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GEO 266: GIS ANALYSIS MIDTERM EXAM – Due May 3, 2011 (before class) Instructions This exam consists of multiple parts that will be completed both in-class and outside of class. The entire exam is ‘open book’ meaning you can use your textbook, on-line resources (i.e. ESRI help), or any resources EXCEPT your peers. This is an individual assignment and all answers must be adequately cited or put into your own words. To submit your midterm, email all responses and outputs in one document (.pdf) to the instructor. PART I Identify the transformation tool used for each illustration. The gray shaded areas indicate polygons in the dataset. Possible choices are (not all are present): Intersect, Union, Clip, Erase, Symmetrical Difference, Identity, Dissolve, Buffer, Near, Simplify, Centroid, Spatial Join, Select. National Forests Ecoregions Ecoregions 1. ____________________ Cities Highways Cities have Attribute of Distance to Closest Highway 2. ____________________ 3. ____________________ 4. ____________________ Counties Cities Counties with “summarized” City Attributes 5. ____________________ PART II LiDAR Data: Based on Josh McLaughlin’s presentation in class answer the following question. 1. Explain one thing about the LiDAR data acquisition / processing that you find most interesting. PART III You will create a map of Heritage Trees in Portland, by Neighborhood. Instructions 1. Download the ