Anna S. Reading a, Janet M. Scarlett a & Elizabeth A. Berliner a a Maddie's Shelter Medicine Program, Department of Population

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1 This article was downloaded by: [Dr Kenneth Shapiro] On: 09 June 2015, At: 11:58 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Click for updates Journal of Applied Animal Welfare Science Publication details, including instructions for authors and subscription information: A Novel Approach to Identify and Map Kitten Clusters Using Geographic Information Systems (GIS): A Case Study From Tompkins County, NY Anna S. Reading a, Janet M. Scarlett a & Elizabeth A. Berliner a a Maddie's Shelter Medicine Program, Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University Published online: 25 Apr To cite this article: Anna S. Reading, Janet M. Scarlett & Elizabeth A. Berliner (2014) A Novel Approach to Identify and Map Kitten Clusters Using Geographic Information Systems (GIS): A Case Study From Tompkins County, NY, Journal of Applied Animal Welfare Science, 17:4, , DOI: / To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

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3 JOURNAL OF APPLIED ANIMAL WELFARE SCIENCE, 17: , 2014 Copyright Taylor & Francis Group, LLC ISSN: print/ online DOI: / ARTICLES A Novel Approach to Identify and Map Kitten Clusters Using Geographic Information Systems (GIS): A Case Study From Tompkins County, NY Anna S. Reading, Janet M. Scarlett, and Elizabeth A. Berliner Maddie s Shelter Medicine Program, Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University A retrospective study using a geographic information system (GIS) was conducted to capture, map, and analyze intake data of caregiver (owner)-surrendered kittens (aged 0 6 months) to the Society for the Prevention of Cruelty to Animals (SPCA) of Tompkins County, NY, from 2009 to Addresses of caregiver-surrendered kittens during the study period were mapped (n D 1,017). Mapping and analysis of the resultant data set revealed that the distribution of kittens was nonrandom. Seventeen statistically significant (p D.001) clusters were identified, 1 of which was the SPCA of Tompkins County (due to anonymously surrendered nonhuman animals). The remaining 16 clusters were composed of 52 homes; 27.5% (280/1,017) of the kittens in the data set originated from these 52 homes. The majority of kittens within clusters were surrendered from high-density residential and manufactured residential home parks. Analyzing such clusters using GIS is a novel approach for targeting spay/neuter and educational programs to areas contributing disproportionately to shelter populations. This method may prove useful to help shelters more effectively allocate their limited resources, but further evaluation of this and other targeted approaches is needed to assess the long-term efficacy of such programs. Keywords: GIS, animal shelter, cats Anna S. Reading is now at University Animal Hospital, Tempe, AZ. Correspondence should be sent to Elizabeth A. Berliner, Maddie s Shelter Medicine Program, Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Box 26, Ithaca, NY eab35@cornell.edu 295

