"Public Health and GIS, Mismatch of Job Skills and Theoretical Training"
Abstract
The escalation of geographical information system (GIS) application in several fields such as environment analysis has been progressive in relation to poverty, crime and education (Duecker and DeLacey, 1990, Huxhold, 1991, Harris and Batty, 1993). However, public health lags behind these fields in its use of GIS (Urban and Regional Information Systems Association, 1994). This limitation had largely been highlighted in the literature (see Fuertstein, 1987; Drummond, 1995; National Association of Country Clubs Off, 2002). However, the development of measures and strategies that could enhance GIS in public health had been proposed in terms of location, area and even in data processing (Queralt and Witte, 1998; McLafferty, 1998; Rushton & Frank, 1995).
The area of GIS and Public Health has risen to prominence in the past two years with the recognition that health surveillance practices and health service allocations need to become more sensitive to the needs of people in local geographic areas. The collection, storage and manipulation of geographic information have undergone a revolution in recent years with the development and widespread availability of GIS software. Many health professionals can benefit from further education in this area, and with their new knowledge, they can influence the progress of health surveillance, environmental health assessment and the geographic allocation of health resources.
This development provide a significant catalyst for the advancement of public health GIS and the use of geospatial data through the Internet (Croner, 2003). They provide timely stimulus for the delivery of public health geospatial information for community, state, and national uses. They portend important changes. Based on emerging geospatial infrastructure in the twenty first century.
A GIS can be a useful tool for health researchers and planners because health and ill-health are affected by a variety of life-style and environmental factors, including where people live, characteristics of these locations (including socio-demographic and environmental exposure) offer a valuable source for epidemiological research studies on health and the environment. Scholten and Lepper (1991),
With the huge increase in the use by planners of geographic information systems (GIS), a need has developed for accompanying statistical routines to aid in the analysis and interpretation of geographical data particularly spatial analysis. (Levine, 1996) Many planners use GIS to isolate geographical areas, subpopulations, land uses, and road systems according to various search criteria, extracting objects on the basis of geographical or attribute conditions. The existing GIS packages are very sophisticated tools for geographical and database operations. They can conduct a wide variety of different overlay operations: creating buffers around objects, selecting objects by their proximity to other objects, unioning smaller objects into bigger ones and splitting larger objects into smaller objects, as well as implementing a whole range of database functions (e.g., conditional queries, object queries). (Levine, 1996)
The information contained in a GIS is not in itself unique. Rather, the uniqueness of GIS lies in its ability to integrate pieces of existing spatially-referenced information in unprecedented ways. Some go so far as to say that, based on the new perspectives offered by GIS, it might even constitute a new discipline (Goodchild, 1990). Whether or not this is a realistic assessment, there is little doubt that GIS offers great benefits in the constructivist, holistic model upon which it is based--a perspective that is gaining attention among educators (Boyer and Semrau, 1995).
Many geography educators hold that enhancing geography education must include integrating spatial technologies such as GIS (Nellis, 1994). GIS research has in turn expanded to include theoretical and practical questions about its place in the framework of education, (Suit, 1995) since lack of such understanding would undermine the potential of the tool itself (Donaldson, 1999). Likewise, if GIS is to evolve into a significant force in education, more thought needs to be given to how it is implemented and used in classrooms. Most geography educators concur that successful GIS implementation will not be possible without a combination of (1) acknowledgment of its usefulness by teachers and administrators; (2) a concomitant level of financial support for this technology; (3) the provision of teacher training; and (4) the creation of networks to supply teachers and administrators with the entry-level and advanced information they need to implement GIS.
This study shall investigate the disparity in terms of the curriculum of spatial methods in the Masteral level and what is taught in Schools of Public Health using GIS (geographic information systems) and what employers, particularly in the Public Health sector both private and public expect these students to be able to do using spatial analysis tools competency. Moreover, this study shall include the state and local public health offices, public health research firms, and GIS educators in order to determine if their employees coming out of these schools possess the necessary skills or are they being taught on the job to use GIS.
Conceptual Framework
This paper shall utilize the mathematical modelling by Arbia, G., Griffith, D. and Haining, R. (1999). The purpose of adopting this approach using maps and error processes with simple but well-defined properties is to understand better how different elements of the situation, individually and together, contribute to the final propagated error. The problem with using real maps (rather than artificially generated maps) is that real maps usually have complex structures so that it may not be clear the extent to which aggregate statistics computed to measure the severity of the error problem are an aggregation across many types of quite different map segments with different structures. Usually, real errors are not known for any data set, and unless their structure is uniform across the map, the same problem for interpreting aggregate statistics could arise.
Using formal mathematical modeling, rather than just simulation, means that where theoretical results can be obtained they can be used to check simulation output before the simulation is used to obtain properties that are not accessible to mathematical analysis. Furthermore it is only through formal mathematical modeling, leading to closed-form expressions, that a rigorous study can be undertaken that yields quantitative and qualitative insights as to how different elements contribute individually or interactively to error propagation. The formal expressions make the contributions explicit, and regression (adding maps) and ANOVA (ratioing) are used to quantify the relative contributions of each term in the expression. Where theoretical results have not been obtained, as in the case of ratioing, simulation alone, even with regression analysis of the outputs cannot produce the same quality of evidence because of the dangers of model misspecification in using regression. (Arbia, G., Griffith, D. and Haining, R., 1999)
Research Questions and Hypothesis
Statement of the Problem
This proposed paper shall evaluate the spatial analysis models and in the public health sector and shall be compared to what is taught in the masteral level. Essentially, the study shall focus on the problems encountered in the spatial modelling and planning in the public health and how the education system helps/lacks in the training of the students.
