A geo-spatial examination of small-owned businesses of Kentucky.

Kunz, Michelle B. ; Ratliff, Janet M.

BACKGROUND OF GIS

Geographic Information Systems, referred to as GIS, have continued to develop and expand applications and uses as technology and computing power have developed. During the mid-1990s GIS technology became more prevalent with many applications. At that time Castle (1995) described GIS as a set of computerized tools that included both hardware and software which allowed the collection, storage, retrieval, transformation and display of spatial data. Essentially, GIS was an integration of data base management systems, and computerized mapping. Furthermore, additional computing capabilities termed geocoding, which matches street addresses from any databse, into GIS database processing, and provides a sophisticated spatial analysis (Drummond, 1995). Address matching is inexpensive, as well, since it can be performed on microcomputers with low-cost GIS software. Today, GIS has matured, and has proven to be a useful technology (Murray, 2010) with application in many disciplines. While GIS technology continues to evolve, so does location science in GIS application, and has great potential for advanced spatial analysis.

Applications and use of GIS technology have been used for target marketing research and community programs where the features allow analysis of labor force and demographics, as well as industry mix in a target region (Black & Powers, 1994). GIS has also been applied to gender research (Bosak & Schroeder, 2005). More recently, changing trends in information technology have influenced how GIS is used in spatial data management. Today it has evolved from traditional cartography and image process to advance 3D visualization and dynamic graphics and graphing tools. It has wide applications in many diverse fields (Khan, Akhter, & Ahmad, 2011). Location and Area Analysis

The use of GIS has many applications in the analysis of geographic locations, or areas of specific interest to researchers. Applications include the spatial characteristics of gaming venue catchment areas (Doran, Marshall, & McMillen, 2007), surrounding gambling facilities in Canberra, Australia, as well as mapping rural poverty-prone areas of Bangladesh (Ahamed et al., 2009). GIS analysis is also relevant to real estate investment and planning, in particular when demographic information can be combined with retail marketing information, allowing appraisers to develop ratio information on retail space square footage per person in a specific market area (Smith & Webb, 1997). Other general area-use applications include environmental and resource economics, in particular those concerned with spatial (area) variations (Bateman, Jones, Lovett, Lake, & Day, 2002). Geographic information systems (GIS) provide an unparalleled power to examine social, economic, and political circumstances (Haque, 2001).

Geographic Information Systems merge the graphic features of a map with its associated data, and because of this, in-depth analyses of geographic relationships are possible (Kowal, 2002). GIS is often defined by its method of storing data and features in layers, allowing users to overlay various types of information to view simultaneously.

Urban Analysis

GIS can contribute to the research field of urban analysis ("Urban analysis with GIS," 2000). The purpose of urban analysis is to explain the processes of spatial distributions in urban areas. Du (2000) determined that GIS was an efficient tool not only for the spatial structure analysis of an urban system, but also for the verification of socio-economic attributes and dynamics of an urban system. GIS is applicable to a wide range of public services and systems users, including city planners as they refine traffic control operations, along with transportation and maintenance departments, as well as property tax assessors (Goldstein, 1997). Further literature supports the use of GIS and urban planning, (Kohsaka, 2000), with the most successful application of GIS in local government, a system of inquiry on the content of urban plan decisions.

Health Services

One area where GIS is relatively new, but which appears to be growing rapidly is that of health care and health services, and particularly community health research (Faruque, Lofton, Doddato, & Mangum, 2003). Geographic information has been used by health scientists conceptually for a long time. Now, the user-friendly GIS tools that are available have been rapidly applied to health research. In particular, the location of disease incidence, health care facilities, community boundaries, surrounding environments, and epidemiological and health care studies. Two studies support the use of GIS relative to health care and community planning (Baum, Kendall, Muenchberger, Gudes, & Yigitcanlar, 2010) to manage and plan activities, and social and pastoral components of individual and community care (Boulos, 2003). In 2010 (Butler, Petterson, Bazemore, & Douglas, 2010) researchers used GIS to examine if the remoteness of specific areas reveals high need populations, when measured against the index of relative socio-economic disadvantage and physician-to-population ratios. Additional studies (Cordivano, 2011; Dubowitz et al., 2011; McLafferty, 2003; Schuurman, Leight, & Berube, 2008) have used GIS to analyze the delivery of needed health care facilities, outreach programs and services, need, access and utilization of health services, as well as allocation of services so that the maximum number of people may be served. Further application and use of GIS can be effective in assessment of environmental exposures, and integration with patient-reported environmental health information (Choi, Afzal, & Sattler, 2006). Thus the mapping capability of GIS is helpful for community and public health workers as they integrate environmental health assessment skills, and raise awareness of environmental health risk factors. Another study the same year, (Peled et al., 2006), used GIS to create thematic maps that identified clinics in southern Israel which treated children but failed to follow clinical guidelines. Other studies (Cinnamon, Schuurman, & Crooks, 2008; Vernon, 2011) have examined disparities of services, and used GIS to plan location of specifically needed services, such as pallative care, in order to provide locations that would facilitate serving the greatest number of people with limited resources, thus helping to reduce inefficiencies in health care systems and services.