4 296 READING, SCARLETT, BERLINER Despite significant efforts to reduce the population of unwanted cats in the last several decades, euthanasia in nonhuman animal shelters remains a leading cause of death of cats in the United States (Kass, 2007). Nonlethal methods that have been used to reduce populations of unwanted cats include identifying risks for relinquishment, providing access to and education about subsidized spay and neuter opportunities, instituting trap neuter return programs, and determining factors that contribute to adoption (Kass, 2007). In spite of these efforts, however, cat populations in U.S. animal shelters largely remain constant or are increasing (Lord et al., 2006; Morris, Wolf, & Gies, 2011). The reasons for the persistently high numbers of cats in shelters are multifold. One crucial contributor is the tremendous fecundity of the species. Cats are polyestrous and can become pregnant at any time of year, although greater numbers of litters are observed in warmer months. Additionally, a lactational anestrous does not occur in cats, so queens can become pregnant even while nursing a litter. Finally, reproductive maturity may occur as young as 4 months of age (Senger, 2003). This early reproductive maturity leads to unexpected litters from queens whose caregivers (owners) intended to have their cats spayed prior to reproduction but did not anticipate pregnancies at such a young age. According to New et al. (2004), almost 2 times as many households with cats reported having at least one litter in 1996 compared with households with dogs. More than two thirds of these cat litters were unplanned, with approximately 2.1 million unplanned cat litters in that year. A second contributor to the large numbers of cats in shelters is the very different relationships that cats may have with people compared with dogs. In addition to serving as companions, cats can live without caregivers and often roam free and experience varying levels of human contact. Such cats can be fed by one or several people, can live on the streets in colonies overseen by caretakers, or can have essentially no interaction with humans and be left to fend for themselves, much like wildlife. Except for those animals solidly in the companion category, these other cats are likely to be sexually intact because no one assumes personal and financial responsibility for having them spayed or neutered (Kass, 2007). This large, free-roaming cat population in the United States is estimated to be approximately 50 million (Levy & Crawford, 2004). The neuter rate among these cats is 2% (Wallace & Levy, 2006) compared with 82% of cats with caregivers (Trevejo, Yang, & Lund, 2011). Although the number of kittens entering shelters from the free-roaming segment of the cat population has not been definitively quantified, given the high percentage of intact cats in this group, it is widely accepted that they are a persistent source of kittens entering animal shelters. EFFECTIVENESS OF SPAY/NEUTER PROGRAMS Surprisingly few studies have evaluated the effectiveness of subsidized spay/neuter programs on reducing animal shelter intakes (Frank & Carlisle-Frank, 2007; Scarlett & Johnston, 2012; White, Jefferson, & Levy, 2010). Frank and Carlisle-Frank (2007) failed to demonstrate an effect of subsidized spay/neuter clinics on cat intake into animal shelters in five communities in the United States. White et al. (2010) reported statistically significant but very modest declines in the rates of cat intakes in both New Hampshire and Austin, TX, following the initiation of a subsidized statewide spay/neuter program (New Hampshire) and a subsidized municipal program (Austin, TX). Similarly, there was a modest decline in the median number

5 IDENTIFYING AND MAPPING KITTEN CLUSTERS USING GIS 297 of cats entering a small shelter following the doubling of spay/neuter efforts for cats in the county of Transylvania, NC (Scarlett & Johnston, 2012). Despite millions of dollars devoted to subsidized spay/neuter efforts for cats throughout the country, the number of cats entering animal shelters has held stubbornly constant in most shelters over time. THE ROLE OF GIS IN A SHELTER MEDICINE CONTEXT New approaches to using shelter intake data may help focus spay/neuter and other preventive efforts to more effectively reduce shelter populations. Some programs are evaluating the geographic origin of their cats with the intent of targeting preventive programs to those areas where the largest numbers of cats originate (Weiss, 2010). One approach has been to examine intake by zip codes (White et al., 2010), but unfortunately, zip codes often encompass large areas and/or large numbers of households, making it difficult to identify practical target areas. A more sophisticated tool to analyze the spatial components of cats entering shelter populations might be found in a geographic information system (GIS). A GIS is a computer database with the capability to capture, analyze, and display spatial data. GIS have been used extensively in veterinary medicine, although their use has been largely limited to the fields of parasitology, disease epidemiology, and conservation medicine (Blondin, Baumgardner, Moore, & Glickman, 2007; Clevenger, Wierzchowski, Chruszcz, & Gunson, 2002; Durr & Gatrell, 2004; Norman, 2008; Odoi et al., 2004; Rinaldi, Musella, Veneziano, Condoleo, & Cringoli, 2009). Few studies exist that apply GIS capabilities to shelter medicine (Aguilar & Farnworth, 2012; Patronek, 2010a, 2010b; Sokolow et al., 2005). Despite the paucity of published studies, many possibilities exist for analyzing shelter data from a spatial perspective. Incorporating a spatial component to data analysis expands the number of relevant questions from the basic question How many animals are entering the shelter? to include: Where are the animals coming from? Are there patterns to the distribution of entering animals? What are the relationships between these animals and their environments? The ultimate goals of such questions are to identify opportunities for intervention and to enact effective targeted strategies to reduce shelter populations. The objectives of this study were twofold: first, to map and analyze the distribution of caregiver-surrendered kittens in a community to identify areas for targeted intervention; and second, to broadly demonstrate the power of GIS to greatly improve the ways in which shelters can view, analyze, and use their intake data. Study Population MATERIALS AND METHODS Intake data regarding owner-surrendered kittens (0 6 months of age) were retrieved from a PetPoint database used by the Society for the Prevention of Cruelty to Animals (SPCA) of