Specifically, two questions shall be answered: (1) What are the problems, factors and challenges facing the public health sector in GIS spatial modelling and how does the training of the masteral students addresses this problem? (2) Can the masteral training of the public health practitioners support and address the spatial problems encountered in the workplace application?
Hypothesis
This study shall test the following null hypothesis:
Public Health Schools provide inadequate GIS education for the actual applications of spatial methods and analysis in the public health sector.
Significance of the Study
This study shall determine the workplace practice and application of geographic information system (GIS) in the public health sector and how the education provided in the academe is compatible/incompatible with the actual practice of spatial analysis. Furthermore, this study shall illustrate the importance of spatial modelling and planning in the training of masteral level students in solving the spatial problems encountered in the workplace. This will be beneficial to educators, students and GIS practitioners alike in determining the capabilities that are required in the public health sector.
Scope and Limitation
This study will discuss the weaknesses and strengths of the masteral spatial GIS education in the public health setting, the factors affecting the efficiency and competence of the students in public health GIS and the improvements that could be done.
Review of Related Literature
This chapter shall present the related studies and researches in the areas of GIS in health planning, spatial analysis in GIS, the disparity between spatial training in education and in the public health workplace. These data shall be utilized as a background of the research and shall provide the measures and the supporting data regarding the spatial analysis of GIS in the health sector. This section shall be divided into three areas: the introduction of GIS and GIS in the health sector, the need for spatial tools in addressing the problems in GIS mapping, modelling and planning and the disparity in the educational training of students in spatial methods.
Background of the Study
Geographic Information Systems (GIS) is a computer-aided database management and mapping technology that organizes and stores large amounts of multi-purpose information. GIS adds the dimension of geographic analysis to information technology by providing an interface between the data and a map. This makes it easy to present information to key decision-makers quickly, efficiently and effectively.
Geographic information systems and remote sensing from earth-observing satellites are sophisticated and powerful technologies that are finding applications far beyond those originally intended. According to the World Health Organization (2003), both are products of the Cold War developed by the departments of defense for military purposes. Together, they allow near real-time access to data on temperature, soil, elevation, patterns of land use, and phases of vegetation in addition to the precise geographic location of water bodies, population centers, buildings, roads, and other infrastructure. Their use for purposes ranging from the search for natural resources to transportation engineering, urban design, and agricultural planning was quickly recognized and exploited.
With the geographic information system, observations regarding the social, economic, political, and physical environments can be referenced to a common geospatial data framework (Rushton, Elmes and McMaster, 2000). This permits varying organizations to share spatial data regarding these phenomena. Geographic information science has the potential to create rich information databases, linked to methods of spatial analysis, to determine relationships between geographical patterns of disease distribution and social and physical environmental conditions. As the core of a decision-support system, geographic information science also has the potential to change the way that allocations of resources are made to facilitate preventive health services and to control the burden of disease.
Geographic information systems and remote sensing have capabilities that are ideally suited for use in infectious disease surveillance and control, particularly for the many vector-borne neglected diseases that are often found in poor populations in remote rural areas (WHO, 2003). They are also highly relevant to meet the demands of outbreak investigation and response, where prompt location of cases, rapid communication of information, and quick mapping of the epidemic's dynamics are vital. However, until recently, the use of these tools in public health were largely limited in use due to two major problems: the prohibitive cost of hardware and the great complexity of GIS software that made it extremely time-consuming as well as costly to extract information relevant to the practical demands of disease prevention and control.
Map Errors
A datum is considered spatial if it contains location information. (Cressie and Gabrosek, 2002) Typically, there is also attribute information, whose distribution depends on its location. Thus, error in location information can lead to error in attribute information, which is reflected ultimately in the inference drawn from the data. Data are considered spatial if they contain location information. Typically, there is also attribute information available. The distribution of the attribute varies from location to location. Attribute information consists of the measured response (or responses), which can be either discrete (for example, counts of animal populations) or continuous (for example, soil pH). With the advent of optimal spatial linear prediction (that is, kriging), the analysis of spatially dependent data has progressed rapidly in the past forty years. (Cressie, 1990)
The study of map error and map error propagation raises a distinct set of problems that go beyond traditional error analysis (Taylor
GIS and Planning
Another way to ensure a more bottom-up approach to GIS is to focus on the incorporation of local knowledge in GIS. There are a few examples of this in the context of planning. Some researchers (Craig & Elwood, 1998; Elwood & Leitner, 1998) have attempted to incorporate local knowledge in the building of GIS databases, working to incorporate value-based, traditionally intangible information, such as how residents value their homes or their feelings about the uniqueness of a given area (Bosworth & Donovan, 1998). Because these approaches seek to give local residents greater access to GIS, they are aligned with other community-based uses of GIS (Elwood & Leitner, 1998). However, they also add the attempt to incorporate resident, or local, knowledge.
