Elementary Spatial Analysis

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# Elementary Spatial Analysis

## Elementary Spatial Analysis

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##### Presentation Transcript

1. Elementary Spatial Analysis GEOG 370 Instructor: Christine Erlien

2. Overview • Spatial Analysis • Flowcharting • Query • Defining spatial characteristics • Higher-level objects • Centroids • Nodes • Boundaries • Networks • Regions

3. Spatial Analysis • Spatial analysis: Way in which we turn raw data into useful information • A set of techniques whose results are dependent on the locations of the objects being analyzed • Variety of methods • Powerful computers • Intelligent users

4. Input Output Operation (Plus conditions) Preparing a Spatial Analysis: Flowcharting Flowchart tools provided by: ESRI’s Model Builder, ERDAS’s GIS Modeler, etc.) Objective – systematizing thinking and documenting procedures about a GIS application/project General form of most GIS flowcharts: From Fundamentals of Geographic Information Systems, Demers (2005)

5. GIS Data Query • Important, useful tool associated with DBMS • Why? • Narrowing down information • Better understanding of map • Complexity • How entities of interest spatially related to other data layers • Ability to make further measurements, comparisons • Total numbers  relative numbers (e.g., density) • What might you want to know? • Which features occur most often • How often they occur • Where are they located?  spatial pattern

6. GIS Data Query • What is it? • Using tools to find records meeting specific criteria • How? • Select criteria • Use operators to define expression • Simple • Complex And: Intersection of sets Ex.: ([area] > 1500) and ( [b_room] > 3) Or: Union of sets Ex: ([age] < 18 or [age] > 65) Not: Subtracts one set from another set Ex.: ([sub_region] = "N Eng") and ( not ( [state_name] = "Maine"))

7. Query Selected Records Records New Set Selected Records Selected Records Add to Set Selected Records Selected Records Select from Set Successive Querying Graphic: Dr. Jean-Paul Rodrigue, Dept. of Economics & Geography, Hofstra University

8. GIS Data Query: Vector • Examining vector entities’ attributes • Check spatial objects’ properties • Using identify tool • Using find tool • Performing queries

9. GIS Data Query: Raster • Examining raster attributes • Unique colors assigned to attribute values • Tabulating results  # of grid cells in each category • For those interested in landscape ecology  fragmentation statistics

10. Raster Data Query: Fragmentation Statistics • Landscape Composition • Proportional Abundance of each Class • Richness: Number of different patch types • Evenness: Relative abundance of different patch types  • Landscape Configuration • Patch size distribution and density • Patch shape complexity • Isolation/Proximity See Fragstats website: http://www.umass.edu/landeco/research/fragstats/fragstats.html

11. Defining Spatial Characteristics:Points • Nominal, Ordinal, Interval/ratio data • Define, separate, retrieve on the basis of: • Category • Class • Magnitude • Examining classes of data & the individuals within each class • Distance between features in same category, class • Distribution: Clustered vs. random or regular • Examining relationships between point objects & other objects

12. Dr. John Snow & the Cholera Map http://en.wikipedia.org/wiki/Image:Snow-cholera-map.jpg

13. http://www.unl.edu/nac/conservation/atlas/Map_Html/Demographics/National/Minority_Operated_Farms/1997.htmhttp://www.unl.edu/nac/conservation/atlas/Map_Html/Demographics/National/Minority_Operated_Farms/1997.htm

14. Defining Spatial Characteristics:Lines • Define, separate, retrieve on the basis of: • Category • Class • Magnitude • Single line entity • Attribute values may change along length • Lines in relation to their surroundings • Easiest in vector, using topological data • Length, Azimuthal direction, Shape/sinuosity • For entire line or its individual segments

15. Defining Spatial Characteristics: Lines Sinuosity information is used in developing stream classifications http://forest.mtu.edu/staff/mdhyslop/gis/sinuosity.html

16. http://clerk.ci.seattle.wa.us/~ordpics/115137At10TRFigA4.gif

17. Defining Spatial Characteristics:Areas • Define, separate, retrieve on the basis of: • Category • Class • Magnitude • Shape: Deviation from particular geometry (e.g., circle or square) • Elongation: Ratio between long & short axes • Orientation • Size  perimeter, area, length • Contiguity: Measure of wholeness (vs. perforation) • Heterogeneity: Measure of how much map area is in contact with polygonal features sharing same attributes