6 298 READING, SCARLETT, BERLINER Tompkins County, NY, for the period 2009 to This shelter is located in a largely rural county in central New York and services an area of 475 square miles ( square kilometers) with an estimated population of 101,000 people (U.S. Census Bureau, 2010). The average annual intake for cats during the period of study was 1,573, with cats accounting for 73% of all entering animals. Kittens (aged 0 6 months old) comprised 49.8% of the entering cat population and largely entered the shelter during the months of May to September (SPCA of Tompkins County, 2011). The shelter conducts cruelty investigations for Tompkins County and provides animal control services for the majority of towns in the county. Study Group Selection and Geocoding Upon intake to the SPCA of Tompkins County, each kitten had information recorded in a PetPoint database that included the following attributes: a unique identifier, date, breed, color, sex, neuter status, impound status, location found (if applicable), caregiver address (if applicable), and municipality of origin. For the purposes of this analysis, only data relating to caregiver-surrendered kittens aged 0 to 6 months were selected, because stray animals did not have the complete location information needed to create the GIS database. Kittens seized for legal reasons were excluded because efforts to control reproduction in these households already had been implemented. Returned adoptions were excluded to avoid double counting individual kittens. Lastly, the study group was further refined by removing the small number of kittens arriving at the shelter from outside of Tompkins County (5.1% of all kittens) and those of unknown jurisdiction (0.3% of all kittens). Once the data set was defined, caregiver addresses were carefully reviewed for errors and completeness. Spelling errors and omissions were corrected when it was clear that doing so would not alter the original location. For example, errors such as raod were corrected to road, and fields missing a state designation (NY) were filled in. Any records that had incomplete addresses that were not amenable to correction, such as along Highway 96, were excluded, as they could not be accurately mapped. After processing the address fields, the data were geocoded using the North American Address Locator in ArcMap 10.1 (Environmental Systems Resource Institute [ESRI], 2012). Geocoding enables addresses in tabular form to be captured by a GIS and displayed as points on a map. Any record that did not match with at least 98% accuracy to an interpolated street address was further reviewed and resubmitted. For those that could not be matched, an interactive geocoding tool was used to manually place points in their correct locations (ESRI, 2012). Upon completion of the geocoding process, 92% of the original records were mapped. This percentage is similar to other published studies using geocoding as a component of the data capture process (Patronek, 2010b). Once a geocoded data set was created, land use data (Heller, 2009) were overlaid and appended to each caregiver-surrendered kitten record to further examine the relationship of the environments from which surrendered kittens were originating. For ease of use, the numerous land use categories from the original data set were consolidated into seven broad land use categories from which kittens were surrendered (see Table 1). The relationship, if any, between land use and the kitten population was explored based on conversations with shelter staff. Anecdotally, it was perceived that high numbers of kittens were surrendered from residential home parks; however, this perception had never been scientifically explored.