Need for Spatial Statistical Tools
The lack of spatial statistical tools hinders planners, who often deal with a spatial relationship between one set of objects and another that should be considered quantitatively rather than qualitatively. (Levine, 1996) For example, housing experts may want to compare the socio-economic characteristics of areas that do or do not contain public housing projects, or transportation planners may want to relate traffic volume measurements to characteristics of the surrounding neighborhood. Standard GIS packages can quantify some aspects of this by, for example, assigning a census tract's median household income to a housing project, or by associating a particular road segment having high traffic volumes with the number of families in the surrounding traffic analysis zone (TAZ). However, such relationships cannot easily be generalized beyond the specific database operation.
Standard statistical packages, such as SPSS and SAS, can conduct these types of summaries much more easily than can the GIS programs. In addition, generalizing the results beyond the specific data requires a set of inferential testing procedures and a statistical theory. (Leviune, 1996)
Furthermore, although displaying the results of a distribution on a map can be informative, its usefulness is limited. All kinds of distortions are produced by visual display; Tufte's classic work illustrates this wonderfully (Tufte 1983). It is very hard to look at a distribution and say whether it is similar to or different from another. Then, too, since most mapping programs are two-dimensional, a visual display can be overwhelmed by a large amount of data. In short, there are limits to database operations and to visual display. Spatial statistics allows for degrees of quantification and inference that are much more rigorous and less prone to misinterpretation. Neither the existing GIS programs nor statistical packages provide quantitative measures of spatial relationships such as dispersion, concentration, or spatial autocorrelation.
Spatial Analysis and Health
Geographers have attempted to account for location uncertainty. When creating maps, location is of paramount interest. At the other extreme, many statistical analyses take no account of location, modeling data as if it were statistically independent. Geostatistics is between these extremes. Geostatisticians use location to model trend and correlation between attribute values over a geographic region; however, they ignore uncertainty in locations.
The advent and then ubiquity of geographic information systems (GIS) has led to an explosion of information available from spatial databases. The easy storage and quick retrieval possible within a GIS requires concomitant development of spatial statistical methodology. Incorporation of attribute-error analysis is often handled through geostatistics, but there is an urgent need for statistical research and software developments to deal with both location error and attribute error (for example, Griffith
Geographers and users of raster-based GIS often model the effects of location error in spatial data by assuming that the attribute value is discrete (often a gray-scale value) and that the spatial domain is a fixed grid of pixels. A commonly used model for the Bayesian restoration of images, attributed to Geman and Geman (1984), has been adapted by researchers working with GIS to investigate how errors in source maps propagate through a GIS to output maps (for example, Goodchild 1989; Arbia, Griffith, and Haining 1998). Output maps result from overlay operations that combine two or more source maps at potentially different scales of spatial resolution.
The Need for GIS in Public Health
Access to health care is an important issue across populations who face substantial barriers in obtaining care, and health care policies and imperatives affected by the location, quality, and quantity of services available with concomitant effects on access. Access describes people's ability to use health services when and where they are needed (Aday and Anderson, 1981). Furthermore, health care decisions are strongly influenced by the type and quality of services available in the local area and the distance, time, cost, and ease of traveling to reach those services (Goodman, Fisher, Stukel and Chang, 1999; Haynes, Bentham Lovett and Gale, 1999). For medical conditions that require regular contact with service providers, travel, time and distance can create barriers to effective service use (Fortney, Rost, Zhang and Warren, 1999; Haynes, Gale, Mugfort and Davies, 2001).
GIS deployment trough the Internet is a relatively new technological development. The remarkable increase in use of the Internet is creating new standards, and challenges, for the efficient use of the Web-based geospatial applications (Longley, Goodchild, Maguire and Rhind, 1999). GIS and Web technologies offer emerging opportunities to analyze complex geospatial data, solve problems, and present data in a graphical format that public health decision makers and the public can easily see and understand (National Association of Country Clubs Off, 2002).
The application field and objectives of a GIS can be varied, and concern a great number of questions linking social and physical problems (transport and agricultural planning, environment and natural resources management, location/allocation decisions, facilities and service planning (education, police, water, and sanitation), and marketing).
Generally, the objectives of a GIS are the management (acquisition, storage, maintenance), analysis (statistical, spatial modeling), and display (graphics, mapping) of geographic data. Even if a few general concepts are presented, the GIS discussed here will be seen from a health perspective. Thus, GIS will be considered as a tool to assist in health research, in health education, and in the planning, monitoring, and evaluation of health programs.
As health is largely determined by environmental factors (including the sociocultural and physical environment, which vary greatly in space), it always has an important environmental and spatial dimension. The spatial modeling capacities offered by GIS can help one understand the spatial variation in the incidence of disease, and its covariation with environmental factors and the health care system.
