revisiting the spatial analysis of crime in national forests

Revisiting the Spatial Analysis ofCrime in National Forests

Michael G. Wing and Joanne F. Tynon

We examined spatial patterns of crime incidents in national forests covering 112,396 km2 in the northwesternUnited States. In this study we analyzed a database containing 40,003 spatially referenced crime incidentsrepresenting felonies, infractions, and misdemeanors during 2 calendar years (2003–2004) at severalgeographic scales. We applied several geospatial analytical techniques including quadrat analysis, nearestneighbor analysis (NNA), and nearest neighbor hierarchical (NNH) clustering to investigate crime incidentspatial patterning. These geospatial tools were beneficial in identifying crime incident relationships containedwithin a large, complex spatial database. NNH clustering identified 15 regional clusters with 16,138 crimeincidents, focused in the central portion of Oregon’s national forests, specifically in the Deschutes, Mount Hood,and Willamette National Forests. Subsequent NNA tests confirmed spatial patterning in all three forests. Closerexamination of a confirmed hot spot in one forest revealed a recreation corridor with adjacent recreationdestination amenities and a large proximate metropolitan area, a combination of circumstances not apparentat the initial regional analysis scale. Other spatial data layers, such as transportation, urban boundaries, andwater bodies, augmented our ability to understand, interpret, and validate geospatial analysis results. Spatialstatistical analysis of crime incidents provides managers with a better understanding of the relationshipsbetween crime patterns, natural resources, and human built environments/infrastructure. Spatial statisticalanalysis can contribute to natural resource law enforcement as the basis for a decision support system. Decisionsupport efforts include identifying places where crime is prevalent and determining where crime occurs withgreatest frequency.

Keywords: crime, geospatial analysis, US Forest Service

C rime research in US national forestsand parks is a recent phenomenon.Crime occurrence in forests and

parks, however, is not new. Chavez andTynon (2000) investigated crime incidentsin the US Forest Service and found a widerange of crimes. Examples of crimes in-cluded assaults; drug trafficking and produc-tion; and violence perpetrated by individu-als, gangs, and extremist groups (Chavez etal. 2004, Tynon and Chavez 2006). Mount-

ing evidence indicates that many types ofcrime occurring in metropolitan areas alsooccur in national forests and other publiclands in the United States (Pendleton 1996,Vanderpool 2002, Wynveen 2005). Law en-forcement officers who investigate crime onpublic lands such as the national forests of-ten are challenged in identifying and evalu-ating crime incident relationships across vastlandscapes (Tynon and Chavez 2006, Wingand Tynon 2006). Spatial analytical tools

that examine the spatial patterning of crimeincidents provide insight into how best toallocate crime mitigation efforts.

The US Forest Service developed theLaw Enforcement and Investigations At-tainment Reporting System (LEIMARS) todigitally encode and store crime incidentsfor all the national forests and grasslands thatit manages (Tynon and Chavez 2006). Inbuilding on an earlier analysis (Wing andTynon 2006), we investigated national for-est crime incident magnitudes at increasingspatial resolutions. Before Wing and Tynon(2006), there were no other published stud-ies that used LEIMARS for the spatial statis-tical analysis of crime in national forests andthere were few examples of landscape analy-sis of crime patterns in rural areas.

Our primary objective was to spatiallyexamine crime incidents and crime patternsin US Forest Service Region 6 national for-ests. More specifically, we wanted to deter-mine where crime was most prevalent in Re-gion 6, what types of crime were occurring,and whether associated landscape featuresoffered explanations for crime patterns. Weused several geospatial analytical tools to ex-amine crime incident patterning at severalspatial scales including regional and individ-ual forest levels.

MethodsSeveral spatial analytical techniques are

available to assist in the interpretation of

Received October 10, 2007; accepted January 17, 2008.

Michael G. Wing ([email protected]) is assistant professor, Department of Forest Engineering, Peavy Hall 204, and Joanne F. Tynon ([email protected]) is assistant professor, Department of Forest Resources, Peavy Hall 107, Oregon State University, Corvallis, OR 97331.

Copyright © 2008 by the Society of American Foresters.

