Spatial Data Mining:
An Emerging Tool for Policy Makers
by Sanjay Chawla, Shashi Shekhar, Wei Li Wu, and Xinhong Tan




 ...
Over the years, information data-            optimal habitat environments for        the classic approach, the value of ε ...
Figure 2. Map with Corresponding Contiguity Matrix                                                value of a spatial regre...
Figure 4. Learning Data Set from the Darr Wetland, 1995
     0                                                            ...
and Mitsch study. The 1995 Darr                                                    habitat model based on a spatial regres...
This study was supported by an inter-     groups, agencies, or organizations in         Army, Army Research Laboratory Coo...
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Spatial Data Mining: An Emerging Tool for Policy Makers

  1. 1. Spatial Data Mining: An Emerging Tool for Policy Makers by Sanjay Chawla, Shashi Shekhar, Wei Li Wu, and Xinhong Tan J ust as the widespread use of are used to test the validity of a hypoth- more related than distant things.” In relational databases triggered esis, while in data mining, patterns and spatial statistics, an area within statistics interest in classic data mining hypotheses are “discovered” by exploring devoted to the analysis of spatial data, (CDM) techniques, widespread the data. Thus, data mining encompasses this tendency is called spatial autocorre- use of spatial databases has a set of techniques to automatically lation. Ignoring spatial autocorrelation increased interest in “mining” useful generate hypotheses, followed by their when analyzing data with spatial but implicit spatial patterns among data. validation and verification via standard characteristics may produce hypotheses Data mining products are being success- statistical tools. or models that are inaccurate or incon- fully used as decision-making and plan- Identifying efficient tools for sistent with the data set. Thus, CDM ning tools in both the public and private extracting information from geo-spatial algorithms often perform poorly when sectors. Knowledge extraction from geo- data is important to organizations that applied to spatial data sets. spatial data was highlighted as a key own, generate, and manage large geo- Any data set that has a spatial, loca- area of research at a recently concluded spatial data sets. In this article, we will tional, or geographic component can be National Science Foundation workshop discuss the differences between CDM considered a spatial database. Examples on geographic information systems, and and spatial data mining (SDM) tech- of common spatial databases include a January 20, 2000, article in the New niques, develop a model for incorpo- maps, repositories of remote-sensing York Times on spatial data mining (SDM) rating spatial properties into both classic images, and the decennial census. demonstrates that interest in this tech- statistical analyses and a data mining Spatial data mining involves the search nology has spread to the public domain. framework, apply this model to an for patterns embedded in large spatial One of the corollaries of the infor- example from ecology involving wildlife databases. Although contemporary SDM mation age is that society has become habitat, and discuss the implications of involves the use of computers, the inundated with large quantities of data. the SDM model for policy makers. following well-known examples of what The sheer size of these data sets often we now call SDM occurred long before makes it difficult to search for mean- Classic Data Mining vs. Spatial Data the invention of computers: ingful patterns or relationships among Mining In 1855, when Asiatic cholera was data. Data mining is a technique that The difference between CDM and SDM sweeping through London, an allows researchers to overcome this is similar to the difference between epidemiologist marked on a map obstacle and discover potentially inter- classic and spatial statistics. One of the those locations where the disease had esting and useful patterns of information fundamental assumptions of classic struck. The epidemiologist discovered embedded in large databases. A pattern statistical analysis is that data samples that the locations formed a cluster, can be a summary statistic such as the are independently generated, much like at the center of which was a water average or mean, or a statistical relation- successive tosses of a coin or rolls of a pump. When government authorities ship such as a correlation between two die, where each toss or roll has no rela- turned off the water pump, the events. A well-publicized pattern that has tionship to the previous one. When it cholera epidemic began to subside. now become part of data mining lore comes to the analysis of spatial data, however, the assumption of indepen- In 1909, a group of dentists discovered was discovered in the transaction data- dence is generally false because spatial that the residents of Colorado Springs base of a national retailer: People who data tend to be highly self-correlated. had unusually healthy teeth. They buy diapers in the afternoon also tend to For example, people with similar charac- attributed this occurrence to the high buy beer. This was an unexpected finding teristics, occupations, and backgrounds level of natural fluoride in the local that the company put to profitable use tend to cluster