# Let’s pretty it up! - PowerPoint PPT Presentation

Let’s pretty it up!

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Let’s pretty it up!

## Let’s pretty it up!

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##### Presentation Transcript

1. Let’s pretty it up!

2. Border around project area Big circles… and semi-transparent Color distinction is clear Everything else is hardly noticeable… but it’s there

3. Spatial Analysis

4. Spatial Analysis • Spatial analysis refers to the formal techniques to conduct analysis using their topological, geometric, or geographic properties. • In a narrower sense, spatial analysis is the process of analyzing geographic data. • Types of spatial analysis you have already been doing: • Buffering • Select by location

5. Layers can be overlaid - placed one over the other based on a shared geographic reference – allows analysis of the relationships between layers

6. Raster analysis is one method of Spatial Analysis.

7. Raster Data • A matrix of cells • Rows and columns (grid) • Examples: aerial photographs, digital photos, scanned maps • Examples in spatial analyst…

8. Vector vs Raster

9. Raster Data • Why do we have to use raster data? • Vector data (points, lines and polygons) are limited to only certain spatial analyses • Point in polygon (which points are within • Line intersections • Vector data only knows about the space it occupies • Raster data covers the entire region • Provides a more powerful format for advanced spatial and statistical analysis

10. The grid data structure • Grid size is defined by extent, spacing and no data value information • Number of rows, number of column • Cell sizes (X and Y) • Top, left , bottom and right coordinates • Grid values • Real (floating decimal point) • Integer (may have associated attribute table)

11. Definition of a Grid Cell size Number of rows NODATA cell (X,Y) Number of Columns

12. Value attribute table for categorical (integer) grid data

13. So now that I know what a raster is… what can I do with it? • derive new information from your existing data, • analyze spatial relationships, • build spatial models, and • perform complex raster operations.

14. Applications of spatial analysis • Find suitable locations • Model and visualize crime patterns • Analyze transportation corridors • Perform land use analysis • Conduct risk assessments • Predict fire risk • Determine erosion potential • Determine pollution levels • Perform crop yield analysis

15. How to find “suitable” locations • Step 1: State the Problem • Find the most suitable location for a new long-term care facility in Long Beach • Step 2: Identify the Parameters and Weight • Supply: needs to be far from existing facilities (weighted by number of beds in the facilities) (25%) • Demand: number of persons over 65 (50%) • Access: close to major streets (25%) • Step 3: Prepare Your Input Datasets • Long Beach Facilities (point) • Census Tracts – Age>65 (polygon) • Major Streets (line) • Step 4: Perform the Analysis

16. ArcGIS Workflow • ArcCatalog: • Make sure all your layers are in the same projection (eg: UTM Zone 11N) • ArcMap: • Load all your layers, double check that you are on the right projection and units (eg: miles) • Turn on Spatial Analyst Toolbar • Set the Environment (very important, ensures that raster layers have the same cell size!) • Load your indicator layers • “Rasterize” your layers (Ex: Kernel Density, Feature to Raster, Euclidean Distance) • Reclassify • Apply weights • Generate final raster

17. Step 1 Ensure that each layer in your project has the SAME projection

18. Step 2 Check the map units *Even if you change the “display” units, spatial analysis will be conducted using the “map” units

19. Right click Step 3 Access ArcToolboxEnvironments Or… From the file menu: Geoprocessing, Environments

20. Step 4 Set the environment Processing ExtentUsually set this to the extent of your project, or the largest layer Raster AnalysisCell size and Mask

21. Step 5 Diagram your work flow

22. Best site for new facility 25% 50% 25% Far from existing facilities Close to areas with high numbers of senior citizens Close to major streets Kernel Density on Number of Beds Feature to Raster on Age>65 Euclidean Distance Long Beach Facilities Long Beach Census Tracts Long Beach Major Roads

23. Step 6 Do the analysis

24. Example: Kernel Density Spatial Analysis Tools > Density > Kernel Density

25. Example: Euclidean Distance Spatial Analysis Tools > Distance > Euclidean Distance

26. Example: Feature to Raster Conversion Tools > To Raster > Feature to Raster

27. Kernel Density on Number of Beds 3 3 3 Reclassify: 3 most desirable 1 = least desirable 1 3 2 1 1 2 Long Beach Facilities Feature to Raster on Age>65 Reclassify: 3 most desirable 1 = least desirable 1 1 3 1 2 3 2 2 3 Long Beach Census Tracts 2 1 3 Euclidean Distance Reclassify: 3 most desirable 1 = least desirable 2 1 1 2 2 1 Long Beach Major Roads Spatial Analysis Tools > Reclass > Reclassify

28. 3 3 3 25% .75 .75 .75 Long Beach Facilities 1 3 2 .25 .75 .5 1 1 2 .25 .25 .5 1 1 3 .5 .5 1.5 50% Long Beach Census Tracts 1 2 3 .5 1 1.5 2 2 3 1 1 1.5 2 1 3 .5 .25 .75 25% Long Beach Major Roads 2 1 1 .5 .25 .25 2 2 1 .5 .5 .25 Spatial Analysis Tools > Map Algebra > Raster Calculator 1.75 1.5 3 1.25 2 2.25 1.75 1.75 2.25