GIS and Health Education
As mapping is an excellent means of communication, GIS can be used, as Kabel (1990) suggests, to help prepare educational materials. In an article on participatory evaluation, M.T. Fuerstein (1987) describes different methods for monitoring and evaluating community health projects, including mapping. Fuerstein (1987) suggested that maps, showing location of houses by number and type, public and private buildings, water sources, sanitation, bridges, roads, social centres, neighbourhood boundaries, health centres, etc. give participants a wider view of where they are living. Maps can help discussion, analysis, decision-making, management and evaluation.
Meyles and de Bakker (2002) conducted a study dealing with demand of employers for Geo-information specialists and the supply of educational institutes. They asserted that there is a need for a clear definition of a geo-information specialist with according content of the geo-information curricula. It needed to distinguish education into to two or three different GIS expert groups.
The situation has changed dramatically over the past few years. Hardware prices have plummeted, simple new devices are now available, and a new generation of civilian satellites is in orbit, circling the world. The Public Health Mapping Programme based within WHO Communicable Diseases has been developed with the goal of providing greater access to simple, low-cost geographic information and related data management and mapping systems to public health administrators at all levels of the health system (WHO, 2003).
Geographic Information Systems is also being used to map and explore variation in need for healthy services and to develop innovative indicators of health care needs (McLafferty, 1998). GIS has been used for many years to link diverse layers of populati0on and environmental information to characterize the many dimensions of health care needs for small areas (Hanchette, 1998; Mohan, 1993). According to McLafferty, in effect they are restricted to predefined geographical areas such as countries or zip codes, but in the future such as systems will likely incorporate GIS-based procedures that allow users to query data for user-defined areas. GIS has an important role in assessing health care needs for small areas by facilitating the spatial linking of diverse health, social and environmental data sets.
As digital information on morbidity, demographics and utilization becomes more widely available, health needs data will be incorporated in GIS-based decision support tools that allow communities and decision-makers to examine questions of health and needs, access and availability. Measures of geographic access can be either area-based or distance-based (McLafferty, 1998).
Information Technology Readiness of the Education System
The commitment to produce geographically-competent students has been the constant driving force in geography education. Yet, as its proponents enjoy the rebirth of geography education, they must also acknowledge that--while the subject matter of geography education may have remained similar over time--the modes by which geographic concepts are learned and taught in the classroom have changed, often significantly. Indeed, while geography education has returned to the school classroom, it has come back in the context of a society that expects learning to be accomplished in concert with modern learning technologies, chiefly the computer. But obstacles are posed by wide differences in the variety of computer technology available in American classrooms (Donaldson, 2001).
Among the most exciting developments in geography education today is the geographic information system (GIS), a tool that enables students to examine layers of geography in ways that can reveal fascinating and unique patterns and processes (Donaldson, 1999, 2001; Meyles and de Bakker, 2002; Thurston, 2001). Yet, the world a GIS can illustrate is being obscured by numerous barriers to its implementation (Donaldson, 1999, 2001; Meyles and de Bakker, 2002). Successful use of GIS in classrooms depends not only on the requisite hardware and software infrastructures, but also on a host of institutional and personal information networks. This article draws on survey research to identify the most significant infrastructures in spatial methods used in public health in school and practice.
Minimal attention has been directed to the essential components of a faculty development technology program (Dillon & Walsh, 1992). The literature on social work distance education has focused primarily on program design, evaluation, and logistics. Basic skills training allows faculty to understand the potential application of technology and encourages the use of available resources and tools.
While one aspect of developing technological expertise involves mastering the technical skills required to use various software programs, an equally time-intensive task includes translating those skills to a specific course or curriculum content (Siegel, 1995). Technology can add new resources to existing course content in traditional classroom settings. For instance, the Internet enhances the range of information available to students in addition to providing opportunities for international communication (Giffords, 1998; Johnson, 1998).
The demand for increased integration of technology can emanate from students, professional or organizational expectations, and advances in the application of technology to social work practice. As students become more familiar with technology, they may begin to expect online access to reading materials, syllabi, and other resources. With advances in distance education technology, the profession may be expected to be responsive to students in remote areas without access to traditional institutions (Kalke, et al., 1998). As technology becomes more integrated into professional practice, social workers will need to be skilled in various aspects of information technology (Giffords, 1998; Gingerich & Green, 1996; Schervish, 1993).
The demand for technological literacy from students requires that faculty be technologically competent to respond to this demand. While for some people, and in certain situations, the demand for technological literacy may be very compelling, for other individuals and in other situations this demand may not be sufficiently compelling to motivate learning (Conceicao-Runlee and Padgett, 2000). Faculty may be willing to invest time in retooling, but be unable to offer such a time commitment.
The demand for GIS experts is growing steadily, along with the increased need for digital geographic data. However, there is no widely excepted definition of a GIS expert (e.g. Thurston, 2001), and also no definition what the current contents of the education to fit the demands should be. The discussion involving the education curriculum, the reaction of employers and the possible need for a certification of the GIS expert or accreditation of GI courses has not yet ended.
Geographic Information Systems
Geographic Information Systems (GIS) are computer systems for capturing, storing, manipulating, analyzing, displaying, and integrating spatial (that is, geographical, or locational) and nonspatial (that is, statistical, or attribution) information (Maguire, 1991). Although professionals in various technical fields (for example, geology, geography, and urban planning) have been using GIS since the 1960s, these techniques still are little known and used in social work.