together in the same drinking water. Now all municipalities by rearranging store merchandise. neighborhoods. The economies of a in the United States ensure that their The promise of data mining is the region tend to be similar. Changes in drinking water supply is fortified with ability to rapidly and automatically natural resources, wildlife distribution, fluoride. search for local and potentially high- utility patterns using computer algorithms. and temperature usually vary gradually In 1919, an investigator discovered Data mining draws on techniques from over an area. (using maps) that all the continents machine-learning, database manage- The tendency of like things to could be fitted together like a giant ment, and statistics to rapidly search for cluster in a space is so fundamental that jigsaw puzzle. Based on this discovery, patterns in the data. Although many geographers have elevated this phenom- the theory of Gondwanaland—which data mining techniques were inspired enon to the status of the first law of states that all the continents once by classic statistical techniques, there is geography: “Everything is related to formed a single landmass—was one major difference: In statistics, data everything else, but nearby things are proposed. 10 CURA REPORTER
  2. 2. Over the years, information data- optimal habitat environments for the classic approach, the value of ε is bases have grown so large that it has birds on the endangered species list. assumed to be generated from identical become both useful and necessary to For example, by using SDM to identify and independent distributions (for automate the search for potentially factors that influence a bird’s choice example, a standard bell-curve distribu- meaningful patterns. For example, an of nesting location, conservation tion). This assumption is based on the interesting problem in crime analysis is managers can ensure that these presupposition that an error associated the detection, explanation, and predic- factors are preserved. with one data-sample observation is not tion of “hot spots”—locations in a dependent on or related to errors associ- community or city that experience Developing a Spatial Regression Model ated with other data-sample observations. outbreaks of increased criminal activity. In the previous section, we gave a In the second step, the investigator The classic statistical approach to detec- general overview of SDM and examples uses the linear regression equation to tion of such spots is for an expert to use of some potential applications of SDM calculate the parameter β based on the a GIS to correlate different map-layers of techniques. In this section, we attempt available data for X and Y. Solving for β attribute data available for that city. The to develop a new model for SDM by in this equation is similar to calculating promise of data mining is that it allows incorporating the concept of spatial the slope and intercept of a straight line the analysis to be recast as a search autocorrelation—the idea that “Every- that represents, in graphic terms, the problem in a database. By using high- thing is related to everything else, but relationship between the dependent and speed computers and smart algorithms, nearby things are more related than independent variable. Theoretically, the it is possible to search the database for distant things.” The value of such a resulting value for β could then be used clusters of data that may characterize model can be illustrated by considering as a predictive instrument. In the case of potential hot spots. Thus, the domain the case of standard regression analysis. crime incidence in the Twin Cities, for expert who earlier searched for hot spots Regression analysis is a standard example, β could be used to predict the with the aid of a GIS is now involved in technique in statistics that is used to incidence of crime for a given neighbor- setting up the correct problem, and then quantify the relationship between a hood based on average house values for interpreting the output from a data dependent variable Y and an indepen- the neighborhood. mining algorithm to determine which dent variable (or variables) X. For Although useful, the classic statistical of the hot spots are worthy of further example, Y might be the number of approach ignores spatial characteristics analysis using standard statistical tech- crime incidents in different Twin Cities of the data used to generate the model niques. Data mining is a tool for gener- neighborhoods, and X might be the —specifically, the spatial relationships ating candidate hypotheses from data average house value in each of these that might exist among the different on which no a priori information is neighborhoods. The goal of an investi- locations where the data were collected. available. gator concerned with identifying crime This explains why, when classic linear Spatial data mining holds the hot-spots in the Twin Cities might be to regression is used to model phenomenon promise of discovering patterns within build a model that can predict the where the data samples have a spatial existing spatial databases with minimal number of crime incidents likely to occur component, the value of the error term human intervention. As such, SDM can in a given neighborhood based on the ε is not always distributed identically be a powerful aid in policy decision average house value in that neighborhood. and