COMMON GIS TECHNIQUES FOR VECTOR AND RASTER DATA PROCESSING. Ophelia Wang, Department of Geography and the Environment, University of Texas

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1 COMMON GIS TECHNIQUES FOR VECTOR AND RASTER DATA PROCESSING Ophelia Wang, Department of Geography and the Environment, University of Texas

2 PART I: BASIC VECTOR TOOLS

3 CLIP A FEATURE BASED ON THE EXTENT OF ANOTHER FEATURE

4 USE THE UNIQUE VALUES OF A ZONING FEATURE TO SPLIT ANOTHER FEATURE

5 CREATE A HOLLOW FEATURE

6 SELECT PORTIONS OF A FEATURE THAT INTERSECTS WITH ANOTHER FEATURE

7 SELECT PORTIONS OF A FEATURE THAT DOES NOT OVERLAP WITH ANOTHER ONE

8 SELECT FEATURES BASED ON ATTRIBUTES OR SPATIAL RELATIONSHIP WITH OTHER FEATURES

9 COMBINE MULTIPLE FEATURES INTO ONE

10 CREATE BUFFER ZONES OF VARIOUS SIZES AND TYPES AROUND FEATURES

11 CREATE NOT JUST ONE BUFFER, BUT MULTIPLE BUFFERS.

12 COMPUTE THE DISTANCE FROM ONE FEATURE TO THE NEAREST FEATURE

13 COMPUTE THE DISTANCES TO ALL FEATURES

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15 AGGREGATE AND DISSOLVE FEATURES

16 PART II: COMMON RASTER PROCESSING; MOSAIC MULTIPLE RASTERS TOGETHER

17 CLIP RASTER BY EXTENT OF A RECTANGLE OR CIRCLE

18 CHANGE RASTER CELL SIZE AND RESAMPLE THE CELLS

19 GET CELL VALUES OF PARTICULAR CELLS

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21 A VERY HANDY TOOL TO USE

22 OR SET THE MASK IN SPATIAL ANALYST S OPTIONS AND THEN CLICK EVALUATE IN RASTER CALCULATOR TO CLIP THE RASTER

23 EXTRACT THE CELL VALUE OF A RASTER AT WHICH A POINT IS LOCATED

24 GET THE CELL VALUES OF A RASTER AT THE LOCATIONS OF ANOTHER RASTER/FEATURE

25 REDUCE CELL SIZE BY AGGREGATING PIXELS

26 REPLACE THE CELL (AND VALUE) BASED ON THE CONDITION OF NEIGHBORING CELLS

27 ASSIGN NEW RASTER VALUES USING RECLASSIFICATION

28 CALCULATE STATISTICS OF MULTIPLE RASTERS

29 GET THE FREQUENCY OF WHEN A RASTER IS LESS, EQUAL, OR GREATER THAN ANOTHER RASTER S CELL VALUES

30 CALCULATE THE AREA OF A ASTER/FEATURE WHEN IT FALLS INTO EACH ZONE OF ANOTHER RASTER/FEATURE

31 CALCULATE THE GEOMETRY OF A RASTER IN EACH ZONE

32 DATA PREPROCESSING FOR THE MERAUKE PROJECT Ophelia Wang, Department of Geography and the Environment, University of Texas

33 DATA REQUIREMENTS 1. All data layers must share the same spatial extent, total number of cells, cell size, and cell ID 2. Within the oil concession zone--- Cell size = 100 m 166,028 cells

34 GENERATING AN ANALYSIS MASK Mask: a habitat layer derived from classifications of land cover and flooded areas, and maps of geology groups and topographic position index

35 In ArcGIS: Convert the habitat raster to point shapefile each point has an ID convert the points back to raster using the IDs the raster value of each cell represents cell ID use as mask

36 LIST OF OTHER LAYERS Production suitability 2-km buffers around each village 500-m buffers around each sacred site or cultural site Rarity Hydrological-wetland connectivity Community-assigned habitat usage scores (six community layers)

37 PRODUCTION SUITABILITY (5 OR 10 CLASSES) Suitability map on a gradient of across the broader Merauke landscape Regrouped to 5 suitability classes for areas within the concession boundary; 5= high, 1=low (Map source: Conservation International)

38 VILLAGES WITH 2-KM BUFFERS Village point shapefile create a 2-km buffer around each point convert buffers to raster use the cell ID mask raster calculator: con(isnull[buffer], 0, [buffer]) (same process to create sacred + cultural 500-m buffer layer)

39 SACRED + CULTURAL SITE BUFFERS NOT OVERLAPPING WITH VILLAGE BUFFERS Overlapped area between village buffers and sacred + cultural buffers: [village] & [sac+cul] calulation con([calculation]==1, 0, [sac+cul]) sacred + cultural site buffers not overlapping with village buffers

40 RARITY AND HYDROLOGICAL CONNECTIVITY LAYERS (MASKED USING SPATIAL ANALYST )

41 COMMUNITY-ASSIGNED SCORES FOR HABITAT TYPES BASED ON USAGE Four important habitat types to use for ConsNet: Flooded savannas, savannas, dry forest, and open land/grassland Reclassify in ArcGIS: remain the codes for the above four habitats and assign other habitats to 0 (done for six community layers)

42 COMMUNITY BOUNDARY LAYERS Use the community score layers for habitats con([score]>0, 1, [score]) Six community boundary layers Four scores remained

43 OTHER POTENTIAL LAYERS Sago (a type of resource use) site buffers not overlapping with village buffers Community boundary Distance between the centroid of each cell to the nearest village, sacred, or cultural site (Convert the cell ID points to raster using the distance as value, each cell value = distance

44 Estimating soil erosion rate based on soil, slope, hydrology, and land cover A = R * K * LS * C * P A = estimated average soil loss in tons per acre per year R = rainfall-runoff erosivity factor K = soil erodibility factor L = slope length factor S = slope steepness factor C = cover-management factor P = support practice factor

45 MODEL BUILDER EXAMPLE: SOIL EROSION

46 FLOW ACCUMULATION LAYER DERIVED FROM FLOW DIRECTION LAYER

47 MAKING SLOPE LAYER FROM SURFACE ANALYSIS TOOL

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51 The results indicates the areas prone to soil erosion based on the calculations. On the layer erosion, the darker areas (areas of higher values) are most likely to suffer from future soil erosion.