WEST VANCOUVER CRIME ANALYSIS USING GWR, CRIMESTATS AND ARCGIS

Transcription

1 WEST VANCOUVER CRIME ANALYSIS USING GWR, CRIMESTATS AND ARCGIS Geography 446 Winter 2003 Bronwyn Agrios Junnie Cheung Shannon Graham Suzanne Guy Josh Howardson Vivien Leung Aaron Licker Kyle MacDonald Grant McKenzie Jennifer Woods Department of Geography University of British Columbia

2 Edited and formatted by Bronwyn Agrios and Jennifer Woods 2

3 ACKNOWLEDGEMENTS The group would like to thank the West Vancouver police department, specifically Sergeant Bob Fontaine, for allowing us access to their crime data and in expressing an overall interest in learning how Geographic Information Systems can be applied to crime analysis. We would also like to thank Dr. Brian Klinkenberg for his thoughts and insight into this project and for the procurement of a TV to watch the Canucks during the editing process. 3

4 WEST VANCOUVER CRIME ANALYSIS OUTLINE 1. Introduction 6 2. Data Analysis CrimeStats Introduction Hotspot Discussion and Analysis Kernel Density Auto Crime and Time of Day Break and Enters Conclusions Geographically Weighted Regression (GWR) Introduction Housing Tenure Analysis Visible Minorities Analysis Population Composition Age Analysis Conclusions Other Methods of Analysis Seasonality and Weather Bus Routes Conclusions Error Conclusions References 85 4

5 LIST OF FIGURES AND TABLES West Vancouver: Composite of Orthophotos 8 Locations of Reported Crimes: Orthophoto Reference 9 The Frequency of Crime in West Vancouver (Fuzzy Mode) 15 Nearest Neighbor Hierarchical Spatial Clustering (Nnh) 18 Risk-adjusted Nearest Neighbor Hierarchical Spatial Clustering (Rnnh) 21 Single Kernel Hot Spot Analysis of Crime 24 Dual Kernel Hot Spot Analysis of Crime 26 Auto Crimes 29 Break and Enter under $ Break and Enter over $ The Relationship between Rental Units and Crime 42 Average Gross Rent in Relation to Crime 44 The Impact of Visible Minorities on Crime Occurrences 47 The Relationship between Recent Immigrants and Crime 51 The Effect of Visible Minorities on Crime 52 The Effect of Lone- Parent Families on Incidents of Crime 54 The Effect of Population Diversity on Crime Incidents (1) 55 The Effect of Population Diversity on Crime Incidents (2) 56 The Impact of Youths (15-24) on Crime Occurrences 60 The Impact of Young Males (15 to 24) on Crime Occurrences 62 The Impact of Elderly Population on Crime Occurrences 64 Table 1: West Vancouver Monthly Crimes 69 The Relationship between Precipitation and Crime 72 Density of Seasonal Crimes per Square Kilometer (Winter/Spring) 74 Density of Seasonal Crimes per Square Kilometer (Summer/Fall) 75 Public Transit Routes and Stops: Orthophoto Reference 78 Table 2: Blue Bus Stations and Crimes 79 Relationship between Break & Enter under $1000 and Public Transit 80 5

6 1. INTRODUCTION Law enforcement has always focused on the collection, aggregation and analysis of both spatial and temporal data. For decades, police departments around the world have relied on mapping as a form of analysis to aid in identifying patterns of criminal activity. The introduction of Geographic Information Systems (GIS) a logical step in the advancement of law enforcement technology - not only allows for the integration and analysis of data to identify, apprehend and prosecute suspects, but also aids more proactive behaviour through the effective allocation of resources and better policy setting (Nelson 1999). Drawing upon this integrated perspective, law enforcement agencies can be more effective in crime prevention, intervention, and community-oriented policing (ESRI 1999). Geographic Information Systems has enhanced the speed and quality of crime analysis, allowing time and manpower to be redirected toward developing special enforcement efforts. Additionally, beats can be redefined and improved according to changing demographics, fluctuations in criminal activity, and other dynamic socio-economic conditions. Relating data gathered by dispatchers, incident reports, and other record management systems to a simple street map, permits a concise accurate visual display of high and low crime areas. Crime maps are responsible for the following information: - Show locations of crime by time, modus operandi, and other characteristics. - Determine crime hot spots. - Relate patterns of crime location with addresses of known offenders. - Reveal patterns or trends in criminal activity. - Show gang boundaries. - Show census data. (ESRI 1999) For this project the class utilized a number of different software packages to ensure an accurate and thorough analysis. ArcGIS, Geographically Weighted Regression, CrimeStats, and Microsoft Excel were all employed to examine property crime in the city 6

7 of West Vancouver ranging from April 2002 to March Techniques such as kernelling and buffering were used to explore spatial and temporal patterns utilizing variables taken from census information such as sex, age, population and rent. The data for this project was acquired through the West Vancouver police department s website ( The community block watch program produces a list of monthly crimes (showing the 100 block, the type of crime, the mode of entry and the items stolen) as a way of alerting the surrounding community of potential threats. While the data on the web was from June 2002 to March 2003, Sergeant Fontaine, the media relations liaison was able to get us a complete year of data. 7

8 Composite of Orthophotos District of West Vancouver Kilometers Source: Aerial Photos May/June 1995 Selkirk Remote Sensing Produced by Kyle MacDonald

9 Locations of Reported Crimes: Orthophoto Reference District of West Vancouver Kilometers Source: West Vancouver Police Department Aerial Photos May/June 1995 Selkirk Remote Sensing Produced by Kyle MacDonald

10 Several other communities in the lower mainland also post crime bulletins on their websites to alert their citizens. New Westminster s block watch program includes a monthly map of various crimes in the city ( North Vancouver also has a block watch program, posting break & enters by the 100-block with no other details, on their website ( As well, many cities in the United States have online crime mapping such as Baltimore ( San Diego ( and St. Louis ( Metropolitan%20Police) highly comprehensive and accessible Safe City program, all of which are aimed at reducing crime through education and awareness. The following paper reviews the entire project from the acquisition and preparation of data to the analysis, conclusions and results that were drawn together from the various software programs 10

11 2. DATA ANALYSIS 11

12 2.2. CRIMESTATS 12

13 INTRODUCTION CrimeStats is a spatial statistical program that is used in the analysis of crime incident locations. The purpose of such a tool is to aid law enforcement agencies in their crime mapping efforts by identifying where the clustering of criminal activity is most concentrated. The use of GIS is a more recent trend in tracking hotspots as it allows the user the ability to map the criminal activity, and then based on these outputs, observe spatial patterns in the distribution. ArcGIS is such a tool that can be used to create these outputs. However, ArcGIS does not contain a statistical method for performing high level statistical functions needed for analyzing crime data. CrimeStats offers a more quantitative approach to analyzing the distribution of crime than simply digitizing the locations and visually drawing conclusions. Once the CrimeStat analysis is completed, the results are then input into ArcGIS to display the results spatially. 13

14 HOTSPOT DISCUSSION AND ANALYSIS Fuzzy Mode It is important to accurately identify concentrations, or hotspots, of criminal activity so that police departments can focus appropriate means of enforcement on these areas. Hotspots are defined as areas that are the target of a higher than expected level of criminal activity and are spatial in nature (Ratcliffe et al, 2001). It is important that these hotspots be identified not only for more efficient policing, but also as a crime prevention tool. The use of GIS is a recent trend in tracking hotspot locations and through the software CrimeStat, we were able determine the hotspot locations for crime in the city of West Vancouver. Fuzzy mode is used to calculate the frequency of incidents for each unique location within a small specified radius. A set of X and Y coordinates identify the area of focus, while the radius is used to calculate the number of incidents that fall within the specified area. The results are a list of coordinates at unique locations with the number of incidents occurring at each location. This list is ranked based on frequency as well as the uniqueness as far as a hotspot location. For our analysis, we set the search radius to 250 meters, which means that for each incident of crime on the map, CrimeStats calculates the number of crimes that falls within this distance. We reasoned that by doing this, all of the crime locations would be identified on the map, but the locations would then be ranked based on the amount of criminal activity for the area. The map entitled The Frequency of Crime in West Vancouver (Fuzzy Mode), shows the hot spots based on colour and size, ranking the locations by number of incidents. 14

15 The Frequency of Crime in West Vancouver (Fuzzy Mode) Land Use Parks & Protected Natural Areas Lakes Residential - Single Family Res - Townhouse & Low Rise Apt Res - High Rise Apartments Commercial Institutional Transport, Comm. & Utilities Open Space and Undeveloped Protected Watershed Fuzzy Mode Kilometers Source: Census 1996 West Vancouver Police Department District of West Vancouver Created by: Suzie Guy

16 It should be noted that the pairing of colour and size is strictly for classifying purposes and they are both showing the same interpretation of the data. The larger, darker red circles denote a higher frequency of crime at these given locations. This means that at these points CrimeStats calculated crime incidents within a 250-meter radius. It is clear from the inset map, showing the hotspots in the south-east section of West Vancouver, that there is a high frequency of crime around the mall and the commercial area of Ambleside along Marine Drive. This area is also characterized by high rise apartments which we can infer also indicate elevated levels of auto crime in underground parking garages. Needless to say, the diversity in land use in this area accounts for a high daytime population and concurrently higher levels of criminal activity. There is also a substantial level of criminal activity, composed mainly of auto thefts as well as theft from autos, occurring near the Horseshoe Bay Ferry Terminal which can be attributed to the high concentration of vehicular traffic moving through and parking in that area. Our analysis certainly shows low levels of crime are scattered throughout West Vancouver, but through the identification of the exact location of hot spots, police officers have the advantage of knowing the areas with moderate to higher levels of criminal activity, and can focus enforcement manpower in these areas. Nearest Neighbour Hierarchical Analysis (Nnh) Nearest Neighbor Hierarchical Spatial Clustering is a means of identifying the hot spot clusters of points using a constant distance clustering routine, to group points by their spatial proximity to each other. In other words, Nnh is used to identify groups of crime that are spatially close. Through the CrimeStat program, parameters are entered to specify a threshold distance, the minimum number of points for each cluster, and an output size for displaying the clusters, which are shown on the map as pink and orange ellipses. Threshold distance is the confidence interval around a random expected distance for a pair of points. The pairs of points that are closer together than the threshold distance are grouped together. In the input parameters, the largest threshold distance was selected, which means that the significance level is higher, and therefore more points will be selected. Although this runs the risk of having chance groupings, this is limited by selecting a lower minimum number of points per cluster. The first-order 16

17 clusters shows the dominant patterns in the criminal activity based on this threshold distance and minimum number of points. This analysis is hierarchical in nature, as the first-order clusters (pink ellipses) are treated as separate points to be grouped into the larger second-order clusters (orange ellipses). The second-order clusters take these original clusters and create further clusters based on the same criteria outlined above. The map titled Nearest Neighbor Hierarchical Spatial Clustering (Nnh) confirms the findings from the fuzzy mode analysis, showing that the most significant clusters are found in the south-eastern neighborhoods of West Vancouver. 17

18 Nearest Neighbor Hierarchical Spatial Clustering (Nnh) District of West Vancouver Spatial Clustering Nnh clustering - 1st order Nnh Clustering - 2nd order Land Use Parks and Protected Natural Areas Lakes Residential - Single Family Residential - Townhouse and Low Rise Apartments Residential - High Rise Apartments Commercial Institutional Transportation, Communication and Utilities Open Space and Undeveloped Protected Watershed Roads Kilometers Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Suzie Guy

19 When comparing the two maps, it is easy to see how the second-order clusters were formed from the smaller fuzzy mode clusters. However, it is interesting to see that the clusters aren t directly centered over Park Royal Mall, but appear to have shifted slightly west. This could be attributed to the high proportion of commercial businesses and highdensity residential housing located along this stretch of Marine Drive, which may attract a high proportion of criminal activity, shifting the clusters away from the mall. It is interesting to note that both of the clusters are located in the same vicinity indicating that although there are first-order ellipses around the ferry terminal and parks, they are not significant enough in terms of threshold distance and minimum number of points to be further grouped into the second-order clusters. From these results we can infer that there is a high level of auto theft/ theft from autos occurring in the parking lots at Park Royal Mall, various parks (e.g. Lighthouse Park), at the ferry terminal, and in residential underground parking garages. Risk Adjusted Nearest Neighbour Hierarchical Clustering The risk-adjusted spatial clustering (Rnnh) is a variation of the Nnh technique, in that it is based on the same criteria for determining clusters and the analysis is performed in a hierarchical manner. However, Rnnh is inversely adjusted in proportion to the population density (secondary baseline variable). In other words, the technique is the same, but the results are normalized by the distribution of population across West Vancouver so that the clusters are not skewed by the census data. This is important because Statistics Canada distributes the surveys to the residential households which only accounts for the night time population. Therefore, the large daytime population centered in the southeast area of West Vancouver is not accounted for in this data. As with Nnh, through the program CrimeStat, a threshold probability is selected to determine the likelihood of pairs occurring, as well as the minimum number of points for each cluster, and an output size for displaying the clusters (shown on the map as pink and orange ellipses). The next step is to specify a kernel density model for the distribution of population densities. The threshold distance is determined by the threshold probability and the kernel density estimates based on the population. It is through this process that the clusters that are formed, taking population density into account. For example, in 19