Public Safety and Criminal Activity

Specific applications of GIS analysis are easily identified in examination of public safety and crime statistics. GIS is an extension of the old "pin in the map" strategy used to identify areas with high crime statistics, as well as to better understand the dynamics of crime and criminal activity (Ackerman & Murray, 2004). By understanding the spatial characteristics of crime via deployment of GIS, the Chicago police department saw an 18% drop in murders during a six month period. Another application (Ceccato & Haining, 2005), using demographic, socioeconomic and land use charcteristics as predictors of vandalism, mapped via GIS, calculated a vandalism ratio.

A study that integrated health concerns and crime combined data on local health, crime and demographics, with a GIS database, was developed to evaluate geographic epidemiology of sexually transmitted infections and HIV risk among adolescents (Geanuracos et al., 2007), and thus developed a tool for public health intervention planning. In conclusion, the position taken almost ten years ago by Koontz (2004) supports the collection and use of geospatial information as an essential tool to federal agencies. GIS is a critical tool in the areas of homeland security, healthcare, conservation of natural resources, as well as other applications.

SMALL BUSINESS OWNERSHIP

Research investigating small business ownership and/or entrepreneurship has reported noticeable differences between male and female motivations, operations and success. Those differences include factors as varied as: how men and women differ in their willingness to take on such a venture, reasons for pursuing the business venture, varying approaches used in performance, their growth and financial successes, to ultimately the various needs each have to succeed overall in their endeavor.

What makes women decide to go into business for themselves? Apergis and PekkaEconomou (2010) found the determinants of female entrepreneurs that were most prominent to be push and pull motives, effective mentoring, personal characteristics such as creativity, marital status, educational level, and a tolerance level for a risk of failure. Minniti (2010) studied 34 countries and investigated gender differences in entrepreneurs. She found differences primarily in perceptions of necessary skills and knowledge, attitude towards opportunities, not letting a threat of failure dictate activities, along with per capita GDP of a country where the entrepreneurial activity exists. "Experts in entrepreneurship--both women and men--say a lack of confidence and bluster, an aversion to risk, and a continued scarcity of women in engineering programs may explain the shortage" (Klein, 2011, p. 6) of women business owners. Regardless of what does or does not make females go into business for themselves, Minniti (2010) reminds us that the end result is the same, that the number of male entrepreneurs far exceeds the number of female entrepreneurs. In her review of studies spanning more than a decade, she concluded that world-wide, the ratio of female entrepreneurs to male entrepreneurs is a significant concern.

The role of women in today's work and cultural society is changing. Women account for a larger portion of college graduates, and an increasingly larger part of the workforce. Changing family dynamics as well as economic and financial issues all play a role in gender differences in the current business climate ("Developments in Women-owned Business, 1997-2007," 2011). Although women have improved their position as business owners over the most recent decades, advancing from the 1970's where there was less than 5% ownership in all US businesses to more than 25% ownership in all US businesses (Brush & Hisrich, 1991) there is still a large disparity in ownership by gender. Recently, a 2011 report from the Small Business Administration ("Developments in Women-Owned Businesses, 1997-2007," 2011), indicates the decade from 1997-2007 showed rapid growth in women-owned businesses, and concluded with almost 29% female ownership in 2007.