GIS software allows a social agency to produce meaningful, attention-grabbing maps that visually show important administrative, policy, and practice issues (Queralt and Witte, 1998). The software also makes it possible for administrators and practitioners to uncover new insights. For example, gaps in service delivery, areas of low service take-up rates, transportation problems, and location of areas of new demand for services. GIS software can also help social agencies communicate more effectively to clients the spectrum of choices available, an issue of increasing importance as the use of vouchers becomes more prevalent in the delivery of services. In short, GIS software gives social services agencies a powerful new way to analyze services in relation to clients and to the communities in which they operate (Queralt and Witte, 1998).
Uses and Benefits of GIS in the Human Services
Being able to place agency records on a map gives management and staff a whole new way of looking at data that may reveal patterns never discovered before (Queralt and Witte, 1998). Specifically, GIS can improve day-to-day practice and management decisions by providing tools to inventory, through maps, the agency's clientele, services, or any other information of interest; to assess the sociodemographic characteristics of the neighborhoods served by the agency; to assess whether the supply of services in a given community is adequate and appropriate for the target population and to forecast need or demand for additional services, given changes in the policy environment, such as the vast changes now taking place under welfare reform.
One of the major reasons for this relative neglect is the difficulty of generating accurate, timely, and inexpensive locational information for human activities. Recently, however, the use of low-cost GIS software for generating such data has become a realistic option. Address matching (also known as geocoding (Drummond, 1995), is a very powerful GIS technology: it can convert any administrative, survey, or business database with street addresses into a GIS database containing locational information. The resulting database can then be either displayed as a pin map, aggregated into regions and displayed as a thematic map, combined with U.S.
GIS also can be a useful research tool. Most researchers have been limited in their ability to analyze data to the levels that correspond to the geographic identifiers that are normally part of available data sets, such as state, county, city, or zip codes (Queralt and Witte, 1998). GIS has opened the possibility of studying small areas, such as census tracts or blocks, and of aggregating data to create new units of analysis, such as neighborhoods, school districts, or mental health catchment areas. This increased flexibility in the creation of geographic areas for planning and analysis is likely to yield more accurate answers to research questions and to result in better delivery of services (Rushton & Frank, 1995).
There is little documentation in the published literature of the use of GIS in the field of social work. What little evidence exists at present points to its use for research-related purposes rather than direct practice or administration (Coulton, Korbin, Chan, & Su, 1997; Coulton, Korbin, Su, & Chow, 1995). Professionals in related fields, particularly health, urban and regional planning, and criminal justice, appear to be using GIS more than in social work, although at present, few have published articles documenting their use of GIS.
Love and Lindquist (1995) used GIS to assess the geographical accessibility of hospitals to elderly people in Illinois by measuring and displaying the distance old people traveled from their homes in each of the 10,796 census block groups in Illinois England
The National Cancer Institute has developed an interactive map program that allows users to construct county-level maps illustrating the geographic distribution of cancer mortality by age, gender, or race (National Cancer Institute, 1992). Wain (1993) discussed how to use GIS in locality profiling. By mapping the location of problems of concern in specific localities (for example, health care problems such as high levels of infant mortality), one can develop a service strategy that is sensitive to the needs of the community. Similarly, she suggested, if one is concerned about the possible effects of a particular environmental hazard, such as an expressway, one can create, with the help of GIS, a map showing a butter zone around the hazardous area and then study the health experiences of people living in this area, compared with others living elsewhere. Armstrong, Rushton, and Lolonis (1991) used GIS to study the geographic distribution of low-birth weight babies in Iowa
Because creating a GIS system can take considerable time and effort, the first step in this process is to make sure that the data to be mapped is up-to-date, accurate, and complete. Prior to placing records on a map, referred to as "pin-mapping" or "geo-coding," it is wise to spend time cleaning up and updating records.
After records to be mapped have been cleaned up, the next step is to save them in a database format compatible with the GIS software package to be used. Having put records in the appropriate database format for the GIS software chosen, the user is ready to create maps for each geographic area served by the agency. These maps will serve as "containers" in which pin-mapped records are kept.
Geographic Information System Use in Public Health
The systems link data generated from surveillance and public health information systems where their common reference points are geographic locations such as regional health areas, enumeration areas or postal codes. GIS tools enable health workers to examine health effects and environmental determinants by layering on a series of maps: population demographics, political and administrative boundaries and environmental factors such as soil, water, air and agricultural information
Health professionals in local/regional, and provincial/territorial public health offices need direct access via the Internet to user-friendly and cost-effective GIS, spatial data and metadata. We have worked with public health regions to develop a GIS Infrastructure that supports the spatial information needs of regional public health programs in their evidence-based planning and decision-management practices. The GIS tools and training are designed according to the needs of a broader range of health workers including those having limited GIS skills and experience.
METHODS AND PROCEDURE
This chapter shall discuss the research methods available for the study and what is applicable for it to use. Likewise, the chapter shall present how the research will be implemented and how to come up with pertinent findings.
