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Descriptive Spatial Analysis

Descriptive Spatial Analysis. Definition of Crime Mapping Single Symbol Mapping Buffers Chart Mapping Graduated Mapping Hotspot Analysis Practical Examples. Definition of Crime Mapping. Crime analysis is …

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Descriptive Spatial Analysis

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  1. Descriptive Spatial Analysis • Definition of Crime Mapping • Single Symbol Mapping • Buffers • Chart Mapping • Graduated Mapping • Hotspot Analysis • Practical Examples

  2. Definition of Crime Mapping • Crime analysis is … • A geographic information system (GIS) is a set of computer-based tools that allow a person to modify, visualize, query, and analyze geographic and tabular data. • Consequently, computerized crime mapping is the process of using a geographic information system in combination with crime analysis techniques to focus on the spatial context of criminal and other police activity. Source: Boba, R. (Forthcoming). Crime mapping. In Encyclopedia of criminology. Chicago: Fitzroy Dearborn Publishers.

  3. Single Symbol Mapping • Uses individual symbols to represent point, line, and polygon features. • Allows for a detailed analysis of small amounts of data.

  4. Example: Too Much Data

  5. Example: Tabular Data

  6. Example: Geographic Data

  7. Example: Geographic Data

  8. Buffers • A buffer is a zone of a specified distance around a feature. • Points, lines, and polygons can be buffered. • Buffers are useful for proximity analysis and can be designated at one or many intervals (e.g., 500 feet, 1,000 feet, 1 mile).

  9. Buffers: Point Example

  10. Buffers: Line Example

  11. Buffers: Polygon Example

  12. Chart Mapping • A chart map allows for the display of the values of many data attributes at once with either a pie or a bar chart. • The mapping program takes the values for numerous variables and displays them in a pie or a bar chart on the designated location on the map.

  13. Chart Mapping: Pie Chart Example

  14. Chart Mapping: Bar Chart Example

  15. Graduated Size Mapping • Data are summarized so that symbols (point or line features) are altered in size to reflect the frequencies in the data. • Reflect more incidents at a given location with a larger symbol or a thicker line.

  16. Example: Too Much Data

  17. Graduated Size Point Mapping Example

  18. Graduated Size Line Mapping Example

  19. Graduated Color Mapping • Point, line, or polygon features are shaded according to a statistical formula, custom setting, or unique value. Also called choropleth mapping. • Most Commonly Used: Unique Value, Natural Breaks (default), Custom. • Others: Quantile, Equal Area, Equal Interval, Standard Deviation.

  20. Points Shaded by Unique Value: Geographic Data

  21. Points Shaded by Unique Value: Geographic Data

  22. Points Shaded by Unique Value: Tabular Data

  23. Points Shaded by Unique Value: Tabular Data

  24. Natural Breaks • The default classification method in most GIS programs. • Identifies natural break points between classes using a statistical formula. Graduated Polygon Example

  25. Custom • Ranges can be determined by the user and are not based on the data. • Important for comparing the same type of data over time. Graduated Polygon Example

  26. Quantile Graduated Polygon Example Each class contains the same number of features (data points).

  27. Equal Interval Graduated Polygon Example • Divides the range of attribute values into equal sized sub-ranges. • Features are then classified based on the sub-ranges.

  28. Standard Deviation Graduated Polygon Example The GIS determines the mean value and then places class breaks above and below the mean based on the standard deviation.

  29. Use of Classifications • Classifications are the descriptive statistics of spatial analysis. Thus, they should be controlled by the analyst and carefully applied. • A danger is that the GIS has defaults (natural breaks into five categories) and analysts do not change them. Guidelines: • Use most, if not all, of the classifications in the beginning of the analysis to determine the nature of the data and its distribution. • Experiment with number of categories and classifications to see how the maps change. • Determine the purpose of the analysis and choose the best classification.

  30. Exercise Scenario: You are a member of a problem-solving team tasked with addressing an ongoing robbery problem in the city. You have been asked to bring an analysis of robbery to the first meeting. What type of map would you bring? How much data? Which unit of analysis? Which classification?

  31. Graduated Points

  32. Graduated Color Polygons: Natural Breaks

  33. Graduated Color Polygons: Standard Deviation

  34. Exercise Scenario: As part of an impact evaluation for a problem analysis project to reduce commercial burglary, you are asked to prepare a map that compares before and after (same amount of time) the response by block group. How would you present this in two maps? In one map?

  35. First of two maps

  36. Second of two maps

  37. In one map: Difference between Pre and Post

  38. Exercise Scenario: The chief asks you to examine aggravated assault and simple assault in the city to see if there are differences in the relative frequencies by block group (or other polygon). That is, are there some areas that are higher in aggravated assault than others and are those the same that are higher in simple assault?

  39. Using Standard Deviation: Aggravated Assault

  40. Using Standard Deviation: Simple Assault

  41. Using Quantile: Aggravated Assault

  42. Using Quantile: Simple Assault

  43. Hotspot Analysis In this context, the term hotspots refers to concentrations of events confined to a particular geographic area that occur over a specific time period. Hotspots are also referred to as clusters or concentrations. Methods for determining hotspots… • Graduated color maps • Map grids • Ellipses • Kernel density interpolation

  44. Hotspot Analysis Graduated Color Maps • Point, line, or polygon features are shaded according to a statistical formula, custom setting, or unique value. • In this example, census groups are shaded by the number of incidents. Note: incidents are placed on the map at their address location for reference.

  45. Hotspot Analysis Map grids • Each grid cell is shaded according to the number of incidents. • Unlike the preceding graduated color map, this method allows for smaller search areas. • However, the grids are arbitrary and may not depict realistic separation of land areas.

  46. Hotspot Analysis Ellipses • Ellipses are drawn around the most dense concentrations of activity. • Software such as S.T.A.C. (Spatial and Temporal Analysis of Crime), developed by the Illinois Criminal Justice Information Authority (ICJIA), uses a statistical method to find clusters. 2nd order cluster 1st order clusters

  47. Hotspot Analysis Kernel Density Method • A grid is applied to the map, and a “score” is derived based on the number of incidents within each grid cell as well as the distance to other incidents. • Cell size and search radius can be dictated by the user.

  48. Hotspot Analysis Factors to consider: • Definition of a hotspot • Choice of variables • Number of hotspots • Scale • Grid size and search area • Visual display • Comparisons There are many different methods of hotspot analysis, and each technique will reveal different groupings and patterns within the groups.

  49. Practical Examples of Descriptive Mapping

  50. To assist in resource allocation of ATF agents: analysis of gun tracing incidents per county for numerous states.

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