Journal of Forestry • March 2008 91

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geospatial technologies

crime incident locations. One such tech-nique is the application of spatial statistics toanalyze geographic phenomena. Spatial sta-tistics can address whether geographic pat-terns exist. Spatial patterns can be describedas occurring randomly, systematically, or ina clustered configuration. A random patternindicates that there is a low probability ofgeographic influence on spatial distribution.A systematic pattern is one that occurs atregular intervals while a clustered pattern is acollection of features that are grouped. Sys-tematic or clustered patterns that are statis-tically significant warrant closer inspectionand may specify where to focus crime pre-vention or mitigation efforts.

Building on our earlier efforts (Wingand Tynon 2006), we used several addi-tional geospatial analytical techniques in-cluding quadrat analysis, nearest neighboranalysis (NNA), and nearest neighbor hier-archical (NNH) clustering. We also usedKolmogorov–Smirnov (K–S) and variance–mean ratio tests to examine the statistical sig-nificance of spatial patterns in the crime in-cident database. We found these tools to bebeneficial in identifying crime incident rela-tionships contained within a large, complexspatial database.

We examined LEIMARS crime inci-dent data reported by US Forest Service Re-gion 6. Region 6 consists of approximately112,396 km2 in the states of Washingtonand Oregon (Figure 1), representing 14.4%of all national forestlands in the entire USForest Service system. The Region 6 data-base contained 40,003 spatially referencedcrime incidents representing felonies, infrac-tions, and misdemeanors for calendar years(CYs) 2003 and 2004. LEIMARS containsnot only investigative information, but alsofeatures the latitude and longitude coordi-nates of crime incidents, serving as a geo-graphic information system (GIS) databasefor the entire 781,000 km2 (193 million ac)national forest system. We also acquired spa-tial data layers for roads, national forestboundaries, urban boundaries, and hydrol-ogy for Region 6. We georeferenced all spa-tial data layers to a standard map coordinatesystem to ensure reliable analysis results. Weused a variety of spatial statistical analysissoftware including ArcGIS (ESRI 2008) andCrimeStat (Levine 2007). In addition, weused geospatial analytical techniques de-scribed in Wong and Lee (2005) and Mitch-ell (2005). These authors provide detailedformulas and application examples for spa-

tially based examinations of data relation-ships and patterns. Eck et al. (2005) providea description of analytical approaches andsoftware packages for analyzing spatial crimedata. Anselin et al. (2000) describe method-ological issues and limitations in spatial sta-tistical analyses of crime data.

Quadrat Analysis. Our study area waslarge in geographic extent. One method forcomparing change across a vast landscape isto grid the area into equal-sized portions(quadrats) and compare them. We wantedto compare crime frequencies in the quad-rats. Quadrat analysis splits an area intoequally sized portions and summarizes thenumber of crimes that occur in each portion

(Eck et al. 2005). Quadrat analysis is a spa-tial statistical technique for quantifying den-sity changes, as described by the proximityof features to one another, across landscapes.The identification of an appropriate quadratsize is critical to this analytical technique.Determining quadrat size requires carefulconsideration of the entire land area and thenumber of features to be analyzed. Wongand Lee (2005) suggest a general approachto identifying an initial quadrat size usingthe equation

quadrat size � 2 A/r,

where A � total land area and r � the num-ber of features.

Figure 1. US Forest Service Region 6 national forests.

92 Journal of Forestry • March 2008

K–S and Variance–Mean Ratio Sig-nificance Tests. Statistical significance ofquadrat density distributions can be testedthrough several techniques. Two commonlyused significance tests are K–S and variance-mean ratio. The K–S test compares observedand expected frequency distributions of fea-tures within quadrats to determine whetherspatial patterns are random. A critical valueis compared with a K–S statistic to deter-mine statistical significance (Griffith andAmrhein 1991). The critical value for a sin-gle sample comparison is calculated throughthe equation

critical value � 1.36/n0.5,

where 1.36 � critical value constant used atthe 0.95 probability level, and n � numberof quadrats. For US Forest Service Region 6data, we calculated a critical value of 0.13(1.36/1160.5).

We needed to calculate the Poissonprobability before we could address the K–Ssignificance test. The Poisson distribution isused to describe spatially random patterns. Acomparative probability for the critical valuebased on the Poisson distribution is calcu-lated as follows:

P(x) � (e�� x)/x!,

where P(x) � probability of a given number(x) of features in a quadrat, e � natural an-tilogarithm, � � average number of pointsper quadrat (�), x � frequency distributionof points observed in quadrats, and x! � fac-torial of frequency distribution of points.

