GIS or geographical information system have become more matured and robust. It is being used in different types of applications in various domains like technology, research, social sciences, governance, business etc. More advanced technologies like web based GIS, mobile GIS etc. are emerging. In current age, information technology applications have become more ubiquitous in nature. Usability of those applications is also another concern. Applications those are social-driven, are more relevant in this context.
Spatial decision making based on spatial data has been introduced and implemented in practice based on web based GIS applications. Any common man can place query and access spatial data from suitable application. This is kind of democratizing of events. GIS is being adopted by social sciences very rapidly. Application of GIS in social science helps to address number of challenges faced by society. (Craglia & Maheswaran, 2004)
Social science is a vast domain. Basically it covers all activities that somehow related to human aspects or studies human aspects. Sometimes social science is termed as soft science. It is just an attempt to categorize physical or quantitatively driven physical science from subjective social science. But it is hard to differentiate between these two as those are overlapped. There is a new category of social sciences with such overlaps, it is called mixed methods. Social science helps in binding members of a society to its environment. Social science can be think of as a process that constructs and describes how people are networked and interact with each other, how the behave, think or act , how people can be grouped together etc. People also forms communities but there will be individual existence. (Adewunmi, 2007)
There will be some kind of spatial framework for any social process. This is very much inherent to the behavior and daily activities of the people. For example, local neighborhood is the place where people live and this is spatial by nature. On the other hand, spatial framework is important for GIS applications and concepts. Here comes the reasons behind application of GIS in social sciences. GIS helps in carrying out different types of socio-spatial research by allowing comparison and integration of data from different contexts, environments etc. GIS has significant applications in such researches. (Craglia & Couclelis, 1997)
A research on social science will be involved in exploring, describing and explaining just like GIS helps in analysis of spatial data, gives insight to the results. In many cases these results and insight to the result have been proven as pivotal for contemporary society (Babbie, 2012).GIS applications needs to use spatial data from appropriate sources and analyzing those using suitable methods. Social issues like public health, education, development, crime, planning etc. are important application domains of GIS into social science. (Nyerges, et al., 2011)
Application domains like health, rural planning, urban regeneration, development of social policies, crime etc. have different key themes for applications of GIS into these domains. For example, cluster analysis is used in determining health related queries from social and geographical information, in urban regeneration visualization is used for getting idea about some proposed development plan. (Moran, 2011)
GIS can be used to tackle various issues with government, academia etc. It is possible to map spatial dimensions or components into different social datasets like data on lifestyle of people, census data, public health records, government data etc. from these data different types of maps like choroplethic map can be developed (Babbie, 2012). For example, it can be visualized that what is the employment status of the people in some area by analysis data from national statistics. This is possible by joining geometric attributes and map creation. It can also help to find patterns from social data ranging from a local area to different regions. Special dimensions like place, location, distribution, scale etc. are part of GIS based applications in analysis of such kind of data. Thus GIS not only helps in explaining social science processes rather it helps in various mapping, measurement and analysis. (Craglia & Couclelis, 1997)
There are several application of GIS in social science. In community development data on community organization, urban spatial politics, narratives based of GIS spatial data etc. are being used. In disaster management geospatial technologies can be used. In land management land use conversion, different regulative mechanisms, geo-informatics are used. In public health, remote sensor based systems are used for identifying areas with high risks of diseases etc. In planning, GIS based terrestrial planning are used for developing wireless network in some area etc. Other than these GIS can be used in numerous social science domains like sustainable development, geography including soil study, agriculture etc.
In practice, the original problem is identified in the light of socio-spatial concepts, then specification of data and methods will be used for gauging the scale of the analysis, limitation of data etc.
There are various issues with social applications of GIS. One of the foremost consideration is how data will be collected and will be analyzed. Then next issue is how data will represent individuals and groups. Then, how social activities and behavior will be analyzed by the spatial patterns. Another consideration is there may be several issues while working with the spatial and longitudinal data sets. (Steinberg & Steinberg, 2005)
Scale and integration is an important factor for datasets collected from GIS based applications. For example, census contains data about people and their households. There may be exceptions to ensure data security and the quality of those data may be not ready to analyze. This is there for ensuring anonymity of data. On the other hand, aggregated datasets are useful for mapping visualizations on those datasets. Ecological fallacy is a great concern in Social applications of GIS. According to it, any characteristic or process may not be present if the scale is changed, hence, it is inappropriate to assume presence of those characteristics or processes while moving from one scale to another. Thus, average characteristics cannot be assumed for aggregated population. At ecological level, associations may be observed among different variable but that do not imply same associations at another levels of groups or individuals. The identification and investigation of such variables is the responsibility of the GIS analyst. GIS analyst is also responsible to scale datasets properly by ensuring there will be no ecological fallacy or other ‘casual links’ unfounded in the datasets (Steinberg & Steinberg, 2005). In another way, multiple scales of the social behaviors and activities should be recognized. These scales may operate on some multi-level model. A multi-level model can be developed from aggregated data or from individual data based on contextual or compositional or both kinds of behavior. These models are made in such a way that it reflects realistic nature of human activities. (Moran, 2011)
With the advancement of GIS and the modeling techniques, now it is not impossible to model neighborhood and behaviors of individuals, but it also helps in modeling social activities of individuals. For example, travelling details of an individual on space, shopping information etc. Researchers can investigate individual level compositional factors other than area based contextual behavior and patterns present in social landscapes. These features also helps in exploring interplay between different factors that operate of the population levels and scales. (Paliou, et al., 2014)
Many of the recent GIS applications in social sciences have given exposure to the pollutants to the environments, healthcare opportunities, travel information etc. Even employment activities of individuals can be captured and analyzed. Models of spatial activity research focus on the activities by individuals on spaces during 24 hours of a day. Models on individual level sometimes collect data from questionnaire, personal details etc. A thing to consider here is, the size of the dataset in such cases will not be very longer, thus there may be issues with representation of data from this smaller sized datasets. On the other hand, larger projects may involve geocoding and more computational budgets for combined datasets on group of individuals. Here one important thing to consider is privacy of individuals.
