Spatial Analysis cont. Density Estimation, Summary Spatial Statistics, Routing

1. Spatial Analysis cont.Density Estimation, Summary Spatial Statistics, Routing Longley et al., chs. 13,14

2. Density Estimation • point interpolation to estimate a continuous surface vs. • density estimation - surface is estimated from counts within polygons • (e.g., population density surface derived from total population counts in each reporting zone)

3. Objects to Fields • map of discrete objects and want to calculate their density • density of population • density of cases of a disease • density of roads in an area • density would form a field • one way of creating a field from a set of discrete objects

4. Density Estimation and Potential • Spatial interpolation is used to fill the gaps in a field • Density estimation creates a field from discrete objects • the field’s value at any point is an estimate of the density of discrete objects at that point • e.g., estimating a map of population density (a field) from a map of individual people (discrete objects)

5. Density Estimation Using Kernels • Mathematical function • each point replaced by a “pile of sand” of constant shape • add the piles to create a surface

6. Width of Kernel • Determines smoothness of surface • narrow kernels produce bumpy surfaces • wide kernels produce smooth surfaces

7. Example • Density estimation and spatial interpolation applied to the same data • density of ozone measuring stations vs. • Interpolating surface based on locations of ozone measuring stations

8. Using Spatial Analyst

9. Kernal too small?(radius of 16 km)

10. Kernel radius of 150 km

11. What’s the Difference?

12. Summary Spatial Statistics Longley et al., chs. 5,14

13. Descriptive Summaries • Ways of capturing the properties of data sets in simple summaries • mean of attributes • mean for spatial coordinates, e.g., centroid

14. Spatial Min, Max, Average

15. An example of the use of centroids to summarize the changes in point patterns through time. The centroids of four land use classes are shown for London, Ontario, Canada from 1850 to 1960. Circles show the associated dispersions of sites within each class. Note how the industrial class has moved east, remaining concentrated, while the commercial class has remained concentrated in the core, and the residential class has dispersed but remained centered on the core. In contrast the institutional class moved to a center in the northern part of the city.

16. Spatial Autocorrelation Tobler’s 1st Law of Geography:everything is related to everything else, but near things are more related than distant things S. autocorrelation: formal property that measures the degree to which near and distant things are related. Close in space Dissimilar in attributes Attributes independent of location Close in space Similar in attributes Arrangements of dark and light colored cells exhibiting negative, zero, and positive spatial autocorrelation.

17. Why Spatial Dependence? • evaluate the amount of clustering or randomness in a pattern • e.g., of disease rates, accident rates, wealth, ethnicity • random: causative factors operate at scales finer than “reporting zones” • clustered: causative factors operate at scales coarser than “reporting zones”

18. Moran’s Index • positive when attributes of nearby objects are more similar than expected • 0 when arrangements are random • negative when attributes of nearby objects are less similar than expected I = nS S wijcij / SS wijS(zi - zavg)2 n = number of objects in sample i,j - any 2 of the objects Z = value of attribute for I cij = similarity of i and j attributes wij= similarity of i and j locations

19. Moran’s Indexsimilarity of attributes, similarity of location Dispersed, - SA Extreme negative SA Independent, 0 SA Spatial Clustering, + SA Extreme positive SA

20. Crime Mapping • Clustering - neighborhood scale

21. Geary’s c Ratio • Like Moran’s Index, use a single value to describe spatial distribution • e.g., of elevations in DEM cells less than 1 (clustered) 1 greater than 1 (random) • e.g., spatial autocorrelation indicator of information loss during conversions between DEMs and TINs

22. Moran’s and Geary’s Lee and Marion, 1994, Analysis of spatial autocorrelation of USGS 1:250,000 DEMs. GIS/LIS Proceedings.

23. Fragmentation Statistics • how fragmented is the pattern of areas and attributes? • are areas small or large? • how contorted are their boundaries? • what impact does this have on habitat, species, conservation in general?

24. 1975 1986 1992 Note the increasing fragmentation of the natural habitat as a result of settlement. Such fragmentation can adversely affect the success of wildlife populations.

25. Fragstats pattern analysis for landscape ecology http://www.innovativegis.com/products/fragstatsarc/

26. FRAGSTATS Overview • derives a comprehensive set of useful landscape metrics • Public domain code developed by Kevin McGarigal and Barbara Marks under U.S.F.S. funding • Exists as two separate programs • AML version for ARC/INFO vector data • C version for raster data

27. FRAGSTATS Fundamentals • PATCH… individual parcel (Polygon) A single homogeneous landscape unit with consistent vegetation characteristics, e.g. dominant species, avg. tree height, horizontal density ,etc. A single Mixed Wood polygon (stand) CLASS… sets of similar parcels LANDSCAPE… all parcels within an area

28. FRAGSTATS Fundamentals PATCH… individual parcel (Polygon) • CLASS… setsof similar parcels All Mixed Wood polygons (stands) LANDSCAPE… all parcels within an area

29. FRAGSTATS Fundamentals PATCH… individual parcel (Polygon) CLASS… sets of similar parcels • LANDSCAPE… allparcels within an area “of interacting ecosystems” e.g., all polygons within a given geographic area (landscape mosaic)

30. FRAGSTATS Output Metrics • Area Metrics (6), • Patch Density, Size and Variability Metrics (5), • Edge Metrics (8), • Shape Metrics (8), • Core Area Metrics (15), • Nearest Neighbor Metrics (6), • Diversity Metrics (9), • Contagion and Interspersion Metrics (2) • …59 individual indices (US Forest Service 1995 Report PNW-GTR-351)

31. More Spatial Statistics Resources • Spacestat (www.spacestat.com) • S-Plus • Alaska USGS freeware (www.absc.usgs.gov/glba/gistools/) • Central Server for GIS & Spatial Statistics on the Internet • www.ai-geostats.org • GEO 441/541 - Spatial Variation in Ecology & Earth Science

32. Location-allocation Problems • Design locations for services, and allocate demand to them, to achieve specified goals • Goals might include: • minimizing total distance traveled • minimizing the largest distance traveled by any customer • maximizing profit • minimizing a combination of travel distance and facility operating cost

33. Routing Problems • Search for optimum routes among several destinations • The traveling salesman problem • find the shortest tour from an origin, through a set of destinations, and back to the origin

34. Routing service technicians for Schindler Elevator. Every day this company’s service crews must visit a different set of locations in Los Angeles. GIS is used to partition the day’s workload among the crews and trucks (color coding) and to optimize the route to minimize time and cost.

35. Optimum Paths • Find the best path across a continuous cost surface • between defined origin and destination • to minimize total cost • cost may combine construction, environmental impact, land acquisition, and operating cost • used to locate highways, power lines, pipelines • requires a raster representation

36. Solution of a least-cost path problem. The white line represents the optimum solution, or path of least total cost, across a friction surface represented as a raster. The area is dominated by a mountain range, and cost is determined by elevation and slope. The best route uses a narrow pass through the range. The blue line results from solving the same problem using a coarser raster.