� � r/n,

where � � average number of points perquadrat, r � total number of points, andn � total number of quadrats. For US ForestService Region 6 data, we calculated � �344.9 (40,003/116). Once we calculated thePoisson probability, we derived the K–S sta-tistic by taking the largest absolute differ-ence between a comparison of observed andexpected Poisson frequencies for the fre-quency distribution of the number of pointsin each quadrat.

As with the K–S test, the variance–mean ratio significance test also comparesobserved and expected patterns. If the spatialdistribution is truly random the meanshould be equal to the variance and the vari-ance–mean ratio is therefore expected to benear 1.0 (O’Sullivan and Unwin 2003). Thevariance is divided by the average number ofpoints per quadrat to calculate the variance–mean ratio.

The variance is calculated as

� � ��nj� xj � ��2�/n,

where � � variance, xj � number of points,nj � number of quadrats containing xj

points, � � average number of points perquadrat, and n � number of quadrats.

NNA. The LEIMARS database con-tained over 40,000 observations and wewanted to determine whether crime loca-tions were arranged in a pattern across thelandscape. NNA describes the spatial loca-tions of features relative to one another andassumes that locations are referenced to amap coordinate system. An observed set of

distances between points is compared withdistances that would result from a randomlydistributed set of features. NNA calculatesthe distance between each point location in aspatial database and the next nearest point.The distances between each pair of neighborpoints are summed and then divided by thenumber of features to determine the averagedistance:

Davg � �di/n,

where Davg � average distance betweenneighboring points, di � measured distancebetween neighboring points, and n � num-ber of points. A completely clustered distri-

Figure 2. Quadrat analysis results for Region 6.


bution would have a mean distance of zerobecause all points would be at the same lo-cation. A systematic distribution is the in-verse of the square root of the number ofpoints divided by the study area size; a com-pletely systematic distribution has a meandistance of one. A random distribution’smean distance is halfway between the meandistances for a completely clustered and acompletely systematic distribution (Mitch-ell 2005).

The statistical significance of NNA re-sults is determined by comparing the averagedistance to the distance that would resultfrom a randomly (spatially) distributed col-lection of points. This comparison can con-

firm whether observed point distances aresystematic, random, or clustered (Krebs1999). An NNA involves calculating a near-est neighbor index (NNI). The NNI is theratio of the observed and random distances.Where NNI � 1, features are clustered; ran-dom results have an NNI � 1; and anNNI � 1 indicates systematic patterns. Az-score test determines statistical signifi-cance.

NNH Clustering. In addition to exam-ining the distance relationships betweencrime locations, we also wanted to seewhether crimes occurred in groups. Thepresence of crime clusters can potentiallyidentify hot spots or areas where crime oc-

curs with greater frequency (Wing andTynon 2006). Clustering techniques seek toaggregate point locations into groups ofpoints based on spatial proximity. Clustersare created based on criteria that are repeateduntil all locations are grouped into clustersor the criteria can no longer be successfullyevaluated. Clustering criteria can includenearest neighbor, farthest neighbor, cen-troid method, group averages, and mini-mum error (Levine 2004). The NNH clus-tering approach is the most commonlyapplied. NNH uses the distance betweenfeatures to determine initial or first-orderclusters and continues to group features intohigher-order clusters (Mitchell 2005). Ingeneral, first-order clusters are more likely toidentify crime hot spots. Input criteria in-clude the minimum number of pointsneeded to create a cluster and the maximumdistance that a cluster can span. When theaverage distance between crime locations issmall, fewer clusters result. Varying the min-imum number of points directly impacts thenumber of clusters that will be formed. Se-lecting a larger number of minimum pointswill lead to fewer clusters. Clusters will onlybe formed that contain at least the minimumnumber specified.

Clustering output can be displayed intwo formats: convex hulls and standard de-viational ellipses (Levine 2004). The convexhull produces a very literal interpretation ofthe groups by creating a bounding polygonaround each cluster. The output ellipses cre-ate an area shape that symbolizes collectionsof points that are included in clusters. Thisrepresentation is not an exact approximationof a typical SD, which would include 68% ofa sample’s data points; it simply serves as avisual display. Generally, a single standarddeviational ellipse will encompass greaterthan 50% of the points within a cluster,whereas two standard deviations will en-compass more than 99%. The standard de-viational ellipse requires the user to input thedesired number of standard deviations thatdefine the visual appearance of the ellipse. Alarger SD is typically preferred for crimesthat cover entire regions (Levine 2004) suchas US Forest Service Region 6. Nevertheless,despite the accepted criteria cited previously,users should experiment with a variety of in-put criteria values to find the best fit for theirspecific research needs.