20 areas of high population, the threshold distance will be smaller in size to account for the additional population. Once again, this analysis is performed in a hierarchical fashion so that first-order clusters (pink ellipses) are treated as separate points to be grouped into the larger second-order clusters (orange ellipses). The map titled Risk-adjusted Nearest Neighbor Hierarchical Spatial Clustering (Rnnh) shows that when the data is normalized for population, the crime clusters are more evenly distributed across the study area. 20

21 Risk-adjusted Nearest Neighbor Hierarchical Spatial Clustering (Rnnh) District of West Vancouver Spatial Clustering Rnnh clustering - 1st order Rnnh clustering - 2nd order Land Use Parks and Protected Natural Areas Lakes Residential - Single Family Residential - Townhouse and Low Rise Apartments Residential - High Rise Apartments Commercial Institutional Transportation, Communication and Utilities Open Space and Undeveloped Protected Watershed Elementary Schools Secondary Schools Roads Kilometers Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Suzie Guy

22 When we compare the maps for Nnh and Rnnh, we can see that the distribution of firstorder clusters is much more spread out across the residential areas and the ellipses are much larger in size. Furthermore, areas that encompass schools are of interest in our analysis as these areas have a larger day time population. Particular attention should be paid to the secondary schools (red) as we can infer from our data that there was a high level of theft occurring around these locations. This helps to verify previous research (Turnbull et al, 2000) that a high proportion of crimes are committed by young males. The Horseshoe Bay ferry terminal and various parks are other target areas to examine. The parking lots for these areas are probable targets for car thefts as well as thefts from auto. The clustering that is evident around the southeast area of West Vancouver is again the focus of criminal activity. The first-order clusters are grouped in this area into the larger second-order clusters and when compared to the Nnh map, there is again a shift and enhancement of these ellipses. This can be accounted for by the high proportion of daytime activity generated by the commercial district in this area. It is interesting that eight out of the thirteen schools are located within these ellipses which can also be explained by the higher day time population as well as the target age of criminals. The following analysis on kernel density will go into further depth by breaking down the types of crime as well as showing the distribution of crime based on the time of year and the impact of weather conditions on frequency of criminal incidents. 22

23 KERNEL DENSITY DISCUSSION The kernel density analysis is a technique for generalizing incidents over an entire area; in actuality it provides density estimations. Whereas the spatial distribution and hot spot statistics provide statistical summaries for the data themselves, interpolation techniques generalize those data incidents to the entire region. Kernel density estimations involve placing a symmetrical surface over each point evaluating the distance from the point to a reference location; this is evaluated and produces a grid. This process can be done using different types of bandwidths, sampling sizes, or the choice of dual or single kernels. For the purpose of this project we have chosen to use an adaptive bandwidth due to its relevance to the project. Sampling size procedures took some trial and error; we finally concluded that a sample size of 5 accurately represented the geographic patterns of the area while incorporating sufficient data points. A dual kernel is normalized by a spatially explicit variable such as population, allowing a different form of surface interpolation. ANALYSIS In analyzing the results for Single Kernel Hot Spot Analysis for West Vancouver there is a definite concentration around Park Royal Mall, while small pockets of hotspots occur throughout the rest of West Vancouver. 23

24 Single Kernel Hot Spot Analysis of Crime District of West Vancouver Crime Intensity Very Low Low Medium Highest Roads Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Josh Howardson Kilometers

25 Two other pockets of crime seem to be quite interesting; the one focused around the ferry terminal and the one around Lighthouse Park. These are two areas sparsely populated but are used by a high number of citizens. Also, vehicles are generally left unattended in these areas for extended periods of time. The kernel concentrations seen around Park Royal and Ambleside frequently occur in areas where both high population densities and major economic activities coincide. Problems like burglaries and theft from autos tend to gravitate towards areas like these because it is much easier for criminals to blend in. In analyzing the Dual Kernel Hot Spot Analysis for West Vancouver map, an interesting change in the geographic representation of the data appears. 25

26 Dual Kernel Hot Spot Analysis of Crime District of West Vancouver Crime Intensity Very Low Low Medium Highest Roads Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Josh Howardson Kilometers

27 There has been a shift, indicating that the percentage of crimes per persons in far less in the densely populated neighborhoods. The areas adjacent to the Horseshoe Bay ferry terminal and Lighthouse Park become areas exhibiting higher risk, while the area around Park Royal Mall becomes less significant. RECOMMENDATIONS As both the single and dual kernel analysis indicated, increased patrols through the ferry terminal and Lighthouse Park parking lots at peak times of usage could be a simple way of limiting the amount of criminal activity in these areas. 27

28 AUTO CRIME AND TIME OF DAY In analyzing the data for Auto Crimes in West Vancouver two crime density kernels were created and compared. In order to create the dual kernel densities, modification of the initial data set was necessary. The initial crime data set was queried in ArcMap 8.2 to display data with crime type equal to type 3 and type 4 ("auto theft" and "theft from auto" respectively). The selected crime points were then exported into a new ArcMap database file under the name of Auto Crimes. This table was once again queried to display the crimes by "Time." Crimes between 11pm and 7am were exported as a new shapefile as well as the alternative, Crimes between 7am and 11pm. These two files provided the basis for the dual kernel Crimestat analysis. These shapefiles were then imported into CrimeStats as the new primary files. Crimestats was then used to create a dual kernel density estimation for both sets of data. The parameters for the Crimestat model remained the same as the initial model (adaptive bandwidth and sample size of five). The results from this analysis were quite interesting. DISCUSSION AND RESULTS The Auto Crimes in West Vancouver map spatially displays the comparison between the two sets of data. 28

29 Auto Crimes District of West Vancouver Auto Crimes from 7am to 11pm (Dual Kernel) Auto Crimes from 11pm to 7am (Dual Kernel) Auto Crime Intensity Very Low Low Medium Highest Roads Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Grant McKenzie Kilometers

30 The results examining auto crimes between 11pm and 7am show areas of high crime density (hotspots) located around the region of the Park Royal shopping center and McKechnie Park. The lightest areas on the map include Lighthouse Park and west along the coast. Since the density being displayed is a dual kernel, the regions of high crime intensity can be explained by lack of population and a static amount of crime, or by a static population and an excessive amount of crime. The hot spots represent the greatest ratio between crime and population. The second map shows a shift in the regions of crime intensity. Between 7am and 11pm, the dual kernel density shows Lighthouse Park as a new "hot spot" as well as upper-west West Vancouver. Park Royal mall seems to remain the same, the time of day having little effect on the amount of crime. McKechnie Park is still a significant crime region, but has dropped from high intensity at night to medium intensity during the day. Overall, the layout does show that the time of day has an impact on the amount of crime and the region in which it is located. 30