7 IDENTIFYING AND MAPPING KITTEN CLUSTERS USING GIS 299 TABLE 1 Land Use Categories Low-Density Residential Medium-Density Residential High-Density Residential Residential Park Commercial/Mixed Use Public Agriculture and Open Space Spatial Analysis Residential land areas with a maximum average of one dwelling per acre. Residential land areas with more than one but fewer than five dwellings on average per acre. Residential land areas with approximately five or more dwellings on average per acre. Composed mainly of urban areas of residential land use patterns including densities ranging from single-family structures to multiunit apartment buildings. Residential land areas with a density of four or more manufactured homes on average per acre and a designation of the property as a manufactured home park or subdivision. Retail/shopping or other commercial and commercial/residential centers of cities and villages where mixed land uses of commercial, public/institutional, and highdensity residential areas exist. Publicly owned facilities (e.g., schools, utility facilities, cemeteries, transportation corridors, etc.). Includes barren and active agricultural land, as well as formally and informally delineated nonurbanized open spaces. Following creation of the geocoded database, two methods of spatial analysis were used to identify geographic patterns in the data. First, a kernel density map was created using the Spatial Analyst extension in ArcMap This enabled the addresses to be viewed in a quantitative manner (number of surrenders per square mile). To further identify and define areas contributing high numbers of kittens (clusters), the data set was imported into CrimeStat III, a freely downloadable spatial statistics application (Levine, 2010). Although originally developed for use in crime analysis, the software has been used to evaluate data in a number of professional fields, including veterinary medicine (Baumgardner et al., 2005). A nearest-neighbor hierarchical analysis was applied to identify pairs of points that were grouped more closely together than would be expected from random distribution. This technique inspected all points within a defined geographic area and calculated the mean distance between nearest neighbors. If the distance between two points in a data set was less than expected, clustering was indicated (Carpenter, 2001). As many of the kittens entering the SPCA were from the same litter, an inherent amount of clustering was expected. To minimize the chance that a cluster would be formed by kittens from a single litter, the minimum number of points (kittens) per cluster was set to 12 (roughly twice the maximum litter size; Nutter, Levine, & Stoskopf, 2004). The probability of grouping a pair of points by chance was set to 0.1% to minimize forming a cluster based on spatial randomness. Statistical Analysis To evaluate whether kitten clustering was associated with land use, the distribution of land use was compared between kittens from clusters and kittens outside of clusters using a chi-square test of independence. Because this overall association was significant, odds ratios calculated according to Dawson and Trapp (2004) were then calculated for each land use type in relation

8 300 READING, SCARLETT, BERLINER to low-density residential land use, which served as the referent category. The p values.05 were considered statistically significant. RESULTS Kittens comprised 49.8% (2,219/4,453) of the cat intake from within Tompkins County during 2009 to 2011, and caregiver-surrendered kittens represented 49.8% (1,105/2,219) of the kitten intake (Table 2). Seized and returned kittens (omitted from the analysis) represented only 3.0% (66/2,219) of kitten intake from within Tompkins County. Eight percent of caregiversurrendered kittens could not be mapped due to insufficient location information. The final data set was composed of 1,017 kittens, representing 92% (1,017/1,105) of the caregiver-surrendered kittens during the study period. Prior to performing the spatial analysis, visual inspection of the geocoded addresses did not reveal unexpected clustering (see Figure 1). The points appeared more dense in areas of greater human population (within population centers of Ithaca, Dryden, and Groton). However, as each point represented only one address, multiple kittens originating from the same address would not be discernible by visual inspection, therefore necessitating the need for further spatial analysis. Creation of a kitten population density map helped clarify areas within Tompkins County contributing proportionately more (or fewer) caregiver-surrendered kittens (see Figure 2). From this analysis, several areas with high densities of kittens became apparent, and many of these were found away from urban population centers. After conducting the cluster analysis, 17 statistically significant clusters were identified (p D.001). However, 1 cluster was the SPCA of Tompkins County due to kittens being surrendered anonymously afterhours. The remaining 16 clusters were composed of 52 addresses and accounted for 27.5% (280/1,017) of the total number of kittens within the data set. Figure 3 displays the distribution of these clusters within Tompkins County, symbolized to indicate the number of kittens found within each cluster during the study period. Clusters were composed of eight or fewer residences. In 6 of the 16 clusters (excluding the SPCA of Tompkins County), a single residence composed the entire cluster and the number of kittens surrendered from these individual addresses ranged from 13 to 19. The remaining TABLE 2 Cat Intake From Within Tompkins County by Age and Source: Age of Cat 6 Months or Older Younger Than 6 Months Source of Cat No. % No. % Caregiver-surrendered 1,105 (49.8) 969 (43.4) Stray 1,048 (47.2) 984 (44.0) Seized, returned adoptions 66 (3.0) 281 (12.6) Total 2,219 2,234

