Geographic Information Systems and Spatial Analysis - Research Paper Example

? Case Study Using GIS and Spatial Analysis Case Study Understanding the spatial distribution of data from phenomena that occur in the current world displays a great challenge to the elucidation of central questions in many areas of knowledge, regardless of whether it lies in health, in environment, in geology, in agronomy, among many others. Such studies are becoming more common, mainly due to the availability of low cost Geographic Information System with user-friendly interfaces. Such systems allow the spatial visualization of variables such as quality of life indexes, individual populations, or company sales in a region using maps. To achieve that, it is enough, and yet necessary to have a database and a geographic base, for example a map, and the GIS is capable of presenting a colored map that allows the visualization of the spatial pattern of the phenomenon . In the modern industrialized world, people of all ages are very mobile. Children commute between home and school or day care, and the working population commutes between home and work almost every day. A considerable proportion of the population move from one region to another within the municipality or country, or from one country to another, every occasionally (Waters, 2004) . Therefore, people expose themselves to different risk factors in different locations, and the relationship between catching a disease and the potential environmental risk factor is difficult to prove reliably. Thus, all the exposures and risks experienced earlier in life may become associated on maps with an inaccurate geographical location and may easily lead to erroneous conclusions and etiological hypotheses without individual-level information of the exposure history (Lees, 2006). Individual-level follow-up studies lie needed to gain information of the measurement of real exposure, but are, in many cases, laborious and costly. However, it would be fascinating to carry out analyses with a spatio-epidemiological model, which would stand more based on individual-level data than coarse spatial data. These individual-level data can lie gathered, for example, by questionnaires or by using modern GPS and GIS technologies. Such a database can function as an individual-level spatial exposure history and undoubtedly strengthen the spatial analyses aimed at search for the causality of the disease (Morra, 2006). While basic spatial analysis involves some spatial queries and attribute queries complicated analysis typically, require a series of GIS operations including multiple attribute and alteration of original data, spatial queries, and generation of new data sets. The methods for organizing and structuring such operations are a major concern in spatial analysis. An efficient and effective spatial analysis is one in which the best available methods are appropriately employed for different types of spatial queries, attribute queries, and data alteration. The design of the analysis however, depends on the purpose of study (Boots, 2000). Many food activists consider access to healthy food a basic human right, and insist that policies that affect our food system should stand enacted accordingly to ensure that everyone has access to healthy food. This is a complex issue, which involves many different players but it is generally more of a concern for lower income groups because they have limited resources and mobility. There have been numerous methods used to identify and analyze food access (Waters, 2004). Mapping patterns of access to food stores using GIS technology is becoming more prevalent and increasingly effective. This technology allows users to identify areas of low food access depending on specific criteria and enables the possibility for detailed spatial analysis. Conversely, GIS technology and spatial data are not readily accessible to everyone. Community organizations are often the most concerned about food access for local residents but they lie especially limited by their capacity to utilize GIS technology. This paper explores the concept of ‘food deserts’ within a given study area and discusses the effectiveness and accessibility of GIS technology for identifying and analyzing food access (Jacquemyn, 2009). There is a significant volume of information on spatial information applications within public health, ranging from assessing health facility placement to creating communicable disease early warning systems for infectious diseases such as West Nile Virus. By far the most frequent references were regarding the generation and statistical evaluation of associations between health outcomes and social and economic environmental factors. The large volume of information published on this area is likely due to both the variety of spatial applications for epidemiological information and the requirement for rigorous evaluation of spatial associations. This is using appropriate epidemiological science, for example case control and cohort studies. The latter is required as most GIS analyses assessing whether there is an association between geography and a health outcome will find one. Usually, however, health outcomes will cluster geographically because of the underlying population characteristics, not because of the environmental and physical geography itself (Jacquemyn, 2009). An important consequence of spatial dependence is that statistical inferences on this type of data will not be as efficient as in the case of independent