ResultsWe used the general approach to quan-

tifying quadrat size as recommended by

Figure 3. NNH clustering results for Region 6.


Wong and Lee (2005) and calculated aquadrat size of 5.6 km2 (2 � 112,396 km2/40,003) for US Forest Service Region 6.Smaller quadrat areas may not be ideal forlarger land areas with many analysis features.In general, smaller quadrat sizes may lead toan excessive number of quadrats. In addi-tion, spatial patterning may not be observ-able with small quadrat sizes. For US ForestService Region 6, a 5.6-km2 quadrat size re-sults in more than 20,000 quadrats, a num-ber too unwieldy for useful analysis.

We experimented with several quadratsizes until we arrived at a quadrat size of2,395 km2. This quadrat area appeared tooffer a better visual segmentation of US For-est Service Region 6. The larger quadrat sizeresulted in 116 quadrats for US Forest Ser-vice Region 6 once we eliminated quadratslocated outside national forest boundaries.

We categorized and mapped the quadratsaccording to their crime incident density(Figure 2). Areas of higher crime incidentdensity in US Forest Service Region 6 in-cluded the western coast of Oregon, an areaalready recognized for elevated crime inci-dents (Wing and Tynon 2006). Other areasof increased crime density included many ofthe national forests located in the CascadeMountains, which extend from northernWashington to southern Oregon. In con-trast, national forests in both northeasternWashington and Oregon exhibited quadratswith relatively lower crime incidents.

To test for statistical significance withinthe US Forest Service Region 6 data, we cal-culated a K–S value of 0.73, which was con-siderably larger than the critical K–S value of0.13 and indicates that the point frequencieswithin the quadrats are not randomly dis-

tributed. We also tested statistical signifi-cance by using the variance–mean ratio test.A nonrandom pattern exists when the t-sta-tistic of the variance–mean ratio is greaterthan the standard critical value of 1.96. Pos-itive t-values are representative of clusteredpatterns whereas negative t-values indicatesystematic patterns (Wong and Lee 2005).For US Forest Service Region 6 data, we cal-culated a variance of 433,437.6 and a vari-ance–mean ratio of 1,257.1 (433,437.6/344.9). This high positive value indicates astatistically significant cluster patterning ofcrime incidents within our quadrats. Thesesignificant spatial patterning results encour-aged us to apply further quantitative analy-ses to confirm our initial findings.

We conducted an NNA on the distri-bution of crime locations to draw furtherinferences about spatial patterning in USForest Service Region 6. Crime incidents inUS Forest Service Region 6 were signifi-cantly clustered (NNI � 0.21; P � 0.01;Wing and Tynon [2006]). NNA, however,does not explain where spatial patterning oc-curs (Chainey and Ratcliffe 2005). UsingNNH with a probability level of 0.05, weidentified several first-order clusters at theregional scale to identify areas of spatial pat-terning. This probability level offers 95%confidence that features in a cluster are notnear each by chance alone (Mitchell 2005).First-order clusters occur after the first iter-ation of the NNH calculation. We then ex-perimented with several different hierarchi-cal clustering parameters and varied thesearch radius, the SD of the ellipse, and theminimum number of points needed to forma cluster. We compared the number and sizeof ellipses that resulted from the variousNNH clustering parameter combinations tojudge the effectiveness of this technique inidentifying hot spots and how resulting clus-ters represented the underlying crime inci-dent locations.

At the regional scale we identified a10,000-m search radius with a 500-point min-imum as the preferred set of criteria needed toform a cluster. In addition, we found that a 2.0standard deviational ellipse helps us recognizehot spots more readily than smaller standarddeviations. This set of parameters created 15clusters in US Forest Service Region 6 (Figure3). These 15 regional clusters contained a totalof 16,138 crime incidents, or an average of1,076 crime incidents per cluster. The regionalclusters were focused in the central portion ofOregon’s national forests, specifically in theDeschutes, Mount Hood, and Willamette

Figure 4. NNH clustering results for three national forests.