9 IDENTIFYING AND MAPPING KITTEN CLUSTERS USING GIS 301 FIGURE 1 Origins of kittens surrendered by caregiver to the SPCA of Tompkins County from 2009 to 2011 (color figure available online). 10 clusters were made up of more than one residence, ranging from two to eight addresses per cluster. The largest number of kittens from a single cluster was 28 kittens from a group of five neighboring homes. The characteristics of each cluster are listed in Table 3. Table 4 illustrates the results of the statistical analysis of land use origins of clustered versus nonclustered kittens. Although the largest proportion of kittens were surrendered from addresses within low-density residential areas, the pattern of land use varied significantly for kittens in clusters compared with those not in clusters (p <.0001). Kittens from identified clusters were 16.1 times more likely to come from residential parks and 13.1 times more likely to come from high-density residential areas than from low-density residential areas compared with kittens who were not from clusters (Table 4). DISCUSSION GIS was used to geocode and analyze kitten intake data and identify areas for targeted intervention. Fifty-two addresses from a county of approximately 40,000 households were

10 302 READING, SCARLETT, BERLINER FIGURE 2 square mile. Density of caregiver-surrendered kittens in Tompkins County from 2009 to mi. sq. D identified in 16 clusters that contributed 27.5% of kittens included in the data set. Unpublished data from this shelter during 2006 to 2008 (Reading, 2012) show similar trends in both the composition and location of the clusters, suggesting that kitten clustering is a persistent and real factor in the county s animal shelter population dynamics. In several clusters, a single address was responsible for contributing as many as 19 kittens to the shelter during the study period. Often, these addresses contributed several kittens each year, indicating that there is an ongoing source of kittens in the area; these results suggest that the targeting of individual households for outreach could make a significant impact on the number of kittens entering this shelter every year. Additionally, the data suggest that in this community, clustered kittens were most likely to originate from manufactured home residential parks and high-density residential areas. These land uses also have higher human population densities; part of the clustering observed may be due to this connection. However, many high-density residential areas and residential parks are present that do not give rise to kitten clusters. Further examination of why clustering occurred is warranted, but it was outside of the scope of this

11 IDENTIFYING AND MAPPING KITTEN CLUSTERS USING GIS 303 FIGURE 3 square mile. Clusters of caregiver-surrendered kittens within Tompkins County from 2009 to mi. sq. D study; future population reduction efforts within the county may benefit from targeting clusters in these land use areas. It should be noted that census income data were also considered for use in this study to further characterize the socioeconomic environments from which surrendered kittens originate. However, the scale of available census data did not lend itself to use on an address-by-address basis. The most detailed and current data available are collected on a block-group level, which often covers very large land areas. Within block groups, there is likely great variation, limiting the usefulness of this information for a study seeking to identify patterns on the scale of individual addresses. For this reason, census data were not considered appropriate for inclusion in the current study. The results of this study show the strength of GIS to find and highlight areas that are disproportionately contributing kittens to the county s shelter. It is important to note that it is a screening tool and should only be viewed as a first step. Once identified, these clusters can be further investigated to ascertain the reason for the clustering. For instance, is a cluster due to residents surrendering kittens from a feral cat colony? If so, a trap-neuter-return program may