31 BREAK & ENTER The Break and Enter crimes were queried from the initial "All Crimes" data set in the same way that auto crimes were separated. The crimes with "type" equal to one, and two, where grouped into two separate groups representing Break and Enter under $1000 (1) and Break and Enter over $1000 (2). Both sets of data were again imported into Crimestat as primary files and both single kernel as well as a dual kernel "hotspot" analyses were run. The resulting crime densities were displayed on the map titled Break and Enter in West Vancouver. DISCUSSION AND RESULTS The single kernel density estimations for break and enter under $1000 and break and enter over $1000 are quite similar. 31

32 Break and Enter under $1000 District of West Vancouver Single Kernel Dual Kernel Break & Enter Intensity Very Low Low Medium Highest Roads Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Grant McKenzie Kilometers

33 The intensity of break and enter over $1000 in and around Park Royal appears a little higher than break and enter under $1000. One other region that is noticeable in comparing the single kernel densities is around McKechnie Park. Break and enter over $1000 produces a hotspot in this region, while break and enter under $1000 does not. The dual kernel density estimations show a drastic change from the single kernel densities. 33

34 Break and Enter over $1000 District of West Vancouver Single Kernel Dual Kernel Break & Enter Intensity Very Low Low Medium Highest Roads Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Grant McKenzie Kilometers

35 The maps show a shift in crime intensity from eastern West Vancouver to western West Vancouver. While Park Royal mall does remain a hotspot for both types of break and enter, the obvious focus in a dual kernel analysis is on most of the western side of the city. Once again, break and enter over $1000 appears to have a generally higher intensity of crimes than break and enter under $1000. The two types of analysis, both dual as well as single kernel density estimations are useful when observing the crime regions of high intensity. The more data that is available for interpretation, the easier it is to understand the crime regions. 35

36 Conclusions The final results using CrimeStats as the primary means for analysis enabled us to draw several interesting conclusions regarding the distribution of crime incidents in West Vancouver. Though our results clearly show several logical locations where criminal incidents involving property crimes tend to be focused, they also point to a few less obvious locations and neighborhoods experiencing high levels of criminal activity. Going into this project, we initially thought that the commercial areas and parkades at Park Royal mall and the Horseshoe Bay ferry terminal were going to be the obvious hotspots for criminal activity in West Vancouver, and in some ways doing a spatial analysis of crime in the city seemed redundant. However, though our analyses certainly did not disprove the importance of these two areas, the analyses also revealed several hotspots we had not originally expected to see, most notably the high level of criminal activity around McKechnie Park. The results from this portion of the project verify the importance of using a modeling program like CrimeStats to explore the spatial variability of criminal incidents throughout the city. These results can help law enforcement agents to better understand the forces driving incidents such as burglaries, car thefts and theft from autos, enabling them to use this spatially explicit knowledge to take effective countermeasures to curb crime. 36

37 2.3. GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) 37

38 INTRODUCTION Geographically Weighted Regression is a method for exploring spatial nonstationarity, a condition where a simple global regression model is unable to adequately explain the relationship between variables over a geographical area. Martin Charlton, Stewart Fotheringham and Chris Brunsdon at the University of Newcastle upon Tyne developed the program to satisfy a need for a local, rather than global, method to interpolate spatial data. The result is that the GWR model is able to change across space to reflect the influence spatial location has, resulting in a variety of summery statistics for many locations. This differs from a global model where one single summery statistic is computed for the whole area. GWR uses a series of inputs on the variables to change the relationship between the variables so they are directly compared over space (GWR), not area (global). Global models tend to average out local variations and anomalies, resulting in a best-fit model for the whole area, as is seen with kernel density estimations in the following CrimeStats section. GWR, on the other hand, allows for the observation, measurement and mapping of these important localized variations, and lends itself well to GIS, especially when trying to make sense out of large quantities of data. The resulting mapped output is similar in many ways to what our eye sees when we use a microscope; detail and patterns that were previously hidden are suddenly clearly visible GWR calculates a spatially significant regression based on a set of user defined inputs. One of the inputs, which is strongly used in the analysis and interpretation of results of this project, is the significance test. This is a measure of whether the independent variables are spatially significant to the overall results. Significance tests are used to determine whether the local data points give a better explanation of the data relationships than the global data, while the purpose of the test is to identify significant spatial variation. The test computes the distance between pairs of points, and the outputs from significance test will determine the variables showing good spatial variation that in turn are suitable for mapping. The remaining variables from the test that are not significant have a high probability that the variation occurred by chance. To determine this, GWR gives the choice between Monte Carlo and Leung tests. Monte Carlo test is used for examining the validity of any inferences drawn from the local results. Observed values 38

39 are compared with simulated values produced from a random number generator (Wichmann-Hill pseudo - generator). The spatial variations of parameters are established by moving points around randomly 100 times and calculating the regression parameters, the parameters are then ranked. The variables that are not geographically important are represented by numbers closer to one and variables showing good spatial variability are represented by number close to or equal to zero. GWR was chosen for analysis of crime and census data for West Vancouver because low overall crime was anticipated. Based on this it was important to identify specific locations including block-by-block radii and neighbourhoods. This would only be possible with a local regression model, opposed to a global model that does not reflect the change of variable across space. 39

40 HOUSING TENURE ANALYSIS DISCUSSION It was decided to incorporate a housing tenure analysis into the examination of property crimes in West Vancouver based on a similar study completed by Elizabeth R. Groff and Nancy G. La Vinge (2001). In this study, the authors examine the usefulness of crime mapping software for strategic criminal analysis with special emphasis on burglaries. As well, GIS is used to enable the visualization of crime patterns and allow the identification of trends and hotspots. The future of crime mapping lies in the ability to identify early warning signs across time and space to allow for a proactive, preventative approach by police. In the study of crime in North Carolina, ten variables were used including housing tenure. According to the research of Groff and La Vinge as well as previous research by Greenberg et al. (1982), rented houses are more often the target of property of crime than owned houses. There is a greater percentage of renter occupied housing units with weaker ties to the neighbourhood indicated by lack of alarm system and poor upkeep of yard and home. These features are easily picked out by criminals and theoretically make rented homes the target of property crime. The data used to analyze the affects of crime on rented units and neighbourhoods was the West Vancouver population from 1996 census data and other significant information regarding the rented units in each enumeration area. The variables used were: Table 1: Variables for analysis Variables Description of Data AvgValue$ Average value of rented units in enumeration area RentedUnits AvgRent$ %Rented Number of rented units in each enumeration area Average gross rent of units in enumeration area RentedUnits taken as a percent of total population These variables were selected because they represent a quantitative value of presence of rented units in the area as well as a monetary value indicating better and lesser off neighbourhoods. Number of rented units per enumeration area can also be used as an 40