samples of the same size. The spatial dependence thus leads to a loss of explanatory power. Generally, this reflects on higher variances for the estimates, as well as lower levels of significance in hypothesis tests and a worse adjustment for the estimated models, compared to the data of similar dimensions that exhibit independence. In most cases, the more adequate perspective is to consider that spatial data is not as a set of independent samples, as compared to realization of a stochastic process. Contrary to the usual independent samples vision, where each observation carries independent information, in the case of a stochastic process the entire observations lie used in a combined way to describe the spatial pattern of the studied phenomenon. Much research has addressed the fact that healthy food is often more expensive and difficult to obtain compared to a less healthy diet that contains calorically dense food. The result is that lower income groups do not get adequate nutrient intake when compared to people in higher income brackets. Healthy food may also cost more for low-income households because purchases remain made in smaller quantities. Many healthy foods, such as fruits, vegetables, and fish, are a more expensive type of calories, lie often viewed as luxury items by those with low incomes, and report that the price of healthy food and the variation between prices is of major concern for those living on low incomes (Jacquemyn, 2009). In particular, the built environment is a critical but under-recognized driver of variation in the spatial accessibility of healthy food. Access to almost every type of food outlet tends to follow a general urbanicity gradient in which more highly urbanized areas, those with higher population density and more land zoned to permit commercial use, are more likely to have stores and restaurants. As a result of urbanized areas often having concentration of minority or low-income populations, retail density may eventually be higher in minority or low-income neighborhoods. One way to control for the influence of the built environment is through sampling strategies that allow comparisons of similar neighborhoods. Researchers compare predominantly black and white suburban neighborhoods, and Goodchild and colleagues compare New York City’s East Harlem and the Upper East Side neighborhoods. Alternatively, some studies statistically control for built environment features. Although explicit consideration of the built environment is not necessary to document disparities, doing so will aid both in interpretation of findings and in generalization of those findings from the literature overall (Goodchild, 2002). In addition to the visual perception of the spatial distribution of the phenomenon, it is very useful to translate the existing patterns into objective and measurable considerations. Spatial analysis is the process by which we turn raw data into useful information. The term analytical cartography lie at times used to refer to methods of analysis that can remain applied to maps to make them more useful and informative (Morra, 2006). The human eye and brain are also very sophisticated processors of geographic data and excellent detectors of patterns and anomalies in maps and images. Therefore, the approach taken here is to regard spatial analysis as spread out along a continuum of sophistication, which ranges from the simplest types that occur very quickly and intuitively when the eye and brain look at a map, to those types, which require complex software and sophisticated mathematical understanding (Young, 2009). GIS technology, in its most basic form, is able to incorporate geography and information into a system that can manage and analyze spatial data for many different purposes. Researchers refer to geo-references as the pins that attach information such as facts, image, and events to certain points on a map. For example, demographic, economic, and environmental data can lie visually linked with actual features on a map like a house, a tract of Land or a stand of trees. GIS has the capability to provide users with representations of historical, cultural, and socioeconomic information in a way that is unique when compared to other data analysis tools. The technology dates back to the days of mainframes and punch cards in the 1960s. In 1962, the Canadian government introduced the Canada Geographic Information System designed to map national land-use data (Waters, 2004). During the 1970s, a landscape architect, created a method, which vertically overlaid environmental data. This included data such as the topography, and soils with infrastructure such as roads and buildings. Research has it that that this innovative work outlined the basis of which geographic information systems function. Roughly a decade later, GIS was made available to anyone with enough technical background as further modifications to the software package combined with decreasing hardware prices. Market researchers were the first to utilize the technology on a large scale by mapping demographic data on population density, household income, and the location of competitors to choose the best sites for retail expansion. Federal, state, and local government agencies soon found GIS to be an invaluable way that drastically decreased inefficiencies in their planning processes (Lees, 2006). Spatial data constitute the backbone of GIS. The number of geo-referenced data sets, which contain information on the health status of individuals, has