National Forests. Additional regional hot spotsappeared in limited portions of the SiuslawNational Forest along Oregon’s coastline andthe Mount Baker-Snoqualmie National Foresteast of the Seattle area.

We used a second set of cluster param-eters to investigate crime patterns at the na-tional forest scale. Clustering parameters in-cluded a 5,000-m search radius with a 500-point minimum necessary to create a cluster.We also found the 2.0 standard deviationalellipse setting to be superior to smaller stan-dard deviations in drawing our attention toforest scale crime incident hot spots. Theforest scale approach resulted in eight clus-ters containing 11,865 crime incidents.These 14 clusters contained an average of848 crime incidents. Of these eight clusters,four were located in the Deschutes NationalForest and two each in the Willamette andMount Hood National Forests (Figure 4).

Quadrat Analyses of Three NationalForests. We decided to further concentrateour analysis in the Deschutes, Mount Hood,and Willamette National Forests based onthe apparent crime incident clusters. Weconducted quadrat analyses, calculated K–Sstatistics and variance–mean ratios, and per-formed NNAs to investigate whether spatialpatterning of crime incidents occurs at finerscales (Table 1). These finer scales includedanalyzing each national forest separately aswell as analyzing all three forests combined.For ease in visibility in our quadrat analyses,we initially targeted 30 quads per forest. Wecalculated the total area for all three forestsand divided this sum by 90 to determine afinal quadrat size of 218 km2. This finalquadrat size required 123 quadrats to coverthe irregular area of the three national forests(Table 1). We found statistical significanceof spatial patterning in each individual na-tional forest and for all three forests com-bined. The K–S test results indicated signif-icance in spatial patterning of the crimeincidents; they are not randomly distrib-

uted. The high positive value for the vari-ance–mean ratio indicates statistically signif-icant clustering of crime incidents.

NNAs of Three National Forests. Weused NNAs to confirm spatial patterning inthe three national forests. The low NNI val-ues for three national forests as well as for thecombined area indicate clustered crime inci-dents (Table 2). The Willamette NationalForest exhibited areas with the most denselyclustered crime incidents, with an averagemean distance between crime incidents of98 m (Figure 5). Ellipse 8, located in theWillamette National Forest, had by far thegreatest crime incident density (120.4 crimeincidents/km2) of all ellipses that we identi-fied in the three national forests. Violationlevels in the LEIMARS database are orga-nized into the following categories: admin-istration, civil, felony, infraction, misde-meanor, noncriminal, and petty offense.Table 3 shows violation levels in CY 2003–2004 for US Forest Service Region 6, theWillamette National Forest, and ellipse 8 inthe Willamette National Forest. The major-ity of violations in US Forest Service Region6 were misdemeanors. Examples of misde-meanors for US Forest Service Region 6were related to alcohol, polluting waterways,timber theft, and criminal mischief. Themajority of misdemeanors in Willamette

National Forest included occupancy use, al-cohol, general forest products (e.g., cuttingor damaging trees and removing timber),sanitation, forest roads/trails, and fire. Forellipse 8, misdemeanors were primarily oc-cupancy use and alcohol violations.

We more closely examined spatial layersof transportation, urban boundaries, hydrog-raphy, and administrative units of the nationalforest area coincidental to ellipse 8. We foundthat ellipse 8 bordered Cougar Reservoir andHot Springs, a popular recreation area easilyaccessible from a major metropolitan area.There is a recreation corridor consisting of nu-merous campgrounds and boat launching ar-eas on the nearby McKenzie River and a camp-ground and two boat ramps directly on thereservoir. McKenzie Ridge Ranger District, anadministrative US Forest Service office, is alsonearby. These additional layers and proximityof key landscape features helped provide an ex-planation for the apparent high crime incidentdensities in ellipse 8. Although these spatiallayers may not reveal information necessary todetermine crime causality, they do provide anindication of landscape, infrastructure, and thehuman dimensions associated with crime den-sity. Investigation and confirmation of the un-derlying causes for crime hot spots within el-lipse 8 should involve collaboration with on-the-ground managers of this resource area.

Table 1. Quadrat analysis results of crime incidents in three national forests (CY 2003–2004).