41 indicator of neighbourhood status because it may point to lower income. This is not that simple in West Van because the overall neighbourhood status is quite well off and rented houses may represent areas lived in by people with a summer and winter home or frequent travelers. Monetary value of hosing has to be considered to differentiate between the two spectrums. The number of rented units in enumeration areas is seen as a percent of total West Vancouver population. As seen in the map titled The Affect of Crime on Areas of Rented Units, there is a negative to positive, east to west, gradient of the affects of crime incidences on rented units. 41

42 The Relationship between Rental Units and Crime District of West Vancouver, 1996 Land Use Parks and Protected Natural Areas Lakes Residential - Single Family Residential - Townhouse and Low Rise Apartments Residential - High Rise Apartments Commercial Institutional Transportation, Communication and Utilities Open Space and Undeveloped Protected Watershed % Rented Houses by Population Kilometers Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by: Bronwyn Agrios

43 Positive results indicate that crime is having a positive effect on rented units. In actuality, because crime is a negative variable, where is has a positive value on rented units, there is higher level of crime. The east west trend may be representative of amount of ground vegetation cover, which increases with movement west into less populated enumeration areas. This can be seen by overlaying orthophoto data with the rent crime data points to visualize where the dense vegetation occurs. Further analysis on the affects of crime on rented units was undertaken using housing value data, also gathered from 1996 census data. According to GWR, the results from this test are not spatially significant as indicated by the p-value of the Monte Carlo significance test, but the visual pattern of results are interesting as seen in the map titled Average Gross Rent in Relation to Crime. 43

44 Average Gross Rent in Relation to Crime District of West Vancouver, 1996 Average Gross Rent Land Use Parks and Protected Natural Areas Lakes Residential - Single Family Residential - Townhouse and Low Rise Apartments Residential - High Rise Apartments Commercial Institutional Transportation, Communication and Utilities Open Space and Undeveloped Protected Watershed Kilometers Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by: Bronwyn Agrios

45 Average gross rent of units shows high property crime areas in the along the water by Ambleside Park, around Park Royal Mall, and in the increasingly wooded areas further west. In these areas, crime is having a negative affect on the average gross rent ($) of units. The affect of crime decreases into northern residential areas. RESULTS The results suggest that rented units most affected by crime are located in the high traffic areas around the ferry terminal, park and wooded areas of Gleneagles and Horseshoe Bay as well as houses bordering the water. Based on the discussion by Groff and La Vinge, rented units in these areas may be affected because the houses are not densely spaced leaving excess ground covering for hiding as well as foliage that will get unruly unless well kempt. This, as mentioned above, is an indicator of rented units that may also not have alarm systems because renters do not invest money into their house. RECOMMENDATIONS It is recommended that an awareness campaign be issued to warn people of the habits of criminals on rented houses and the areas and houses that are most often targeted. As discussed earlier in the introduction, most criminals who commit residential and small scale commercial property crimes are acting on a whim and not involved in a large scale operation with studies of the area. On the whim crimes are likely to take easy targets and not go for the risky but rewarding theft. In this case rented homes showing the signs described by Groff and La Vinge are targeted because it is assumed that there is minimal security and the house may be older and easier to break into. 45

46 VISIBLE MINORITIES ANALYSIS DISCUSSION This section examines the relationship between crime and visible minorities in the West Vancouver area. The purpose of this section of analysis is to examine whether the densities of Visible Minorities have a significant impact on crime occurring in the areas. The population of West Vancouver shows limited visible minority density based on 1996 census data and the results the analysis may not be as significant as age and income distribution. The minority population is a collaboration of different ethnic groups such as Black, South Asian, Chinese, Korean, Japanese, Southeast Asian, Filipino, Arab/West Asian, Latin American, and all others. The percentage of Visible Minorities is calculated by dividing the population data by total population. RESULTS The GWR results generally show a negative relationship between the visible minority and crime data. The GWR results offer two sections with useful data. Under the caption of Global Regression Parameters, the output contains the estimate of with the standard error of The negative sign of the estimate conveys that crime is negatively related to visible minorities, and the increase in population density of visible minorities will decrease the number of crimes in West Vancouver. The GWR Monte Carlo significance test results have the p-value of 0.84, which is not geographically significant. However, whether the estimate is geographically significant or not depends on their visual performance. On the map of Impact of Visible Minorities on Crime Occurrence we observed that high concentrations of visible minorities are generally located at the east and northeast side of West Vancouver. 46

47 The Impact of Visible Minorities on Crime Occurrences District of West Vancouver, 1996 Crime relates to Visible Minorities Percentage of population Density 0% - 5% % % % % % % % % Kilometers Source: Census 1996 West Vancouver Police Department District of West Vancouver Prepared by Vivien Ka Ki Leung

48 The enumeration area is represented by the background color from dark to light brown. The dot symbols on the map represent the impact of visible minorities toward crime. Red symbols locate at west side of the area and Ambleside commercial area indicate the positive impact on crime, whereas the green symbols in all other areas indicate negative impact on crime. It is interesting to see visually how the geographical location of visible minorities relates to the number of crimes. From the map we observed that the west area which contains the least visible minorities have a positive impact on crime. In another words, if the percentage of density increases by 1% then the number of crimes will increase by % to %. On the other hand, where the area contains high density of visible minorities at the east side of West Vancouver, there is a negative impact on crime. This lends to the idea that if the percentage of density increases by 1% then the number of crimes will most likely decrease. To summarize, these results reflected that although the concentration of visible minorities is negatively related to the crime statistics, and it is not significantly varied geographically, it still indicates an increase in density will decrease the number of crime. 48

49 POPULATION COMPOSITION DISCUSSION The spatial distribution of crime is determined by population composition and the associated socio-economic factors. These variables all interact in a complex way. In this part of the analysis, GWR is utilized to estimate the relationships between the density of crime incidents (dependent variable) and a set of socio-demographic variables (independent variables) in West Vancouver. For this GWR model, five variables were selected to test the relationship between the population characteristics and the crime density. These variables are: Table 1: Variables for the Population Composition Model Variables Description of Data LONEPAR Percent of lone-parent families RECEIMM Percent of recent ( ) immigrant population MOMTONG Percent of population whose mother tongue is a non-official language HOMLANG Percent of population whose home language is a non-official language MINOR Percent of visible minority population These variables were selected because they are significant characteristics in representing the population and how they interrelate to each other in different neighborhoods. More specifically, these variables were chosen to represent the heterogeneity of the population in the West Vancouver community. It is believed that crimes are likely to occur where the population is more diverse and fragmented. For example, people are less likely to interact with each other. Therefore, they are less likely to be aware of any unusualness in their neighborhood and to be looking out for each other. In a way, this creates more opportunities to criminal activities. The variables MOMTONG and HOMLANG were chosen because language may be a barrier for people to interact with one another. The variables LONEPAR, RECEIMM, and MINOR were chosen to represent the diversity of the neighborhoods. 49