increased tremendously during the past few decades. GIS also provides access to additional information from a wide variety of sources. In site of an example, Global Positioning Systems can lie used to obtain the precise locations of point features on a map. Furthermore, GIS can process aerial or satellite imageries to allow easy integration of information of such parameters as temperature, soil type, and land use and determination of spatial correlations between potential risk factors and the occurrence of diseases. The use of GIS, however, by no means overcomes two major concerns: the availability and quality of data. It is of the essence to understand the fact that the nature of errors in spatial data and the effect they may have on the quality of the analyses made with GIS (Ding, 2002). Whatever the choice of spatial resolution, it is artificial and, to variable extents, fails to capture people’s everyday living environment. Administrative and political areas are still important from the viewpoint of national or regional health politics and administration. Spatial data of a relatively high resolution, also pose the question of conflict between individuals and administration. The use of high-resolution spatial data may endanger the privacy of individuals if used incautiously. None of us wants to stand identified on maps or in statistics by the authorities or by fellow citizens (Young, 2009). Spatial analysis is the vital part of GIS. It can lie done in two ways. One is the vector-based and the other is raster-based analysis. Since the advent of GIS in the 1980s, majority of the government agencies have invested heavily in GIS installations. This lies inclusive of the purchase of hardware and software and the construction of mammoth databases. Two fundamental functions of GIS have been widely realized: generation of maps and generation of tabular reports. Indeed, GIS provides a very effective tool for generating maps and statistical reports from a database (Boots, 2000). Nonetheless, GIS functionality far exceeds the purposes of mapping and report compilation. In addition to the core functions that lie related to automated cartography and database management systems, the most important uses of GIS are spatial analysis capabilities. As spatial information lies organized in a GIS, it should lie able to answer complicated questions regarding space (Ding, 2002). Regardless of whether simple mapping methods or advanced spatial models are used, the final goal ought to be differentiation between meaningful and spurious information. Straightforward use of classical statistical modeling that assumes independence of events may lead to less desired results in spatial epidemiology. Spatial relationships based on proximity and relative location form the core of the spatial analysis and spatial statistical models lie based on the idea of spatial dependence; in other words, disease incidence in one area is likely to correlate spatially with that in the neighboring areas. Without this presumption, there would be no spatiality, and it would be appropriate to show the results in a tabular form without map topology (Crabbe, 2000). A good statistical model is able to describe the mechanism that produces observations, or rather, the mechanism behind the data. The concept of probability and the way we interpret it is fundamental to our understanding and interpretation of occasional incidents. Several other methods regard the value of the phenomenon of interest as a fixed, unvarying, quantity without a probability distribution. They calculate confidence intervals for this quantity or the significance tests of the hypothesis concerning it. However, the frequentist approach, whereby probability lies seen as a set of trials, is not fully compatible with ecological research designs, which assume that there is only one realization of events, and which cannot stand repeated. What should we do if we wanted to make reliable evaluations of the true disease risk in a certain area in a certain year in the past? Is GIS of any benefit in studies on spatial patterning of health? GIS undoubtedly offers epidemiologists a new fascinating tool full of promises, but if poorly used, it can do more damage than good. Much of the benefit of GIS depends on the availability of valid, accurate, and complete spatial data. At the small area level, nevertheless, relatively minor inconsistencies might have a major impact on the findings. Currently, geo-referenced data are abundant and continuously increasing (Morra, 2006). GIS can play an effective role in handling large volumes of spatially referenced health data routinely collected on small spatial scales. In a scenario whereby such databases are not available, GIS can provide other forms of data. GIS is capable of using remotely sensed satellite data and locally global positioning systems are a feasible way for spatial data capture (Crabbe, 2000). Motivated by different application areas, the inferential models lay separately developed for each of the situations described above. The unification of this field lies not completely defined, and it is often likely to apply more than one type of modeling to the same data set. Then what would be the advantages of a form upon the other? Sometimes, of course, the phenomenon under study presents discrete spatial variation, that is, isolated points in space. Nonetheless, often the discrete models lie frequently used for practical reasons, for example, the availability of relative area data