The issue remains the same today: women represent far fewer business owners than their male counterparts. This research study will uncover patterns of location in the numbers and placement of entrepreneurs/small business owners throughout Kentucky as represented by gender of ownership. Perhaps there is more than just a desire and innate abilities that influence an individual to open their own business. Variables which may have an influence could include: location, economic and environmental factors, income distribution, population and socio-demographic characteristics of a region.

PURPOSE OF STUDY

This purpose of this study is to examine the distribution of small businesses across the state of Kentucky, using GIS analysis. Geo-spatial analysis will provide a visual representation of the location of small businesses in Kentucky. Economics, population and environmental factors all influence where individuals might choose to locate a business. Thus, inclusion of median income and population pattern changes should also be "mapped." Furthermore, given the review of gender differences in today's workforce, examination of ownership by gender is warranted.

State Geographic Boundary Structures

There are three basic "boundary maps" that can be identified in Kentucky. The first is the map of the 120 counties in the state. The other two include the assigned areas to each of the 15 Small Business Development Centers (SBDC), which work in conjunction with the US Small Business Administration, and the 15 Area Development Districts (ADD). Both the SBDC and the ADD respective assigned service areas use county boundaries.

Small Business Development Centers

The Kentucky Small Business Development Center is co-sponsored by the U.S. Small Business Administration and is administered by the University of Kentucky in partnership with regional universities, community and private colleges, and the private sector ("About Us," 2012). The mission of the KSBDC is to strengthen the state's economy by providing business services that assist entrepreneurs and small business owners in creating both wealth and jobs. There are 15 SBDC offices serving 13 center areas. Figure 1 ("Kentucky Small Business Development Center," 2012) is the map of the 13 SBDC service regions.

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Area Development Districts

The Kentucky Council of Area Development Districts originated in the early 1960's ("History of the Area Development Program," 2009) as Area Development Councils, which were organized in all counties. Fifteen Area Development Districts were formed from 1966 to 1972. The ADDs were designed to bring local civic and governmental leaders together, and to take advantage of opportunities that could not be achieved by these entities acting alone. The ADDs serve as forums, clearinghouses, technical centers and conveners for the region ("About the Area Development Districts," 2009), and have both federal and state statutory authority. Figure 2 is a map of the ADD locations downloaded from the website at: http://www.kcadd.org/District_Contacts.html. While the SBDC centers' service regions, and ADD individual districts are not identical, for the most part, the respective regions cover approximately the same counties. Since Area Development Districts are used for reporting data at the federal level, this is the most appropriate GIS mapping tool for this study.

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The research questions for this study are:

Q1 Can patterns of concentration/distribution of SBA registered businesses be identified?

Q2 Do patterns of concentration/distribution of SBA registered businesses differ based upon gender of ownership?

Q3 How do patterns of concentration/distribution of SBA registered businesses relate to Area Development Districts (ADD) in Kentucky differ based upon of gender ownership?

Given general knowledge of the economic activity with the state of Kentucky, it is predicted that business locations will be concentrated in or near the golden triangle of Louisville, Lexington and northern Kentucky (near Cincinnati, OH); near metropolitan or heavier populated areas; and are more likely to be near an interstate or major highway.

METHODOLOGY

Data Collection

The Small Business Administration website provided the source for this research ("Dynamic Small Business Search," 2012). This is a self-certifying database, which means individual business owners submit the information provided as well as any certifications or special status indicators. The SBA does not make any representation as to the accuracy of any of the data included, other than certifications relating to 8(a) Business Development, HUBZone or Small Disadvantaged Business status. The SBA strongly recommends that contracting officers diligently review a bidder's small business self-certification before awarding a contract. Data can be identified and screened for ownership and self-certifications. This data was selected for the entire state of Kentucky, and then a separate download was selected for women-owned businesses (WOB).

Analysis was conducted based upon the entire state dataset, sorted by gender of ownership. The address of individual businesses was geo-coded using ArcMap 10.0. After cleaning the data, and removing duplicate addresses, there were 2793 male-owned businesses (MOB) and 729 women-owned businesses (WOB). This is a ratio of male to female owned businesses: 2793:729, which indicates 26% of those registered with the SBA are women owned businesses. When analyzing the data in GIS software, 100% of the MOB addresses could be identified, while only 90% of WOB addresses could be mapped, for a total of 657. According to the GIS consultant, 80% matching results are considered to be quite good.

