disaster Med 2005;20:54-60.
Vaz FS, Ferreira AM, Motghare DD, Kulkarni MS. Conceptual framework of a geographical information system for environment - Public health surveillance in Goa. Indian J Public Health 2007;51:64-5.
Athavale AV, Zodpey SP. Public health informatics in India: The potential and the challenges. Indian J Public Health 2010;54:131-6.
Kumar N. Changing geographic access to and locational efficiency of health services in two Indian districts between 1981 and 1996. Soc Sci Med 2004;58:2045-67.
Kumar C, Singh PK, Rai RK. Under-five mortality in high focus states in India: A district level geospatial analysis. PLoS One 2012;7:e37515.
Fu SH, Jha P, Gupta PC, Kumar R, Dikshit R, Sinha D. Geospatial analysis on the distributions of tobacco smoking and alcohol drinking in India. PLoS One 2014;9:e102416.
Matthan R. Geographical data - Do we have a fundamental right to access it? Curr Sci India 2000;79:499-503.
Jaishankar R, Jhonson CP. Geomatics and public health. Indian J Public Health 2006;50:24-7.
Sumathi VR, Natesan U, Sarkar C. GIS-based approach for optimized siting of municipal solid waste landfill. Waste Manag 2008;28:2146-60.
Rathore CS, Dubey Y, Shrivastava A, Pathak P, Patil V. Opportunities of habitat connectivity for tiger (Panthera tigris) between Kanha and Pench national parks in Madhya Pradesh, India. PloS One 2012;7:e39996.
Kamel Boulos MN, Resch B, Crowley DN, Breslin JG, Sohn G, Burtner R, et al
. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: Trends, OGC standards and application examples. Int J Health Geogr 2011;10:67.
Kapur A. Geography in India: A languishing social science. Econ Polit Wkly 2004;39:4187-95.
Lahiri-Dutt K. Geography as a marginal social science. Econ Polit Wkly 2005;40:689-91.
Kandwal R, Garg PK, Garg RD. Health GIS and HIV/AIDS studies: Perspective and retrospective. J Biomed Inform 2009;42:748-55.
Narasimha R. Science, technology and the economy: An Indian perspective. Technol Soc 2008;30:330-8.
Luc A, Syabri I, Kho Y. GeoDa: An Introduction to Spatial Data Analysis. Geographical Analysis 2006;38:5-22.
SatScanTM. Softare for spatial, temporal and space-time statistics. Available from: http://www.satscan.org
. [Last accessed on 2015 Aug 03].
Levine N. CrimeStat: A Spatial Statistics program foer the analysis of Crime Incident Locations (V3.3) Ned Levine and Associates, Houston TX, and the National Institute of Justic, WEashington DC; 2010.
Google Inc. Google Earth (V 5.1.3533.1731) [software]. Avaialble from: https://www.google.com/earth/. [Last accessed on 2015 May 09].
QGIS Development Team, 2013. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org
. [Last accessed on 2015 May 09].
[Table 1], [Table 2], [Table 3], [Table 4]