only. One of the advantages of continuous models is that the inference does not limit itself to arbitrarily defined areas. On the other hand, discrete models allow the easier estimation of association parameters between the variables. The researcher will make the final choice, for he knows there is no such thing as the correct model, but searches for a model that better adjusts to the data and that offers the greatest potential for the comprehension of the phenomenon under study (Blough, 2003). The need to combine different inferential models and to have a solid knowledge of the different techniques derives from the very nature of the geographic space. To employ the formulation with regard to the research, the space is a whole, expressed by the dualities between form and function and between structure and process; these polarities remain evident when we utilize analytical tools. Using GIS and spatial analysis, one could adequately characterize the form of the space organization, but not the function of any of its components (Crabbe, 2000). We can also establish what the structure of the space is when we model the phenomenon under study, but hardly will we be able to establish the dynamic nature of the processes, be they natural or social. The relationship between structure and process can only be solved when the combination of analytical techniques that describe the structure of the organization of space and the specialist that understands the dynamic of the process (Waters, 2004). In summary, GIS can be useful in generating data for input into epidemiological models, displaying the results of statistical analysis, modeling processes that occur over space and presenting subsequent analysis in map format. Most often however, data ought to lie entered into separate software applications for statistical analysis and for model building, after which the processed data must lie, returned to GIS for the design and production of cartographic outputs (Goodchild, 2002). There are obvious restrictions to the view that proximity to or coverage of food stores in particular neighborhoods has a significant impact on individual food choices. The factors affecting healthy food consumption are multiple and most likely extend well beyond spatial or geographic features. Although the food desert literature addresses just one piece of the food access problem there is plenty of valuable information that can lie gathered from spatial analysis. It is critically important for future studies to consider how the local food environment may be creating barriers to healthy eating, especially for underprivileged areas (Blough, 2003). The implementation of GIS technology for identifying at-risk communities is key to understanding these complex spatial relationships. By taking advantage of the help of intermediary institutions, community groups would greatly benefit from using GIS to analyzing local food access patterns. With the support and credibility that comes from high-quality data analysis, organizations could be more effective at acquiring additional sources of funding and influencing policy (Goodchild, 2002). The subject of healthy food access would greatly benefit from further research. Additional refinement of the definition of food deserts, particularly in rural areas, would allow researchers to more accurately classify regions as having low food access. Using GIS in conjunction with multivariate models to examine the relationship between neighborhood need and potential spatial access to food stores might give researchers a better understanding of situations in particular areas. Lastly, as the consolidation of food stores becomes more of a concern in rural areas, an important focus of research would be to explore the pattern of food stores closing down or limiting healthy food options in these areas. GIS technology could also stand applied to examine other aspects of the rural food environment such as delivery routes. References Blough, D. R. (2003). Integrating GIS Into The Survey Research Process. New Directions for Institutional Research, 2003(120), 37-52. Boots, B. (2000). Using GIS To Promote Spatial Analysis. Journal of Geographical Systems, 2(1), 17-21. Crabbe, H., Hamilton, R., & Machin, N. (2000). Using GIS And Dispersion Modelling Tools To Assess The Effect Of The Environment On Health. Transactions in GIS, 4(3), 235-244. Ding, Y., & Fotheringham, A. (2002). The Integration Of Spatial Analysis And GIS. Computers, Environment and Urban Systems, 16(1), 3-19. Goodchild, M., Haining, R., & Wise, S. (2002). Integrating GIS And Spatial Data Analysis: Problems And Possibilities. International Journal of Geographical Information Systems, 6(5), 407-423. Jacquemyn, H., Wiegand, T., Vandepitte, K., Brys, R., Rold -Ruiz, I., & Honnay, O. (2009). Multigenerational Analysis Of Spatial Structure In The Terrestrial, Food-deceptive Orchid. Journal of Ecology, 97(2), 206-216. Lees, B. (2006). The Spatial Analysis Of Spectral Data. Applied GIS, 2(2), 14.1-14.13. Morra, P., Bagli, S., & Spadoni, G. (2006). The Analysis Of Human Health Risk With A Detailed Procedure Operating In A GIS Environment. Environment International, 32(4), 444-454. Waters, R. S. (2004). Applied GIS And Spatial Analysis. The Photogrammetric Record, 19(107), 254-255. Young, L. J., Gotway, C. A., Yang, J., Kearney, G., & DuClos, C. (2009). Linking Health And Environmental Data In Geographical Analysis: Ita€™s So Much More Than Centroids. Spatial and Spatio-Temporal Epidemiology, 1(1), 73-84. Read More