RESULTS

As predicted, the resulting map of the full data set shows concentrations around the major metropolitan areas of Louisville, Lexington, and northern Kentucky bordering Cincinnati, Ohio. (Figure 3). There is a slight (light) pattern that parallels the two north-south interstate highways, (I-75, I-65). The remaining points are distributed fairly evenly across the state. Examination of the patterns by ADD, shows ADD 5 and 6 with higher concentrations, in addition to the districts which contain Louisville, Lexington and northern Kentucky (the golden triangle). The SBDC regions closely parallel the ADD districts, as the two groups' identified service areas, while not identical, are quite similar. Thus the same area SBDC offices serve many of the same counties.

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When examining the map for the male owned businesses (MOB), Figure 4, only concentrations around the three metropolitans are evident. In addition, the concentration in northern Kentucky is much lighter. There are no visible patterns following interstate highways, but there is representation in every county across the state. When examining for ADD and SBDC regions, again the same two ADD districts, 5 and 6, appear to have a slightly higher number of MOB.

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Examination of WOB patterns show the same higher concentration of business in the three metro areas, as well as patterns down the two north-south interstate corridors, Figure 5. However the most obvious difference is that the degree of concentration for the WOB in these three metro areas is much denser than the same areas for male-owned businesses. Eastern Kentucky has fewer WOB locations than west-central and western Kentucky. In addition there are several counties that are not represented on this map. This means that 28% (34 of a total 120) of the counties in Kentucky have no registered women-owned businesses. Further examination found these under-represented ADD districts with multiple counties, Figure 5, were in the eastern one-third of the state. In addition, district 15 (ADD) which borders Tennessee, and is in south-central Kentucky has the largest percentage of counties with no women owned businesses; seven of the ten counties in this district have no WOB.

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The original research questions for this study asked if patterns could be identified, based upon location and gender of ownership as well as geographically identified service regions. The answers to the individual research questions are presented here. Research question one: Can patterns of concentration/distribution of SBA registered businesses be identified? Yes, in particular concentrations are evident in the metro areas, or what is termed the "Golden Triangle" in Kentucky. Research question two: Do patterns of concentration/distribution of SBA registered businesses differ based upon gender of ownership? Yes, again concentrations in the three metro areas are evident, but when examining male-owned business, the remaining locations show fairly equal distribution across the state. Women owned businesses follow the metro concentration pattern, but also parallel the north-south interstates, and are also not represented in more than one-fourth of all counties statewide. One south-central ADD had only 30% of the counties with registered WOB. Research question three: How do patterns of concentration/distribution of SBA registered businesses relate to Area Development Districts (ADD) in Kentucky differ based upon of gender ownership? Examination of WOB patterns, found eastern Kentucky ADD/SBDC areas to be under-represented. Two districts in west-central Kentucky showed slightly higher concentration of male-owned businesses.

SUMMARY AND CONCLUSIONS

The use of GIS analysis was effective for this study and the visual presentation provided the researchers with distinct patterns of the data. Further analysis and comparison of economic and population variables provided additional insights into GIS patterns. Overall, patterns were identified relating to distribution and location of small businesses, as well as gender of ownership.

For the data analyzed, there is a concentration of businesses in higher populated areas. Upon examination of 2010 census data, Figure 6, small business locations seem to lie in the golden triangle, and follow interstate routes, which are areas showing moderate to high population growth. Areas that show under representation, in particular women owned businesses, tend to follow those areas with population loss, ("County-Level Population Data for Kentucky: Percent Change in Population, 2000-10," 2011).

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When analyzing the median income by county ("County-Level Population Data for Kentucky: Percent Change in Population, 2000-10," 2011), the areas which are underrepresented by WOB, and that have lower presence of small-owned businesses are those with median income below $37,500 (Figure 7). Thus, the distribution of income, and those counties with low median income are much less likely to have SBA registered businesses, regardless of owner gender. Conversely, the two ADD districts with higher concentrations of male-owned businesses have individual counties with higher median income levels. One might question, which is the cause, which is the effect: higher income or business ownership? This analysis cannot answer that question. Another theory which might answer this question comes from concept of cluster and cluster development (Rahman, 2011), which theorizes that a region may be more entrepreneurial than others, and therefore, attracts more innovative and economic activity. Rahman also posits the concept of knowledge spillover as a possible contributing factor for regional diversity, and supports theories that suggest economic growth in cities can be the result of entrepreneurial activity in the respective city or area.