Number of quads Mean crimes per quad � K–SK–S

critical Variance Variance–mean ratio

Deschutes 48 184 183.98 0.73a 0.20 6,219,181 704.24*Mt. Hood 41 111 111.46 0.63a 0.21 1,247,712 273.02*Willamette 45 114 114.00 0.67a 0.20 1,241,238 241.96*Three forests 123 151 150.66 0.67a 0.12 8,687,744 468.82*

a Denotes statistical significance at 95% confidence

Table 2. Nearest neighbor, ellipse, and density results for crime incidents (CY2003–2004) in three national forests.

Total crime incidents Area (km2) Crime/km2Mean distance

between incidents (m) NNI P-value

Deschutes 8,831 7,576 1.2 116 0.25 P � 0.01Ellipse 1 1,040 100 10.4Ellipse 2 1,308 90 14.5Ellipse 3 880 85 10.4Ellipse 4 703 68 10.3

Mt. Hood 4,570 4,795 1.0 99 0.19 P � 0.01Ellipse 5 759 24 31.6Ellipse 6 754 17 44.4

Willamette 5,130 7,268 0.7 98 0.17 P � 0.01Ellipse 7 597 58 10.3Ellipse 8 843 7 120.4

Three Forests 18,531 19,638 0.9 107 0.21 P � 0.01


DiscussionIn our earlier study (Wing and Tynon

2006), we relied on visual analyses of crimelocations, crime densities, and landscape lay-ers to determine whether patterns existed in

the spatial distribution of crime on US For-est Service lands. We also visually exploredthe relationship of observed patterns toother geographic features using additionalspatial databases. These spatial databases in-

cluded transportation networks, administra-tive boundaries, hydrology, elevation, anddigital orthophotographs.

In this study, we augmented our visualexamination by using statistical significancetests to determine whether observed spatialrelationships were caused by chance alone.In the current study, we also applied severalgeospatial analytical techniques includingquadrat analysis, NNA, and NNH cluster-ing. The combination of analytical tech-niques and subsequent statistical signifi-cance tests supported our examination of40,003 crime incidents in the database atseveral spatial scales. Traditional investiga-tion techniques (e.g., statistical summariesand point location mapping) are simply notas versatile or powerful. The different spatialstatistical analysis techniques and scales en-abled us to investigate crime density at theregional, forest, and subforest administrativelevels.

The multiscale approach we appliedcan be used by resource managers and lawenforcement officials to direct their crimeprevention and mitigation efforts. These ef-forts can involve large landscapes or can befocused on smaller, specific areas wherecrime incidents or patterns appear to be con-centrated. In addition, spatial statisticalanalysis can be used to identify the types anddescriptions of crime incidents associatedwith a unique geospatial unit, includingpoints, quadrats, clusters, or ellipses. Addi-tional spatial data layers, such as transporta-tion, urban boundaries, and water bodies,can augment the ability to understand andinterpret quantitative geospatial analysis re-sults. This augmented ability not only en-hances interpretation, it can also validatequantitative results. For example, we foundan ellipse in the Willamette National Forestthat had a substantially larger density ofcrime incidents. We were able to determinepotential explanations for the increasedcrime incident density of this ellipse by us-ing spatial statistical analysis techniques toinvestigate landscape and other featureswithin and surrounding the ellipse. Our in-vestigation revealed that this ellipse was in arecreation corridor with many adjacent rec-reation destination sites including campsitesand water features. There also was a largemetropolitan area in close proximity. Thiscombination of circumstances was not ap-parent initially at the regional scale but be-came obvious at the finer scale where we in-tensified our use of multiple spatial layers.

There is no established set of analysis

Figure 5. Crime incident densities for Willamette National Forest.

Table 3. LEIMARS violation levels for US Forest Service Region 6, Willamette NationalForest, and ellipse 8 (CY 2003–2004).