independently across the data set. making. The following areas in which The classic statistical approach to Instead, the value of ε often varies SDM is already playing an important building such a model consists of two systematically over space. This is because role were showcased in a January 20, steps. First, the investigator describes the independent and dependent variables 2000, New York Times article: the relationship between Y and X using themselves are spatially related in ways Monitoring lending patterns of insti- a linear regression equation such as the that differentially affect the values of tutions. Consumer advocacy groups one shown in Figure 1 (a). In this equa- these variables, and in turn the value of ε. are using SDM to map the lending tion, ε represents the residual error. In In short, because the classic approach practices of banks and other lending ignores the first law of geography and institutions. By relating the location assumes that there is no spatial autocor- Figure 1. Classic and Spatial Regression relation present among the data, it cannot of banks with the demographics of Models adequately account for such relationships. surrounding neighborhoods, SDM techniques can provide more accurate a The spatial regression approach information about whether poor attempts to overcome this problem by neighborhoods are being denied fair introducing into the regression equation access to credit. an additional “correction” term. An investigator using such an approach to Crime mapping and hot-spot analysis. (a) The classic regression model to construct a model would describe the Techniques from SDM can be used to determine the relationship between relationship between X and Y using a detect local patterns in crime data- variable Y and variable X. spatial regression equation such as the bases and examine related databases b one shown in Figure 1 (b). In this equa- to search for an explanation. For tion, the correction term ρWy attempts example, a sudden spurt in crime in a to capture the spatial characteristics given neighborhood may be the result of the data. The spatial information of an ex-convict moving into the (b) The classic regression model, is encoded using what is called a conti- neighborhood. modified to account for spatial autocor- guity matrix, abbreviated W. For Protecting the environment. Spatial relation in the dependent variable by example, Figure 2 (a) shows a map of data mining is being used to design inclusion of a “correcting” term. the seven counties that constitute the SEPTEMBER 2000 11
  3. 3. Figure 2. Map with Corresponding Contiguity Matrix value of a spatial regression analysis a b framework using the example of A C D H R S W ecological habitat prediction. A A 0 0 0 1 1 0 1 Applying a Spatial Regression Model C 0 0 0 1 0 1 0 H R to Ecological Habitat Prediction W D 0 0 0 1 1 1 1 The availability of accurate spatial C H 1 1 1 0 1 1 0 habitat models is an important tool for wildlife management, as well as protec- S D R 1 0 1 1 0 0 1 tion of critical habitat and endangered S 0 1 1 1 0 0 0 species. Because the underlying process governing the interaction between A = Anoka County R = Ramsey County W 1 0 1 0 1 0 0 wildlife and environmental factors is C = Carver County S = Scott County D = Dakota County W = Washington County (b) The contiguity matrix complex, statistical techniques are often H = Hennepin County (abbreviated W) for the map used to gain insight into the process shown in (a). A non-zero entry on the basis of data collected during (a) A map of the seven counties in the Twin fieldwork. Writing in the 1997 issue of in the matrix indicates that the Cities metropolitan region. Ecological Modelling, Uygar Ozesmi and corresponding spatial entities on the map (in this case, counties) William Mitsch developed a spatial are neighbors. habitat model for predicting the nesting locations of the wetland-breeding red- Twin Cities metropolitan area. The Figure 3. Here the goal is to predict the winged blackbird (Agelaius phoeniceus L.) contiguity matrix (W) for this map is actual location of bird nests marked by in the Great Lakes region. We will use shown in Figure 2 (b). Counties whose an A in Figure 3 (a). Figures 3 (b) and their model, and the accompanying borders touch each other are indicated 3 (c) show the results of two models data they used to generate it, to by a 1 in the matrix, and counties whose used to predict the location of nests, illustrate the benefits of using spatial borders do not touch are indicated by a indicated by a P. Although the predic- regression analysis techniques. 0. The contiguity matrix essentially tions of the model used in Figure 3 (c) The goal of the Ozesmi and Mitsch quantifies the first law of geography — are clearly closer to the actual location study was to create a model able to that nearby things are more related than of the nests than are the predictions of predict nest locations (dependent distant things — because counties that the model used in Figure 3 (b), the variable) based on several explanatory touch each other are more “nearby” than classic approach would fail to distin- (independent) variables, including those that do not touch. The investigator guish between the predictions of these distance to open water, water depth, would use the available data to solve for two models. and dominant vegetation durability. ρ and β, where ρ signifies the degree to Having argued that