LIMITATIONS AND FUTURE RESEARCH

The dataset acquired for this study is self-reported, and therefore it may not be representative of all small businesses in the state. In addition, there is no indication on the SBA website as to how recently the data was submitted by the business owners or how often this data is updated. Not all of the women-owned businesses could be analyzed, due to non-matching addresses, which may be a result of corrupted data, or of rural addresses that cannot be matched to the GIS street information.

Further analysis could include other sub-classifications from the SBA database, such as economically disadvantaged, minority owned, veteran-owned, NAICS classification, or type/nature of business. Additional analysis could be conducted state-by-state, thus comparing nation-wide data. GIS analysis could provide insights into under-represented areas where SBDC offices or ADDs could be identified to assist small business development and economic growth. Additionally regional universities and colleges of business could also provide resources and assistance in areas of need. Further data on income and population changes could be tracked longitudinally to analyze how these variables affect small business ownership. This type of data analysis could also be utilized by small business owners to look for opportunities to start, develop or further expand business in specific locations based upon identified characteristics of an area.

REFERENCES

About the Area Development Districts. (2009) Retrieved February 10, 2012, from http://www.kcadd.org/what_is_ADD.html

About Us. (2012) Retrieved February 10, 2012, from http://ksbdc.org/ about-us/ Kentucky Small Business Development Center. (2012) Retrieved February 1, 2012, from http://www.ksbdc.org

Ackerman, W. V., & Murray, A. T. (2004). Assessing spatial patterns of crime in Lima, Ohio. Cities, 21(5), 423437.

Ahamed, T., Khan, M. I., Takigawa, T., Koike, M., Tasnim, F., & Zaman, J. M. (2009). Resource management for sustainable development: a community- and GIS-based approach. Environment, Development and Sustainability, 11(5), 933-954.

Apergis, N., & Pekka-Economou, V. (2010). Incentives and female entrepreneurial activity: evidence from panel firm level data. International Advances in Economic Research, 16(4), 371-387.

Bateman, I. J., Jones, A. P., Lovett, A. A., Lake, I. R., & Day, B. H. (2002). Applying geographical information systems (GIS) to environmental and resource economics. Environmental and Resource Economics, 22(1-2), 219-269.

Baum, S., Kendall, E., Muenchberger, H., Gudes, O., & Yigitcanlar, T. (2010). Geographical information system: an effective planning and decision-making platform for community health coalitions in Australia. Health Information Management Journal, 39(3), 29-33.

Black, S., & Powers, G. (1994). A GIS-based approach to community analysis for targeted marketing. Economic Development Review, 12(2), 69.

Bosak, K., & Schroeder, K. (2005). Using geographic information systems (gis) for gender and development. Development in Practice, 15(2), 231-237.

Boulos, M. N. K. (2003). Geographic information systems and the spiritual dimension of health: a short position pape. International Journal of Health Geographics, 2, 6-4.

Brush, C. G., & Hisrich, R. D. (1991). Antecedent influences on women-owned businesses. Journal of managerial Psychology, 6(2), 9-16.

Butler, D. C., Petterson, S., Bazemore, A., & Douglas, K. A. (2010). Use of measures of socioeconomic deprivation in planning primary health care workforce and defining health care need in Australia. Australian Journal of Rural Health, 18(5), 199-204.

Castle, G. (1995). Geographic information systems (GIS): The location, location, location technology. Real Estate Issues, 20(2), 52-52.

Ceccato, V., & Haining, R. (2005). Assessing the geography of vandalism: Evidence from a Swedish city. Urban Studies (Routledge), 42(9), 1637-1656.

Choi, M., Afzal, B., & Sattler, B. (2006). Geographic information systems: a new tool for environmental health assessments. Public Health Nursing, 23(5), 381-391.

Cinnamon, J., Schuurman, N., & Crooks, V. A. (2008). A method to determine spatial access to specialized palliative care services using GIS. BMC Health Services Research, 8, 1-11.

Cordivano, S. (2011). Maternity ward closures in Philadelphia: using GIS to measure disruptions in essential health services. Journal of Map & Geography Libraries, 7(3), 282.