12 304 READING, SCARLETT, BERLINER TABLE 3 Number of Addresses and Number of Surrendered Kittens Per Cluster Cluster No. of Addresses No. of Kittens Surrendered Land Use Type A 5 28 B 7 27 C 2 22 D 8 20 E 5 19 F 1 19 G 6 18 H 2 18 I 1 15 J 1 14 K 4 14 L 1 14 M 1 13 N 3 13 O 1 13 P 4 13 Total TABLE 4 Comparison of Type of Land Use Area for Kittens Originating in Clusters Versus Kittens not Originating in Clusters Kittens From Clusters b (%) Kittens Not Clustered c (%) Odds Ratio (OR) 95% CI for OR p Values Low-Density Residential Medium-Density Residential High-Density Residential <.0001 Residential Park <.0001 Agricultural/Open Space <.0001 Other a Note. CI D confidence interval. a Other includes public, commercial/mixed-use land. b Kittens from the cluster at the Society for the Prevention of Cruelty to Animals (SPCA) of Tompkins County have been removed from this table, as the SPCA is not a source of caregiver-surrendered kittens, but it was identified in the analysis due to anonymously surrendered animals. n D 280. c n D 716. be beneficial in this area. Or is a cluster due to cats who have caregivers but remain intact? In this case, education about the health benefits of spay and neuter, as well as information on accessible subsidized spay and neuter programs may be used. During further investigation, it will also be important to examine the temporal aspects of the clusters. For example, was each cluster the result of repeated litters over each year in

13 IDENTIFYING AND MAPPING KITTEN CLUSTERS USING GIS 305 the interval or the result of several cats giving birth in 1 year? This information is critical to understanding the cause(s) of clustering and to developing appropriate outreach programs. Not only is targeted outreach likely to be more efficient and cost-effective than nontargeted approaches (White et al., 2010), but also funding agencies are showing an increasing preference for targeted outreach efforts (PetSmart Charities, n.d.). The results of this analysis were shared with the SPCA of Tompkins County and were used successfully to attain grants for targeted outreach programs tailored to high-density/cluster areas. Although the results of this study demonstrate the tremendous potential for using GIS in shelter medicine, using a GIS approach optimally requires quality data. Mapped kitten litters were limited to only those identified as being caregiver-surrendered in this shelter due to the lack of complete address information on the majority of stray animals. Additionally, although 92% of the addresses were located and mapped, the remaining 8% of caregiver-surrendered kittens were lacking complete address data and could not be included in the analysis. These challenges illustrate the importance of completing all data fields accurately upon animal intake. Only with complete data will future analyses be maximally effective. CONCLUSION GIS is a relatively new tool to shelter medicine but one that has the power to transform the way in which shelters use, view, and analyze data. Although the software requires special training and expertise, many municipalities already utilize GIS in departments such as planning and development; animal shelters may be able to tap into existing municipal resources. For shelters that do not have a municipal relationship, nearby colleges or universities often have GIS programs, and there may be opportunities for partnership between such entities. Online resources are becoming increasingly available for shelters that wish to incorporate GIS as a component of data management and analysis. The American Society for the Prevention of Cruelty to Animals (ASPCA) currently uses GIS to help partner communities target their preventive programs. Additionally, on their website, the ASPCA has several PDFs describing how to start using GIS that are very helpful (ASPCA, n.d.). Targeting the efforts of spay/neuter programs to the demonstrated needs of a specific location or community has the potential to increase the impact of these programs on reducing cat intake numbers at community shelters. As outlined earlier, studies to date have been unable to demonstrate a major impact of subsidized spay/neuter programs on shelter intake for cats. As shelters and spay/neuter clinics use new technologies such as GIS to better target their efforts, it will be critical to collect complete data and conduct further studies of the effectiveness of these approaches. ACKNOWLEDGMENTS We thank the Society for the Prevention of Cruelty to Animals of Tompkins County for permission to use its data and its continued partnership with our program. We also thank the reviewers and editor for helping to improve this manuscript.

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