there are Data were collected in 1995 and 1996 which the values in the dependent significant drawbacks to using a classic from two wetland sites — Darr and variable are spatially autocorrelated. approach to spatial data analysis, Stubble — located on the shores of Lake Another shortcoming of the classic and that the introduction of a spatial Erie in Ohio. The data collected at the approach involves the way that the regression equation can overcome these Darr site were used to build a classic outcomes of various models are inter- problems and lead to more accurate regression model. This model was then preted. Consider the example shown in predictions, we now demonstrate the tested using the data collected at the Stubble site in order to evaluate the Figure 3. Classic and Spatial Model Interpretation model’s predictive power. To create the model, a uniform grid A = actual nest in pixel P = predicted nest in pixel was imposed on the Darr wetland, and in each cell the values of several P structural and environmental factors A P P A P A (independent variables) were recorded. P P These included water depth, dominant vegetation durability, and distance to A A A A A A open water. For each cell, it was also noted whether or not a red-winged (a) The actual locations (b) The locations of nests (c) The locations of nests blackbird nest was present (dependent of nests in a grid, with predicted by Model 1, predicted by Model 2, variable). The geometry of the Darr locations marked by an A. with predicted locations with predicted locations wetland, the locations of the nests, and marked by a P. marked by a P. Although the spatial distribution of the indepen- the predictions produced dent variables are shown in Figure 4. by Model 2 are spatially When the data are mapped, it is more accurate than those apparent that both the dependent and produced by Model 1, independent variables show a moderate classic measures of classi- to high degree of spatial autocorrelation. fication accuracy are For example, Figure 5 shows hypothet- unable to recognize this ical random spatial distributions for an distinction. independent variable and for bird nests 12 CURA REPORTER
  4. 4. Figure 4. Learning Data Set from the Darr Wetland, 1995 0 0 20 20 40 40 60 60 80 80 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 (a) The geometry of the wetland, with the locations (b) The spatial distribution of vegetation dura- of red-winged blackbird nests marked by an X. bility in the wetland, with darker areas indicating increased durability. 0 0 20 20 40 40 60 60 80 80 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 c (c) The spatial distribution of water depth in the (d) The spatial distribution of distance to open wetland, with darker areas indicating proximity to water in the wetland, with darker areas indicating water of greater depth. increased distance. in the Darr wetland, as might be expected pendent and identical) distribution such the random distribution in Figures 5 (a) to occur if there were no spatial autocor- as the ones pictured here would be and 5 (b) are quite different from the relation present among the data. If there expected. However, as one can see from actual distribution of nests in the Darr were no spatial autocorrelation present, Figures 4 (b), 4 (c), and 4 (d), the distrib- wetland shown in Figure 4 (a). then the values recorded for variables at ution of independent variables reveals a The goal of our experiment was to one location would have no influence gradual variation in values across space, evaluate the effects of including the on the values recorded in the vicinity of indicating moderate to high spatial spatial autocorrelation term ρWy in the that location, and thus a random (inde- correlation among the data. As a result, regression model used in the Ozesmi Figure 5. Hypothetical Spatial Distributions of an Independent Variable and Bird Nests in the Darr Wetland 0 0 20 20 40 40 60 60 80 80 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 (a) An independent and identical distribution for (b) A random distribution of bird nests, consistent an independent variable, consistent with the with the random distribution assumptions under- random distribution assumptions underlying lying classic regression analysis. classic regression analysis. SEPTEMBER 2000 13
  5. 5. and Mitsch study. The 1995 Darr habitat model based on a spatial regres- future highway expansion or light-rail wetland data were used as a learning set sion analysis more accurately describes transit construction based on the vast to construct both a classic and a spatial the relationships between various amounts of data collected by traffic regression model. The accuracy and wetland features and the presence of sensors embedded in local highways predictive power of the model was then wildlife nesting sites than does a classic and on-ramps. The potential range of tested using the 1995 Stubble wetland approach to this problem. Such a model applications is nearly limitless. data as a test set. Finally, the predictive could be used by the Minnesota Depart- In addition, because it can be used ability of the classic and spatial regres- ment of Natural Resources and other with very large data sets, SDM also has sion models was compared on the basis environmental agencies to improve the advantage of allowing policy makers of receiver operating characteristic conservation efforts for species such as and researchers to generate hypotheses (ROC) curves. ROC curves were first the red-winged blackbird. An SDM across data sets. For example, SDM used in World War II to distinguish approach could also serve as the basis could be used to combine data from between the radar signatures of friendly for similar models in other areas of remote sensing, cartographic maps, and enemy ships. We used them to conservation ecology, such as fisheries traffic sensors, and the census in order compare how accurately the two models management or wildlife population to generate hypotheses that take into predicted the location and non-location control efforts. account each of these sources of data. of red-winged blackbird nests. ROC Spatial data mining could be applied Spatial data mining is a relatively curves plot the relationship between the to other environmental questions as new term for an approach to data true positive rate (TPR) and the false well. For example, one of the greatest analysis that can be useful in many positive rate (FPR) for a predictive challenges currently facing the Metro- arenas, including econometrics, model. The TPR represents the propor- politan Council is the issue of how to environmental management, regional tion of correctly identified nest locations, balance the growth in the Twin Cities science, geographic analysis, epidemi- and the FPR represents the proportion metropolitan area with the goal of ology, and remote sensing. The potential of correctly identified no-nest locations. protecting the remaining natural and of SDM lies in its ability to rapidly The results of these comparisons are agricultural areas in the region. Spatial generate interesting and potentially shown in Figure 6. For either the classic data mining tools can play a crucial role useful hypotheses that researchers can or spatial model, the higher the curve is because they can quickly generate “what then verify, modify, and refine using above the straight line TPR = FPR, the if” scenarios regarding the effects of standard statistical techniques. This better the model is at generating accu- growth and development based on the technique is not a substitute for statis- rate predictions. The spatial regression available data. tical analysis, but rather a tool that model clearly demonstrates substantial Applications of SDM are not limited researchers can use to analyze large and systematic improvement over the to environmental issues, of course. For data sets that have a strong spatial component. Like spatial statistics, which Figure 6. Receiver Operating Characteristic (ROC) Curves for Classic and Spatial has attained a distinct identity within Regression Models the field of statistics, we believe SDM can carve out its own niche within the 1.0 1.0 framework of CDM. True Positive Rate (TPR) True Positive Rate (TPR) .8 .8 s Sanjay Chawla was a postdoctoral .6 .6 associate in the Department of Computer R R Science at the University of Minnesota at FP FP = = .4 PR .4 PR the time this research was conducted. He T T is now with Vignette Corporation. His research interests include spatial database .2 Spatial Regression .2 Spatial Regression management and data mining. Shashi Classic Regression Classic Regression Shekhar is an associate professor in the 0 0 0 .2 .4 .6 .8 1.0 0 .2 .4 .6 .8 1.0 Department of Computer Science at the a False Positive Rate (FPR) b False Positive Rate (FPR) University of Minnesota, and an active member of the Army High Performance (a) Comparison of the models’ (b) Comparison of the models’ Computing Research Center as well as the performance using the 1995 Darr performance using the 1995 Center for Transportation Studies, both at wetland (learning) data set. Stubble wetland (test) data set. the University of Minnesota. His research interests include databases, geographic information systems, and intelligent trans- classic model. This is true for both the example, SDM techniques could be used portation systems. Wei Li Wu is a doctoral 1995 Darr (learning) data set and the by Twin Cities consumer groups to student in computer science at the Univer- 1995 Stubble (test) data set. determine whether people of color in sity of Minnesota. She is working on her the metropolitan region have fair access dissertation in spatial data mining. Applications of Spatial Data Mining for to credit at local lending institutions. Xinhong Tan was a masters student in Policy Makers Local law enforcement agencies could computer science at the University of We believe SDM can be a useful tool for use SDM to predict crime hot spots Minnesota at the time this research was researchers and policy makers, both in based on existing crime data for the conducted. She is now a software engi- Minnesota and throughout the nation. Twin Cities. The Minnesota Department neer at Parametric Technology Corpora- As we have demonstrated, a wildlife of Transportation could use SDM to plan tion in the Twin Cities. 14 CURA REPORTER