50 RESULTS The results of the Population Composition Model are presented in Table 2. Table 2: Results for the Population Composition Model Variables Estimate P-value LONEPAR RECEIMM MOMTONG HOMLANG MINOR The results show that the variables RECEIMM and MINOR have a negative effect on the density of crime. 50

51 The Relationship between Recent Immigrants and Crime District of West Vancouver, 1996 Recent Immigrants (%) Land Use Natural areas Residential areas Commercial areas Institutional areas Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Junnie Cheung Undeveloped areas Kilometers

52 The Effect of Visible Minorities on Crime District of West Vancouver, 1996 Visible Minority (%) Land Use Natural areas Residential areas Commercial areas Produced by Junnie Cheung Institutional areas Source: Census Undeveloped areas West Vancouver Police Department District of West Vancouver Kilometers

53 In other words, crime incidents tend to be lower in neighborhoods with higher proportion of recent immigrants and minorities. This may be because recent immigrants and minorities are more cautious and protective of themselves in a foreign community. The results show that the variables LONEPAR, MOMTONG, and HOMLANG have a positive effect on the density of crime. 53

54 The Effect of Lone- Parent Families on Incidents of Crime District of West Vancouver, 1996 Lone- Parent Families (%) Land Use Natural areas Residential areas Commercial areas Institutional areas Undeveloped areas Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Junnie Cheung Kilometers

55 The Effect of Population Diversity on Crime Incidents (1) District of West Vancouver, 1996 The Effect of Non- Official Languages Spoken at Home on Crime Use of Non-Official Language at Home (%) Land Use Natural areas Residential areas Commercial areas Institutional areas Undeveloped areas Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Junnie Cheung Kilometers

56 The Effect of Population Diversity on Crime Incidents (2) District of West Vancouver, 1996 Mother Tongue (non-official languages) in Relation to Crime Mother Tongue (%) Land Use Natural areas Residential areas Commercial areas Institutional areas Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Junnie Cheung Kilometers Undeveloped areas

57 In other words, crime incidents tend to be higher in neighborhoods with a higher proportion of lone-parent families and non-native speakers. This may be because they are less attached to the neighborhood. For example, most lone-parents have to work to raise their families. The P-values from the Monte Carlo significance test suggest that the results are insignificant in spatial variation. In addition, the GWR results show that there is little correlation between the demographic variables and the crime density. The GWR estimates were imported into ArcMap in order to see if it is possible to identify and examine the spatial pattern of crimes. An east-west directional pattern was observed on the maps. The GWR estimates tend to be significantly different when comparing those in the east and to those in the west. The divide is approximately where 27 th Street lies. Positive results for the variables LONEPAR and HOMLANG indicate that there are a high proportion of crimes in the west. In contrast, positive results for the variables RECEIMM, MOMTONG, and MINOR indicate that there are a high proportion of crimes in the east. The variables MOMTONG and MINOR reveal a fairly significant correlation to the density of crimes in the maps. In a sense, population heterogeneity, as measured by MOMTONG and MINOR, is positively related to the crime incidents. The higher proportion of MOMTONG and MINOR were found in Park Royal and Ambleside, where a higher number of crime incidents were documented. CONCLUSION Overall, the GWR analysis shows mixed results. More plausible results can be explained by examining the census data more carefully. A further look at the census data shows intuitively and theoretically plausible results and provides a further explanation on the GWR results. 57

58 For example, lone-parent families comprise of only 10% of all families in West Vancouver. The small proportion may affect the overall significance relating to crime. By examining the simple spatial distribution of the lone-parent family proportion, it is obvious that the highest proportion is clustered in the Capilano Indian Reserve. Hence, this may skewed the GWR results on this correlation. Although the neighborhoods are becoming increasingly multicultural, the proportion of the recent immigrants is not prominent in the residential neighborhoods. The distribution is fairly homogenous, averaging less than 20% of the population. The high P-value (0.8200) supports this observation. In this case, the GWR results were insignificant. 58

59 AGE ANALYSIS DISCUSSION The relationship between different age groups within the community and incidents of crime was an area we wanted to explore using GWR. We hypothesized that perhaps a high population density of youth would occur in areas reporting high levels of crime incidents. FBI statistics in the United States show that apprehended burglars are overwhelmingly males under 25 (Turnbull et al 2000), who are drawn to the semi-skilled nature of burglary. With that in mind we chose to further examine the young male population as a separate group. Further, FBI studies have also indicated that these younger criminals tend to rely on sporadic criminal opportunity rather than on planning, which is why we included the location of secondary schools on the map. We also wanted to identify areas with high population densities of elderly (65+) and elderly populations living alone as they would potentially be at a higher risk for being victims of crime, with the thought that they too would correspond to an elevated level of crime for that enumeration area. RESULTS To determine if the results we acquired from the GWR were spatially meaningful, we used a Monte Carlo simulation as a significance test. Without the aid of a simulation method, models like GWR only reveal a single outcome, which is generally the scenario most likely to occur. The simulation runs through every possible point then takes the original GWR result and compares the regression, ranking all the parameters and determining whether the results are geographically significant. A p-value < 0.05 is an indicator of a strong spatially significant parameter. The first GWR run we executed examined what effect, if any different age groups had on crime occurrences (The Impact of youths (15 to 24) on Crime Occurrences). 59

60 The Impact of Youths (15-24) on Crime Occurrences District of West Vancouver, 1996 Youths related to Crime Roads Land Use Parks and Protected Natural Areas Lakes Residential - Single Family Residential - Townhouse and Low Rise Apartments Residential - High Rise Apartments Commercial Institutional Transportation, Communication and Utilities Open Space and Undeveloped Protected Watershed Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Jennifer Woods Kilometers

61 This was the only GWR run executed during this project to receive a p-value of from the Monte Carlo geographic significance test, indicating to us that these results were highly significant. The results showed a correlation between youths (15 24) and crime in the enumeration areas centered on the Park Royal and Ambleside commercial districts. This is an area of mixed land use, where high density housing and commercial uses dominate the landscape. Previous studies indicate that areas of mixed land use are often the location of elevated incidents of criminal activity, when compared to the surrounding neighborhoods (Turnbull et al 2000). Certainly our incident data agrees with that theory, as the majority of crimes in West Vancouver appear to be occurring along this corridor dominated by major transportation, commercial and residential uses. We next broke down the impact of youths to just examine the impact of young males (15-24) on the occurrence of crime (The Impact of Young Males (15 to 24) on Crime Occurrences). 61

62 The Impact of Young Males (15 to 24) on Crime Occurrences District of West Vancouver, 1996 Crimes Related to Males Roads Land Use Parks and Protected Natural Areas Lakes Residential - Single Family Residential - Townhouse and Low Rise Apartments Residential - High Rise Apartments Commercial Institutional Transportation, Communication and Utilities Open Space and Undeveloped Protected Watershed Kilometers Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Jennifer Woods

