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

2. Density Estimation 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)

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

5. (A) A collection of point objects (B) A kernel function for one of the points The kernel’s shape depends on a distance parameter— increasing the value of the parameter results in a broader and lower kernel, and reducing it results in a narrower and sharper kernel. When each point is replaced by a kernel and the kernels are added, the result is a density surface whose smoothness depends on the value of the distance parameter. AB

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 measured level of ozone at measuring stations

8. Using Spatial Analyst

9. Kernal too small?(radius of 16 km) each kernal isolated from neighbors

10. Kernel radius of 150 km

11. What’s the Difference?

12. A dataset with two possible interpretations: First, a continuous field of atmospheric temperature measured at eight irregularly spaced sample points, and second, eight discrete objects representing cities, with associated populations in thousands. Spatial interpolation makes sense only for the former, and density estimation only for the latter

13. GEO 565: Best locations for a new Beanery more factors: proximity to a highway, zoning concerns, income levels, population density, age, etc.

14. Pandemic Video

15. Origins of Computer Viruses

16. Directory Harvest Attacks

17. Origins of Email Spam

18. Density of “The Dance”

20. From Transformations to Descriptive Summaries: Summary Spatial Statistics

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

22. Spatial Min, Max, Average

23. Spatial Autocorrelation Arrangements of dark and light colored cells exhibiting negative, zero, and positive 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

24. 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”

25. 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 = n  w ij c ij /   w ij  (z i - z avg ) 2 n = number of objects in sample i,j - any 2 of the objects Z = value of attribute for I c ij = similarity of i and j attributes w ij = similarity of i and j locations

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

27. Crime Mapping Clustering - neighborhood scale

28. Moran’s Index similarity of attributes, similarity of location Maps from Luc Anselin, University of Illinois U-C, Spatial Analysis Lab

29. The Local Moran statistic, applied using the GeoDa software (available via geodacenter.asu.edu) to describe local aspects of the pattern of housing value among U.S. states (darker = more expensive) Below avg surrounded by above-avg Oregon, Arizona, Pennsylvania Moran’s I = 0.4011, clustering

30. Moran’s Index similarity of attributes, similarity of location Maps from Luc Anselin, University of Illinois U-C, Spatial Analysis Lab

31. Moran’s Index similarity of attributes, similarity of location Maps from Luc Anselin, University of Illinois U-C, Spatial Analysis Lab

32. Hotspot Analysis, Getis-Ord Video in 3 Parts http://bit.ly/cGzF7G http://bit.ly/a5342O http://bit.ly/9BFnHa

33. Moran’s and Geary’s http://bit.ly/7klty Lee and Marion, 1994, Analysis of spatial autocorrelation of USGS 1:250,000 DEMs. GIS/LIS Proceedings.

34. 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?

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

36. Fragstats pattern analysis for landscape ecology See GEO 580 site for direct links

37. 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

38. 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

39. FRAGSTATS Fundamentals PATCH… individual parcel (Polygon) CLASS… sets of similar parcels All Mixed Wood polygons (stands) LANDSCAPE… all parcels within an area

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

41. 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)

42. More Spatial Statistics Resources GeoDA (geodacenter.asu.edu) 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 541 – Spatio-Temporal Variation in Ecology & Earth Science