  6. 6. This study was supported by an inter- groups, agencies, or organizations in Army, Army Research Laboratory Coopera- active research grant from CURA and the Minnesota. These grants are available to tive Agreement No. DAAH04-95-2-0003 Office of the Vice President for Research, regular faculty members at the University and Contract No. DAAH04-95-C-0008. The University of Minnesota. Interactive of Minnesota and are awarded annually contents of this study do not necessarily research grants have been created to on a competitive basis. reflect the position or the policy of the encourage University faculty to carry out The study was also sponsored in part government, and no official endorsement research projects that involve significant by the United States Army High Perfor- should be inferred. issues of public policy for the state and mance Computing Research Center under that include interaction with community the auspices of the Department of the Project Awards s Legal Advocacy for Battered Women. s Real Estate Foreclosures. This project T o keep our readers up-to-date about CURA projects, each Based in St. Paul, the Battered Women’s will focus on real estate home loan fore- issue of the CURA Reporter Legal Advocacy Project works to elimi- closures by subprime lenders throughout features a few capsule descrip- nate oppression of and violence against the Twin Cities metro area during the tions of new projects underway. The women. Project staff will work with a 1990s. A graduate student will work projects highlighted in this issue are graduate student to assess the status of with Minnesota ACORN (Association of made possible through CURA’s battered female criminal defendants in Community Organizations for Reform Communiversity Personnel Grants. The Hennepin County and southwestern Now)—an advocate for low- and grants are awarded twice each year to Minnesota, and to identify how many moderate-income families in the Twin grassroots organizations in the commu- were in need of legal representation in Cities—to determine whether there is a nity. Each grant supports the extra the year prior to the project start date. correlation between foreclosures and personnel needed by local organiza- The research project will document the non-conventional mortgages, and tions, usually by providing an advanced nature of the representation provided, whether the hardest hit areas are neigh- graduate student who works directly the defense strategies employed at trial, borhood minority communities with a with the organization receiving the the length of sentences, and the stable rate of home ownership. award. The grants are competitive, and adequacy of representation. organizations working with people of s Racial and Low-Income Housing color are favored. The projects described s Services for American Indian Impact Statement. The Metropolitan here are only a sampling of projects Children at Risk. The Indian Child Interfaith Council on Affordable that will receive CURA support during Welfare Law Center is a nonprofit Housing (MICAH) mobilizes congrega- the coming year. agency that provides culturally appro- tions and people of all faiths to advocate priate legal services to American Indian for public policies that increase the s Empowering Latino Youth. La families in the child protection system. supply of affordable housing in the Escuelita is a youth-serving organiza- The center serves as a community Twin Cities metro area and promote fair tion that focuses on the development of development resource for education, housing for all residents. A graduate Latino youth, specifically in the areas of advocacy, and public policy, and helps student will research and review academic enrichment, service-learning, connect American Indian children at existing literature on affordable housing, recreation, and cultural empowerment. risk with an existing network of service identify particular housing policies and A graduate student will research litera- people and programs. The organization practices in the Twin Cities area, and ture on youth development and Latino will have a graduate student update and work with MICAH staff to prepare a youth-serving organizations, and write reorganize its information database in racial and low-income impact statement a report that includes a framework for order to assist the center in setting goals regarding metro area housing policies. evaluating such organizations. for the future. s Business Plan for Social Service s Resources for American Indian s Preventing Child Abuse and Organization. Damiano is a community- Women. The Minnesota Indian Women’s Neglect in the Latino Community. based organization that provides social Resource Center in the Phillips neigh- Unidas Para Los Ninos (United for Chil- service programs to low-income families borhood of Minneapolis provides services dren) is a coalition of individuals and in the central hillside neighborhood of to American Indian women and their organizations formed to prevent child downtown Duluth. Using a local archi- families in the Twin Cities metro region abuse and neglect in the growing metro tectural firm’s feasibility study as a and on several area reservations. A grad- area Latino community. A graduate foundation, the organization will work uate student will assist the center’s staff student will research culturally appro- with a graduate student to develop a in gathering resource material and priate resources on child abuse and business plan for development of rental developing new curricula in several neglect for use by social service space in the Damiano Center, where the areas, including culturally-based chem- programs and health practitioners organization is housed. ical dependency treatment, parenting, working with Spanish-speaking families domestic violence, and sexual assault. in the area. SEPTEMBER 2000 15

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