Method of Research to be Used
This study shall use the descriptive research method which uses observation and surveys. In this method, it is possible that the study would be cheap and quick. It could also suggest unanticipated hypotheses. Nonetheless, it would be very hard to rule out alternative explanations and especially infer causations. This descriptive type of research will utilize observations in the study. To illustrate the descriptive type of research, Creswell (1994) will guide the researcher when he stated: Descriptive method of research is to gather information about the present existing condition. The purpose of employing this method is to describe the nature of a situation, as it exists at the time of the study and to explore the cause/s of particular phenomena. The researcher opted to use this kind of research considering the desire of the researcher to obtain first hand data from the respondents so as to formulate rational and sound conclusions and recommendations for the study.
The research described in this document is based fundamentally on quantitative research methods. This permits a flexible and iterative approach. During data gathering the choice and design of methods are constantly modified, based on ongoing analysis. This allows investigation of important new issues and questions as they arise, and allows the investigators to drop unproductive areas of research from the original research plan.
This study basically intends to investigate the disparity in what is taught in Schools of Public Health using GIS (geographic information systems) and what employers, particularly in the Public Health sector both private and public expect these students to be able to do using spatial analysis in GIS. Specifically, this study shall discuss the state and local public health offices, public health research firms, and GIS educators in order to determine if their employees coming out of these schools possess the necessary skills or are they being taught on the job to use GIS.
The primary source of data will come from a researcher-made questionnaire and interviews conducted by the researcher among employees and personnel in the public health sector, students who have taken GIS courses in public health and educators in GIS.
The secondary sources of data will come from published articles from Health and Information Technology Journals, books and related studies on Public Health, GIS curriculum and instruction and GIS application in the public health setting.
For this research design, the researcher will gather data, collate published studies from different local and foreign universities and articles from social science journals; and make a content analysis of the collected documentary and verbal material. Afterwards, the researcher will summarize all the information, make a conclusion based on the null hypotheses posited and provide insightful recommendations on the dealing with GIS in the public health sector.
Respondents of the Study
The general population for this study will be composed of selected personnel in the public health sector, GIS students and GIS instructors numbering to 60 respondents. The researcher shall also provide interviews for public health managers whose function is directly related to the organisation and implementation of the GIS employed in the organization.
Instruments to be Used
To determine the effects of GIS education in the public health sector, the researcher will prepare a questionnaire and a set of guide questions for the interview that will be asked to the intended respondents. The respondents will grade each statement in the survey-questionnaire using a Likert scale with a five-response scale wherein respondents will be given five response choices. The equivalent weights for the answers will be:
Range Interpretation
4.50 – 5.00 Strongly Agree
3.50 – 4.00 Agree
2.50 – 3.49 Uncertain
1.50 – 2.49 Disagree
0.00 – 1.49 Strongly Disagree
Validation of the Instrument
For validation purposes, the researcher will initially submit a sample of the set of survey questionnaires and after approval; the survey will be conducted to five respondents. After the questions were answered, the researcher will ask the respondents for any suggestions or any necessary corrections to ensure further improvement and validity of the instrument. The researcher will again examine the content of the interview questions to find out the reliability of the instrument. The researchers will exclude irrelevant questions and will change words that would be deemed difficult by the respondents, to much simpler terms.
Administration of the Instrument
The researcher will exclude the five respondents who will be initially used for the0 validation of the instrument. The researcher will also tally, score and tabulate all the responses in the provided interview questions. Moreover, the interview shall be using a structured interview. It shall consist of a list of specific questions and the interviewer will not deviate from the list or inject any extra remarks into the interview process. The interviewer may encourage the interviewee to clarify vague statements or to further elaborate on brief comments. Otherwise, the interviewer will be objective and not influence the interviewee's statements. The interviewer will not share his/her own beliefs and opinions. The structured interview will mostly be a "question and answer" session.
Statistical Treatment of the Data
When all the survey questionnaire will have been collected, the researcher will use statistics to analyse all the data.
The statistical formulae to be used in the survey questionnaire will be the following:
1. Percentage – to determine the magnitude of the responses to the questionnaire.
n
% = -------- x 100 ; n – number of responses
N N – total number of respondents
2. Weighted Mean
f1x1 + f2x2 + f3x3 + f4x4 + f5x5
x = --------------------------------------------- ;
xt
where: f – weight given to each response
x – number of responses
xt – total number of responses
The researcher will be assisted by the SPSS in coming up with the statistical analysis for this study.
References
Aday, L. and Anderson, R. (1981) Equity of Access to Medical Care: A Conceptual and Empirical Overview. Med. Care 19 Supp: 4-27.
Arbia, G., D. Griffith, and R. Haining (1998). "Error Propagation Modelling in Raster GIS: Overlay Operations." International Journal of Geographic Information Science 12, 145-67.
Arbia, G., Griffith, D. and Haining, R. (1999) Error Propagation Modeling in Raster GIS: Adding and Ratioing Operations. Cartography and Geographic Information Science, Vol. 26.
Armstrong, M.P., Rushton, G., & Lolonis, P. (1991). Relationships between the birth weights of Iowa Des Moines
Audit, R. and Paris, J. (1997) "GIS Implementation Model for Schools: Assessing the Critical Concerns," Journal of Geography 96, no. 6 (November/December 1997): 293-300.