Analysis can be either local or global. GIS based social applications may focus on the longitudinal studies on historical GIS. From these resources data on social records can be accessed based on different time frames. After that these data can be analyzed. For example, patterns on spatial inequality over time can be understood from such datasets. Standardization is a key issue here. (Steinberg & Steinberg, 2005)
In case on any longitudinal study it needs to consider the changes restricted by social landscapes, administration etc. on different time frames. Thus, the data sets cannot just overlay on the top of each other as the characteristics of the landscapes change over time. The variations on the boundaries or spatial units are also needed to be considered. A process called interpolation can be used in such cases. It helps in disaggregation of the datasets from the original sources and re-aggregates those at the target level. For example, if the census data of some state is needed to be analyzed from time frame from 1990 to 2000, then the simplest way of doing so is to enumerate data for different towns or cities of the state. But again, simple overlay is not possible to aggregate census data from different towns and cities. The suitable approach is to transform the datasets into same spatial units. This problem belongs to the domain of areal interpolation. This is a process based on areal weighing by assuming uniformity of the characteristics across the space. Then it intersects the datasets from sources and target and helps in establishing a shared area. On the shared area it performs re-distribution of the population based on the proportional value. (Raper, 2003)
A historical study considers how geography effects datasets over a period of time during collection of data. Data standardization helps in study of different landscapes of individual behavior and social advancements of individuals over time. (Casino, 2009)
GIS has significant implication on social sciences and society. One of the most important GIS based development is the emergence of critical GIS. In early days GIS based applications were more focused on technical and quantitative approaches. Geographers and GIS researchers were challenged to follow on these quantitative approaches later on and they gave attention to surveillance and controlling properties of GIS. Application of technology on society is often shaped by different social conditions. GIS based technology and its application on social sciences is no exception. Technology also has significant impact on society. Social shaping and social impact are two perspective towards ‘social constructive’ and technology. These help in understanding the roles of information and communication technologies in society, among individuals and groups. (Milson & Alibrandi, 2008)
Usually an investigation framework is followed while finding insight from the results of the analysis. Application of GIS to social sciences provides just some glimpses to the emerging realm of GIS and its impacts on society. There are various issues to negotiate during such applications of GIS in social sciences. There are various social based GIS applications.
A typical investigation framework contains several steps. During the very beginning, it is necessary to have a clear definition and interpretation of the actual problem. The concepts should be very clear. Then the conceptual framework can be developed. This is important for identification of the variables and actors as a part of the study and area of the study, its environmental and social impacts. Without the conceptual framework it is difficult to gauge the requirements. (Cope & Elwood, 2009)
When the goals of the project are defined clearly, then the investigation methods are needed to be selected. In case of social sciences, there are options to take qualitative, quantitative or mixed approach in investigation. On the other hand, in GIS, the focus is more on the qualitative investigation. There are various tools and techniques to measure connectivity and distance. Recent advancements in activity modeling at individual level and participatory techniques have helped in such cases. For example, biographies, oral histories can also help in analysis applications, visualization of the environment of individual etc. It also helps in demonstrating different geographical setting and enhance quality of qualitative analysis.
After identifying the methodology to follow in the analysis process, it needs establishment of the datasets. In some cases, databases are available readily and easily fits into the purpose. But in some cases, opposite may happen. There may be more data than requirement. It should be remembered, that quality and quantity of data are important. There may be issues with ‘padded’ datasets that is data sets based on some proxy based mechanisms. There are various publicly available data sets like census and others from national statistics. Then it comes about data aggregation. The nature, scale and aggregation of data should be considered. Proxy mechanisms sometimes are also helpful, but it depends on the problem and available dataset and also how the proxy mechanisms are being used. (Cromley & McLafferty, 2011)
There is a wide range of analytical methods supported by GIS. These methods can be applied to social sciences. The selection of spatial data model effects the available analysis methods and tools.
At last, proper interpretation of the findings is a key issue. There may be applications and use of theories behind finding spatial patters on human behavior. Finding the right set of information from the finding from the analysis is a crucial part. (Lünen & Travis, 2012)
GIS is rapidly gaining its popularity among the domains and applications related to social sciences. The key steps of any process will be conceptualization of the actual problem, deciding the methodology for analysis, collecting and building suitable dataset, analysis of the dataset and interpretation of the analysis.
GIS has very promising and significant applications on social sciences. There are several researches going on this topic. It helps in improving society and lives of the individuals. Thus it can change social landscapes and its elements. But there are several notable issues. For example, data privacy of individuals. Collection of data should consider data privacy of individuals. It does not gives right to access any personal information about anyone. With emergence of technology and GIS based application this issue is becoming more prominent. GIS practitioner and analysts should take care of this issue. They should use data and its analysis in the proper way. It should not be the case that instead of making the lives of individuals easier, these applications make them suffer. The impact of GIS can be very potential but it should be understood at the first place. (Mehrer & Wescott, 2005)
In this essay, different facets of applications of GIS in social science have been discussed. To make the content easier to understand, at the very beginning, the terms like society, social science etc. have been defined clearly. Then there are discussions on impact of GIS based application in past, present and future scopes, the methodologies used in these applications, the process of GIS based applications in social sciences, what are the things to understand and follow in this case etc. (Scholten, et al., 2009)
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