Violation level US Forest Service Region 6 Willamette National Forest Willamette National Forest, ellipse 8

Misdemeanor 35,462 (88.6%) 4,830 (94.2%) 834 (98.9%)Infraction 3,023 (7.6%) 184 (3.6%)Noncriminal 797 (2.0%) 54 (1.1%) 1 (0.1%)Felony 552 (1.4%) 54 (1.1%) 6 (0.7%)Petty offense 108 (0.3%)Administrative 34 (0.1%) 3 (0.1%)Civil 27 (0.1%) 5 (0.1%) 2 (0.2%)Totals 40,003 5,130 843


procedures that direct the sequence of stepsand techniques to analyze large spatial data-bases. Our spatial database was characterizedby irregular administrative boundaries cov-ering 112,396 km2. In addition, the forestboundaries were not contiguous and theamorphous boundaries did not easily lendthemselves to systematic analysis. For exam-ple, we found that general recommendationsfor quadrat area sizes were not well suited forour purposes. We chose instead to experi-ment with different quadrat extents so thatwe could visualize crime incident densitiesand patterns at several scales more appropri-ate for our analysis objectives. We also ex-perimented with standard deviational ellipseparameters and varied clustering (NNH) pa-rameters to maximize our ability to discerngroupings of crime incidents. Our experi-mentation and variation of analytical pa-rameters led us to consider additional quan-titative techniques that enabled us todiscover and identify spatial relationships.These spatial relationships would not havebeen apparent without the use of geospatialanalyses.

We discussed in a previous study (Wingand Tynon 2006) several potential concernsabout the consistency and quality of datawithin the LEIMARS database. The USForest Service has invested significant re-sources in collecting and cataloguing crimeincident data for the vast lands that it man-ages. One of our concerns was with the geo-graphic accuracy of crime incident locations.The accuracy of recording crime incident lo-cations depends heavily on the abilities andresources of law enforcement officers whorecord the geographic coordinates. This de-termination may come from existing maps,conversations with others, global position-ing systems (GPS), or other sources. Data-base quality could be improved throughadoption of a systematic and reliablemethod of recording geographic coordinatesfor crime incidents. One such methodwould be to require law enforcement officersto use GPS receivers to record the location ofevery incident. GPS receiver technology hasevolved such that reliable GPS receivers areavailable for about $100 each. Wing et al.(2005) determined that such GPS receiversare capable of spatial accuracies within 10 mor less, even while operating underneathdense forest canopies. Crime incident de-scriptive information could potentially beentered into the GPS receiver and laterdownloaded to a digital database for storageand retrieval. Digital information contain-

ing spatial and descriptive informationcould be made available to law enforcementofficers in the field, allowing them to bemore effective.

The spatial cataloguing and analysis ofcrime incidents can provide managers with abetter understanding of crime occurrencepatterns. In addition, relationships with nat-ural resources and human built environ-ments/infrastructure also may be investi-gated. These attributes have the potential toprovide important contributions to law en-forcement in the US Forest Service and oth-ers who manage natural resource areas. As adecision support system, these contributionsmay help managers best direct crime preven-tion and mitigation resources. Decision sup-port efforts include identifying places wherecrime is prevalent and determining wherecrime occurs with greatest frequency.

For example, a predictive spatial modelcould guide rural law enforcement decision-making to reduce opportunities for crimeand to promote safety on rural lands. A spa-tially based predictive model could identifyareas that have a high probability for use asmarijuana cultivation and methamphet-amine production sites. Existing spatialdatabases of eradicated drug productionsites on federal, state, and other public ru-ral lands could be used to evaluate themodel. Rigorous field-based examinationscould assess the effectiveness of site pre-dictions with measured confidence. Visitsto predicted sites could verify whetherdrug production occurs or has occurred inthese areas. GPS receivers can record thespatial extent of the drug sites, significantadjacent resources, and any other notablefeatures. Resource data collected at thepredicted drug sites could confirm andhelp evaluate the spatial accuracy of GISdatabases used in the predictive model andthe model’s success rate.

A predictive spatial model can behoused within a GIS-based decision supportsystem. An evaluative component can be toassess the decision support interface, the easeof access for law enforcement users, and theoutput quality. Acceptability by multiple ru-ral law enforcement organizations could bemeasured through a series of trials and eval-uative survey techniques. Debriefings of usetrials could provide decision support systemfeedback opportunities. This decision sup-port system could encourage rural law en-forcement organizations to conduct theirown predictive analyses to identify areas forincreased crime prevention efforts. This

would show how active use of GIS technol-ogy and working across jurisdictions to poolresources could lead to more effective re-gional law enforcement. In addition, law en-forcement agencies could apply the decisionsupport system to better understand the re-lationship between drug production and en-vironmental factors—a very serious issue thataffects all citizens and has practical implica-tions for rural law enforcement across theUnited States (Tynon and Chavez 2006).

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revisiting the spatial analysis of crime in national forests

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