Bakker, M. de, and L. Bakker, 2000. Changes in GI education, EUGISES 2000, 7- 10 September, Budapest , Hungary
Bosworth, M., & Donovan, J. (1998, October). A mapmaker's dream: Public involvement applications utilization of GIS. Position paper presented at Project Varenius Specialist Meeting: Empowerment, Marginalization, and Public Participation GIS, Santa Barbara , CA
Boyer, B. and Semrau, P. (1995) "A Constructivist Approach to Social Studies: Integrating Technology," Social Studies and the Young Learner 7, no. 3 (January/February 1995): 14-16.
Conceicao-Runlee, S. and Padgett, D. (2000) DESIGNING A FACULTY DEVELOPMENT PROGRAM ON TECHNOLOGY: IF YOU BUILD IT, WILL THEY COME?. Journal of Social Work Education, Vol. 36.
Coulton, C. J., Korbin, J., Chan, T., & Su, M. (1997). Mapping resident perceptions of neighborhood boundaries. Cleveland : Case Western Reserve University , Mandel School
Coulton, C., Korbin, J., Su, M., & Chow, J. (1995). Community level factors and child maltreatment rates. Child Development, 66, 1262-1276.
Craig, W. J., & Elwood, S. (1998). How and why community groups use maps and geographic information. Cartography and Geographic Information Systems, 25(2), 95-104.
Cressie, N. (1990). "The Origins of Kriging." Mathematical Geology 22, 239-52.
Cressie, N. and Gabrosek, J. (2002) The effect on attribute prediction of location uncertainty in spatial data. Geographical Analysis, Vol. 34.
Creswell, J.W. (1994) Research design. Qualitative and quantitative approaches. Thousand Oaks , California
Croner, C. (2003) Public Health, GIS, and the Internet. Annu. Review of Public Health. 24:57-82.
Dillon, C. L., & Walsh, S. M. (1992). Faculty: The neglected resource in distance education. American Journal of Distance Education, 6(3), 5-21.
Dolinoy, D., Miranda, ML., and Overstreet, A. 2002. Mapping for prevention: GIS models for directing childhood lead poisoning prevention programs. Environmental Health Perspectives, Vol. 110.
Donaldson, D. (2001) WITH A LITTLE HELP FROM OUR FRIENDS: Implementing Geographic Information Systems (GIS) in K-12 Schools. Social Education, Vol. 65.
Donaldson, D.P. (1999) "Public High Schools' Ability to Support GIS: An Ohio
Drummond, W. (1995) Address matching: GIS technology for mapping human activity patterns. Journal of the American Planning Association, Vol. 61.
Elwood, S., & Leitner, H. (1998). GIS and community-based planning: Exploring the diversity of neighborhood perspectives and needs. Cartography and Geographic Information Systems, 25(2), 77-88.
Faison, C. (1996) "Modeling Instructional Technology Use in Teacher Preparation: Why We Can't Wait," Educational Technology 36, no. 5 (September/October 1996): 57-59
Fortney, J. Rost, K., Zhang, M. and Warren
Fuerstein, M.T. 1987. Partners in evaluation. leadership. ASEAN Training Centre for Primary Health Care Development, 1(1), 34-35.
Geman, A., and D. Geman (1984). "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6, 721-41.
Giffords, E. D. (1998). Social work on the Internet: An introduction. Social Work, 43, 243-251.
Gingerich, W. J., & Green, R. K. (1996). Information technology: How social work is going digital. In P. Raffoul & C. A. McNeece (Eds.), Future issues for
Goodchild, M. (1989). "Modeling Error in Objects and Fields." In The Accuracy of Spatial Databases, edited by M. Goodchild and S. Gopal, pp. 107-13. London
Goodchild, M. (1990) "Spatial Information Science," Proceedings of the 4th International Geographical Union (Columbus, OH: 1990), 3-12.
Goodman, D. Fisher, E., Stukel, T and Chang, C. (1997) The Distance to Community Medical Care and the Likelihood of Hospitalizations: Is closer always better? American Journal of Public Health 87: 144-50
Griffith, D. A., R. P. Haining, and C. Arbia (1999). "Uncertainty and Error Propagation in Map Analyses Involving Arithmetic Operations: Inventory and Prospec" In Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources, edited by K. Lowell and A. Jaton, pp. 11-25. Chelsea , Mich. : Ann Arbor
Hanchette, C. 1998. GIS Implementation of 1997. CDC Guidelines for Childhood Lead Screening in North Carolina
Harris, Britton, and Michael Batty. 1993. Locational Models, Geographic Information and Planning Support Systems. Journal of Planning Education and Research 12,3:184-98.
Haynes, R., Bentham, G., Lovett, A. and Gale, S. (1999) Effects of Distances to Hospital and GP Surgery on Hospital Inpatient Episodes Controlling for Needs and Provisions. Soc Sci Med. 49:425-33
Haynes, R., Gale, S., Mugfort, M., and Davies, P. (2001) Cataract surgey in a Community Hospital Outreach Clinic: Patient Costs and Satisfaction. Soc Sci Med 53:1631-40.
Hirschfield, P., Brown, J. B., & Bundred, P. (1995). The spatial analysis of community health services on Wirral using geographic information systems. Journal of the Operational Research Society, 46, 147-159.