63 Here we tried to see if a connection existed between crime rates in the community and that particular group, which FBI statistics have identified as committing a proportionally high number of offences. The GWR results from this run pointed to a positive relationship between incidents of crime and young men, with a maximum value of Thus, we can estimate a maximum increase in crime of 0.04% for each additional 1% increase in the population of young males in the enumeration area. However, the Monte Carlo simulation did not find these results to be geographically significant, giving the run a p-value of In our final step we used GWR to examine the elderly population of West Vancouver, specifically males and females over the age of 65 (The Impact of Elderly Population on Crime Occurrences). 63

64 The Impact of Elderly Population on Crime Occurrences District of West Vancouver, 1996 Crimes in Relation to Persons Percentage of Population 65+ 0% % 6.31% % 14.44% % 22.07% % 37.75% % Source: Census 1996 West Vancouver Police Department District of West Vancouver Produced by Jennifer Woods Kilometers

65 With this particular data, the GWR indicated that there was a relationship between the 65+ populations and incidents of crime. From these results we can estimate that with every 1% increase in the elderly population, there may be up to a 0.01% increase in the incidents of crime. This final result was much lower than we had anticipated, as we had originally felt that elderly people would be likely targets for property crime. However, as with the results from the analysis of the young male impact on crime, the elderly results were found not to be geographically significant; the Monte Carlo simulation gave the run a p-value of RECOMMENDATIONS As our data indicates there to be a relationship between incidents of property crime and youth, it may be prudent to implement an awareness campaign focusing on the secondary schools, both in the public and private systems, to help educate youth on the wrongfulness of committing property crime of any degree. Certainly we are not claiming that teens are the main perpetrators of property crimes in West Vancouver, though research in other jurisdictions does show a high level of crime can be attributed to this group, especially the male component. We simply feel that by focusing preventative attention on the secondary schools, problems in the future may be avoided. As well, campaigns focused on both the elderly populations and residents located in the outlying suburban areas of West Vancouver would also be an important step to help prevent property crimes. Though our results did not point to the elderly population as being highly vulnerable, with home invasions targeting the elderly occurring in other communities in the Lower mainland, it is important to make this particular group Reminding people of the basic steps they can take to reduce the likelihood of becoming victims of such an invasion can never be over estimated. 65

66 CONCLUSIONS The results gathered from the GWR analysis of crime points and census data allowed us to draw some interesting conclusions about the patterns of crime in relation to housing tenure, visible minorities, age of residents, and various other relevant socioeconomic factors. Although we were able to draw logical conclusions from the analysis, as seen in the results sections, GWR has not proved to be a superior tool for investigating crime data in relation to population characteristics to determine areas and reasons of high and low crime. It was found that results were easily changed with the addition and subtraction of variables from the original GWR analysis (see Appendix 6.3.). Running the regression model with multiple independent variables formed different results then running the variables separately. This observation forced us to run all variables that were relatively significant, as independent members of the analysis. For example, visible minority run together with crime has positive results and when the visible minority variables are separated and run individually with crime points the results are negative. These results foreshadow the larger problem of user defined input impacting the outcome of the analysis. This is especially prevalent in GWR because of the multiple input options including independent/dependent variables, bandwidth, kernel density and others describe above. Another factor that impacts the level of error in analysis is to what degree the interpretation of results is correct. The analytical output consists of many spatially irrelevant sections that are very difficult to interpret and most often the parameters are simply entered into ArcMap, viewed, and analyzed based on one or two useful sections. However, the outputs can be relevant to a high level of analysis, but are not useful in this particular project. This is why it is suggested that GWR be used in conjunction with CrimeStats for further analysis of the West Van crime data. This is not to say that GWR is not a useful tool for statistical spatial analysis as there are many examples of its practical uses. Based on literature reviewed and experience with the program, GWR is useful for analyzing the change over space of housing prices, disease 66

67 outbreak, school performance, and political voting patterns. It is useful in these areas of study because they are more highly based on regular, planned occurrences and patterns, while crimes are often committed at random. The GWR analysis of this project assumes that criminals in the area preplan and learn about lifestyle of the neighbouthood, while it is stated in the age analysis that most crimes committed by young males tend to be more opportunistic in nature. With specific regard to this project, GWR caused problems because the ea and crime points used were not extensive enough for the level of analysis completed. For example, ea data is collected at night and therefore not representative of daytime activities, such as children as school and parked cars at areas of business and the number of crime point only totaled 590 crimes. The poor data correlation was especially evident in the results from the Monte Carlo significance test, which describes if the results are spatially important to the analysis. Few of the results obtained were spatially significant according to the test and is representative of the poor correlation between the ea points, crime points, and crime area under analysis. This problem can be discussed as the MAUP (Modifiable Areal Unit Problem). The areal units utilized in the study were based on ea (enumeration area) polygons, these polygons represent arbitrary divisions onto the underlying population, and therefore the census data utilized represents divisions of an artificial nature and not of reality. This idea makes it understood why the results from the spatial result were poor because the spatial test was completed using arbitrarily organized spatial data. In this was the ea centroids are misleading. Based on the GWR analysis done in this crime project, it is not recommended that future analysis be conducted primarily with GWR, but that it is used as a supplement to other methods of analysis like CrimeStats. 67

68 2.3. OTHER METHODS OF ANALYSIS 68

69 SEASONALITY AND WEATHER A simple way to interpret the crime data collected in West Vancouver is to conduct an analysis related to weather and seasonality. While it is not completely known whether or not weather is related to crime, it is still important to make the best use of the data and assess all possible spatial relationships based on available data. There are few studies that have attempted to correlate crime and seasonality or weather. In Cohn and Rotton s analysis of crime and seasonal trends, they discovered that: more crimes were reported during summer than other months (2000). Furthermore, in an unrelated study, it was reasoned that the number of days on which precipitation exceeded 25mm was positively related to robbery rates (DeFonzo 1984). These findings, however, were not consistent with what we discovered in West Vancouver. On the contrary, more crimes were committed in the winter months than the summer ones, and more crimes were also committed on non-rainy days. Table 1: West Vancouver Monthly Crimes Number of Reported Crimes April May June July August September Months ( ) October November December January February March All Crimes Burglaries What is to be gathered from existing research is that generally, inconsistent and sometimes contradictory results characterize the literature on weather. The place specificity of each area under scrutiny essentially disallows for any one study to be applied to another research area. Thus, understanding the effects of seasonality and weather on crime in Minneapolis (Cohn and Rotten 2000), does not guarantee and 69