County-level population data for Kentucky: percent change in population, 2000-10. (2011, March 30) http://www.ers.usda.gov/Data/Population/ PopList.asp?ST=KY& LongName=Kentucky%29...county, Retrieved February 10, 2012.

Developments in women-owned business, 1997-2007 (Office of Advocacy, Trans.). (2011). In W. Sargeant (Ed.). Washington DC.

Doran, B. J., Marshall, D. C., & McMillen, J. (2007). A GIS-based investigation of gaming venue catchments. Transactions in GIS, 11(4), 575-595.

Drummond, W. J. (1995). Address matching--GIS technology for mapping human activity patterns. American Planning Association. Journal of the American Planning Association, 61(2), 240-240.

Du, G. (2000). Using GIS for analysis of urban systems. GeoJournal, 52(3), 213-213-221.

Dubowitz, T., Williams, M., Steiner, E. D. M. P. P., Weden, M. M. P., Miyashiro, L., Jacobson, D., & Lurie, N. (2011). Using geographic information systems to match local health needs with public health services and programs. American Journal of Public Health, 101(9), 1664-1665.

Dynamic small business search. (2012, 2004) Retrieved January 19, 2012, from http://dsbs.sba.gov/dsbs/search/dsp_dsbs.cfm

Faruque, F. S., Lofton, S. P., Doddato, T. M., & Mangum, C. (2003). Utilizing geographic information systems in community assessment and nursing research. Journal of Community Health Nursing, 20(3), 179-191.

Geanuracos, C. G., Cunningham, S. D., Weiss, G., Forte, D., Reid, L. M. H., & Ellen, J. M. (2007). Use of geographic information systems for planning HIV prevention interventions for high-risk youths. American Journal of Public Health, 97(11), 1974-1981.

Goldstein, H. (1997). Mapping convergence GIS joins the enterprise. Civil Engineering, 67(6), 36-39.

Haque, A. (2001). GIS, public service, and the issue of democratic governance. Public Administration Review, 61(3), 259-265.

History of the Area Development Program. (2009) Retrieved February 10, 2012, from http://www.kcadd.org/Our_history.html

Khan, K. A., Akhter, G., & Ahmad, Z. (2011). GIS as project manager in geophysical applications software. Journal of Geographic Information System, 3(2), 166-172.

Klein, K. E. (2011). Business plan contests: where are the women? BusinessWeek.com, 6-6.

Kohsaka, H. (2000). Applications of GIS to urban planning and management: Problems facing Japanese local governments. GeoJournal, 52(3), 271-280.

Koontz, L. D. (2004). Geospatial information: better coordination and oversight could help reduce duplicative investments: GAO-04-824T. GAO Reports, 1.

Kowal, K. C. (2002). Tapping the Web for GIS and mapping technologies: For all levels of libraries and users. Information Technology and Libraries, 21(3), 109-114.

McLafferty, S. L. (2003). GIS and health care. Annual Review of Public Health, 24(1), 25.

Minniti, M. (2010). Female entrepreneurship and economic activity. [Article]. European Journal of Development Research, 22(3), 294-312.

Murray, A. (2010). Advances in location modeling: GIS linkages and contributions. Journal of Geographical Systems, 12(3), 335-354.

Peled, R., Reuveni, H., Pliskin, J. S., Benenson, I., Hatna, E., & Tal, A. (2006). Defining localities of inadequate treatment for childhood asthma: A GIS approach. International Journal of Health Geographics, 5, 3-9.

Rahman, M. (2011). Entrepreneurship and urban growth: dimensions and empirical models. Journal of Small Business and Enterprise Development, 18(3), 608-626.

Schuurman, N., Leight, M., & Berube, M. (2008). A Web-based graphical user interface for evidence-based decision making for health care allocations in rural areas. International Journal of Health Geographics, 7, 1-12.

Smith, C. A., & Webb, J. R. (1997). Using GIS to improve estimates of future retail space demand. The Appraisal Journal, 65(4), 337-341.

Urban analysis with GIS. (2000). Geo Journal, 52(3), 181-188.

Vernon, Z. D. T. G. H. (2011). Role of race and ethnicity predictive modeling and spatial analysis in addressing health disparities. Journal of Map & Geography Libraries, 7(3), 304.

Michelle B. Kunz, Morehead State University

Janet M. Ratliff, Morehead State University