Johnson, A. K. (1998, March). Globalization from below: The use of the Internet to internationalize social work education. Paper presented at the Annual Program Meeting of the Council on Social Work Education, Orlando , FL.
Levine, N. (1996) Spatial statistics and GIS; software tools to quantify spatial patterns. Journal of the American Planning Association, Vol. 62.
Longley, P.A. Goodchild, M.F., Maguire, DJ., Rhind, DW. (1999) Geographical Information Systems. New York
Love, D., & Lindquist, P. (1995). The geographical accessibility of hospitals to the aged: A geographic information systems analysis within Illinois
Maguire, D. J. (1991). An overview and definition of GIS. In D. J. Maguire, M. F. Goodchild, & D. w. Rhind (Eds.), Geographical information systems: Principles and applications (Vol. 1: Principles, pp. 9-20). New York
Masser, I. and F.Toppen, 1992, Survey suggests GIS education trends in Europe , GIS Europe, pp 40- 43
McLafferty, S. 1998. GIS and Health Care. Annual Review of Public Health. Volume 24; pp 25-42.
Meyles, EW and de Bakker. (2002) Matching the supply of GIS educators with the demand of the GIS job market. Third European GIS Education Seminar EUGISES, Girona, Spain, September.
Mohan, J. 1993. Healthy Indicators? Applications of Census Data in Health Care Planning. In Population Matters: The Local Dimension (Eds) A Cahmpion, pp 136-149: Paul Chapman.
National Association Country City Health Office. (2002) Geographical Information Systems.
National Cancer Institute. (1992). State cancer control map and data system, version 8.4. Atlanta: Division of Cancer Etiology, Clinical Epidemiology Branch.
Nellis, M.D. (1994) "Technology in Geography Education: Reflections and Future Directions," Journal of Geography 93, no. 1 (January/February 1994): 36 39.
Petch, J.R., 2000, IT trends and GIS education and training, a white paper, EUGISES 2000, 7- 10 September, Budapest, Hungary
Queralt, M. and Witte, A. 1998. A map for you? Geographic information systems in the social services. Social Work, Vol. 43.
Rushton, G., & Frank, S. (1995). Sharing spatial data among social scientists. In H. J. Onsrud & G. Rushton (Eds.), Sharing geographic information (pp. 461-474). New Brunswick, NJ: Rutgers University.
Rushton, G., Elmes, G. and McMaster, R. (2000). Considerations for Improving Geographic Information Research in Public Health. Urban and Regional Information Systems Association Journal (March 2000).
Schervish, P. H. (1993). Information use by levels in human service organizations. Computers in Human Services, 9, 397-408.
Scholten, H.J.; de Lepper, M.J.C. 1991. The benefits of the application of geographical information systems in public and environmental health. WHO Statistical Quarterly, 44(3).
Siegel, J. (1995, May/June). The state of teacher training. Electronic Learning, pp. 43-53. social work practice (pp. 19-28). Boston: Allyn and Bacon.
Sui, D. (1995) "A Pedagogical Framework to Link GIS to the Intellectual Core of Geography," Journal of Geography 94, no. 6 (November/ December 1995): 578-591.
Taylor, J.R. 1982. An introduction to error analysis. Mill Valley, California: University Science Books.
Thurston, J., 2001, The New Geo-Jobs, Redefining Job Descriptions in the New Millennium, Geoinformatics, pp 12-15
Tufte, Edwin R. 1983. The Visual Display of Quantitative Information. Cheshire, CN: Graphics Press.
Urban and Regional Information Systems Association. 1994. 1994 Annual Conference Proceedings, Volume II: Abstracts. Milwaukee, WI: Urban and Regional Information Systems Association. U.S. Bureau of the Census. 1970.
Wain, R. (1993). The use of a geographical information system in locality profiling. Mapping Awareness, 7(8), 20-22.
World Health Organization. 2003. GIS and public health mapping. Communicable Disease Surveillance & Response. World Health Report 2003.
Appendix 1
Definition of Terms
Geographic Information System (GIS): It is a system that uses computers to enter, store, manage, analyze and present spatial data. The system brings together databases and graphics to make products, such as maps, data tables and charts.
Health surveillance: the continuous, systematic use of routinely collected, non-identifiable health data to guide public health action.
Information management: the ways that data are managed, analyzed and used.
Metadata: is descriptive information about each data set stored in the Spatial Data Warehouse. Metadata describes how and when and by whom a particular set of data was collected, updated and formatted. Metadata is essential for understanding information stored in data warehouses.
Public health: the science and practice of protecting and improving the health of a community through: population health assessment; health surveillance; health promotion; disease and injury prevention and; health protection
Public health professionals: people who provide programs and services, or work in an institution, that emphasize the prevention of disease and the health needs of the population as a whole. Working together, public health professionals maintain and improve the health of all people through collective or social actions.
Spatial data: is data that has geographical reference to a specific location on Earth. Spatial data is stored in the Spatial Data Warehouse including examples such as hospital locations, regional health area boundaries and more. Spatial data serves to link information to diseases, health risks and health determinants for visualization and analysis.
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