70 understanding of similar effects in West Vancouver. Therefore, all the findings from the following research should considered specific to West Vancouver only, and should also be approached with a level of caution. Weather data that was used in this analysis was obtained courtesy of the National Weather Service in the US. This data was obtained in comma delimited format, with temperatures in Fahrenheit and precipitation levels in inches. These numbers were then converted into metric and then finally converted into a database (DBase IV) file. Crime data was procured from the City of West Vancouver Website and appropriate levels of massaging were undertaken to allow for it to be integrated into a GIS. Unfortunately, from a spatial point of view, it was almost impossible to reconcile the crime data points with a particular temperature or precipitation level. That is to say then where a crime was reported, the associated temperature data was not present. This was due to the fact that it is highly difficult to display time-series data (time of day, day of month etc.), with spatial data. Thus, a different type of analysis was undertaken to add a spatial aspect to weather and seasonality. This was done in two ways. The first way was to assess the spatial differences between rainy day crimes and non-rainy day crimes. Since there was no timeseries analysis involved in this type of inquiry it was far easier to display in a spatial manner. Also, since it was assumed that any day when precipitation was reported, there was precipitation all day, it was possible to create a split file of crimes with rainy day crimes and non-rainy day crimes. The resulting densities of these two sets of crimes could be mapped and compared easily. The second type of analysis that was undertaken was to determine the effect of seasonality of criminal activity. This was done without the use of the weather data but rather, the analysis relied on the dates that were contained within the crime database. Crime days were sorted into four seasonal categories and split off into separate files. In total four surfaces showing the density of seasonal crimes per square kilometer were created. The division of the four seasons was done intuitively with summer crimes consisting of all crimes in June, July and August, fall crimes consisting of September, 70

71 October and November, winter crimes consisting of December, January and February, and spring crimes consisting of March, April and May. DISCUSSON Rainy Days versus Non-Rainy Days In total there were 350 days used in this study (15 were omitted due to unavailability of weather data). In total there were 363 crimes committed on days were precipitation was equal to zero, and 234 crimes were committed on days were precipitation was greater than zero. Or, 39% of crimes were committed on rainy days and 61% of crimes were committed when it did not rain. While it appears that at outset that more crimes occur when it does not rain, it should also be understood that it rained 135 days out of 350 or 39% of the time. This would suggest that from a criminal activity perspective, rain has no effect on the frequency of crimes. However, that is not to say that there are no spatial implications for the locations of these two types of crimes. Indeed, according to the maps produced, there exist significant spatial differences between the two types of surfaces. 71

72 The Relationship between Precipitation and Crime District of West Vancouver Density of Crimes when there was no Precipitation Measured Density of Crimes per Square Kilometer Crimes Roads Source: National Weather Service (Seattle, WA) West Vancouver Police Department District of West Vancouver Produced by Aaron Licker Kilometers Density of Crimes when there was Precipitation Measured Density of Crimes per Square Kilometer Crimes Roads Source: National Weather Service (Seattle, WA) West Vancouver Police Department District of West Vancouver Produced by Aaron Licker Kilometers

73 In comparing the two surfaces, it becomes clear that there is a greater density of crimes in the Caulfield and Horseshoe Bay areas when it is not a rainy day. While it would be spurious to assume that rain somehow inhibits criminal activity in these areas, it must be noted that most crimes in these areas are property thefts, which would be bettereffectuated on dry days. Also, there is significant amount of criminal activity at the ferry terminal. Since it is well understood that on sunny days, and in the summer, ferry usage goes up, it stands to reason that corresponding crime rates would follow. Therefore, a secondary hot spot of criminal activity presents itself in this area. Seasonality and Criminal Activity Maps displaying seasonality data (Density of Summer Crimes, Density of Fall Crimes, Density of Winter Crimes, Density of Spring Crimes) show the densities of criminal activity related to seasonal changes. 73

74 Density of Seasonal Crimes per Square Kilometer District of West Vancouver Density Surface of Winter Crimes Density of Crimes per Square Kilometer Winter Crimes Roads Source: West Vancouver Police Department District of West Vancouver Produced by Aaron Licker Kilometers Density Surface of Spring Crimes Density of Crimes per Square Kilometer Spring Crimes Roads Source: West Vancouver Police Department District of West Vancouver Produced by Aaron Licker Kilometers

75 Density of Seasonal Crimes per Square Kilometer District of West Vancouver Density Surface of Summer Crimes Density of Crimes per Square Kilometer Summer Crime Roads Source: West Vancouver Police Department District of West Vancouver Produced by Aaron Licker Kilometers Density Surface of Fall Crimes Density of Crimes per Square Kilometer Fall Crime Roads Source: West Vancouver Police Department District of West Vancouver Produced by Aaron Licker Kilometers

76 There were 118 crimes committed in the fall months, 201 crimes committed, 133 crimes committed in the spring months, and 138 crimes were committed in the spring months. Clearly, more crimes were committed in the winter months; this could perhaps be due to the longer period of darkness, or due to increased amounts of shoppers at the mall. Although the numbers of crimes are not equal between the seasons, it is still possible to compare their density with a reasonable amount of accuracy. This is due to the fact that the spatial distribution from each density surface is unique. That is to say all four maps display different densities in different places. As has been mentioned, criminal activity in the winter months was the most widespread. While there was a large hotspot centered on Park Royal Mall, there was also an ancillary concentration in the Dundarave/Caulfield area. The first concentration is easily explained through the increased population densities of the area, the second concentration is harder to understand. Initial evidence points to increased break and enters in these areas suggesting that they occur primarily in the winter months when families are away for vacation. Spring Crimes had the most clustered distribution, centered in the Park Royal area. The majority of these crimes can be explained better by the increased population density of the area then by any other outside factor. In the summer months (map 3) increased criminal activity is present in the Horseshoe bay area; this hot spot can be directly attributed to thefts from cars in the ferry terminal parking lot. Due to the fact that the ferry is still in operation during the fall months, a corresponding hot spot appears in the Horseshoe bay area. However, on the whole, densities of crimes in the spring months are the least spatially concentrated, which suggests that there are no particular influences on criminal activities during this period. 76

77 BUS ROUTES Previous research has indicated that a link between public transit stations and property crime exists; Groff and La Vigne used this specific factor as part of their study which accurately predicted crime rates in Grier Heights, Georgia. The theory is that criminals tend to locate new targets by scouting potential targets along their daily commute, be that along major roadways or public transit routes. Areas that are serviced by frequent public transit could therefore be at higher risk of being burgled. With this in mind, we thought that it would be interesting to look at the spatial relationship between crime incidents and bus stops in West Vancouver, and try to determine whether a link could be made between the two. 77

78 Public Transit Routes and Stops: Orthophoto Reference District of West Vancouver West Vancouver Blue Bus Stations 257 Stations 255 Stations 252 Stations 254 Stations 251 Stations 253 Stations 250 Stations Kilometers Source: Translink Aerial Photos May/June 1995 Selkirk Remote Sensing Produced by Kyle MacDonald