18. Defining Spatial Characteristics:Areas Minor axis Major axis 2.5 2.5 R = 1 3.5 1.5 R = 2.33 Major axis • Along longest part of polygon • Must divide polygon in two equal parts Minor axis • Along shortest part of polygon • Must divide the polygon in two equal parts Major axis / Minor axis ratio • Values > 1 denote elongated polygon • Value = 1 denotes uniform polygon Graphic: Dr. Jean-Paul Rodrigue, Dept. of Economics & Geography, Hofstra University

19. Defining Spatial Characteristics:Areas • Perimeter • Length of all segments of closed polygon • Length of the contact surface of a feature with other features • Lake shoreline • Fence • Area Area Perimeter Graphic: Dr. Jean-Paul Rodrigue, Dept. of Economics & Geography, Hofstra University

20. Perimeter = 7 miles Area = 25 sqr miles CI = 7 / 25 = 0.28 CI = 15 / 25 = 0.60 Area = 25 sqr miles Perimeter = 15 miles Defining Spatial Characteristics:Areas • Shape • Perimeter to Area Ratio • perimeter/area • Expression of the geographical complexity of a polygon • High ratio  complex • Low ratio  simple Graphic: Dr. Jean-Paul Rodrigue, Dept. of Economics & Geography, Hofstra University

21. Higher Level Objects • Higher-level objects: Need to be determined rather than being encoded through digitizing • Types • Points • Lines • Areas

22. Higher-level Point Objects • Types • Centroids • Nodes • Centroid: Indicates geographic center of polygon • Simplest to calculate for simple shapes (e.g., rectangle, circle) • Not well-suited to raster • Calculated using the trapezoidal rule • Polygon separated into overlapping polygons • Each polygon’s centroid calculated, then weighted-average calculated

23. Higher-level Point Objects: Centroids From Fundamentals of Geographic Information Systems, Demers (2005)

24. Higher-level Point Objects: Centroids • Types of Centroid: • Simple centroid: Absolute geographic center of polygon • Center-of-gravity centroid (mean center): Central point of distribution • Weighted mean center: Centroid calculated on basis of location & associated weighting factor

25. Centroid Types From Fundamentals of Geographic Information Systems, Demers (2005)

26. Centroid Types: Mean Center Mean Center (Center of gravity) Average individual X &Y coordinates for all points in the coverage/layer Result: Single pair of X, Y values representing the central point of distribution From Fundamentals of Geographic Information Systems, Demers (2005)

27. Centroid Types: Weighted Mean Center • Characteristics from attribute table used as additional weighting factor • Weighting factor • Each X, Y coordinate multiplied by a weight • Weighted mean center derived from sum of weighted coordinates divided by number of points

28. Centroid Types: Weighted Mean Center From Fundamentals of Geographic Information Systems, Demers (2005)

29. Higher-level Point Objects: Nodes • Locators along line & area entities • Generally encoded during input • Difficulties arise when coded as point rather than node • Used to isolate line segments

30. Higher-level Line Objects • Types • Boundaries/borders: Major change in single or multiple attribute values as move across • Networks: Interconnected line entities whose attributes share a common theme related to flow

31. Higher-level Line Objects: Networks • Types: • Straight line network • Branching network • Circuit • Networks can be directed or undirected • Directed: Flows move only in a single direction • Undirected: Flows can go back and forth along the network in either direction • If attribute data are lacking  limits ability to use linear features as higher-level objects

32. Higher-level Area Objects • Regions: Areas of uniform content within a coverage • Homogeneous sets or homogeneous combinations of factors • Types: • Contiguous: Wholly contained in a single polygon • Fragmented: Comprised of more than 1 polygonal form separated by intervening space that doesn’t share same attributes • Perforated: Uniform polygon interspersed with smaller polygons not sharing the same mix of attributes • Region: Matrix • Perforations: Smaller internal polygons that don’t share same attributes

33. Higher-level Area Objects From Fundamentals of Geographic Information Systems, Demers (2005)

34. Wrapping up: You should know • The purpose of flowcharting • The why & how of using attributes in search/query • What higher-level attributes are & what they can be used for • Centroids • Nodes • Boundaries • Networks • Regions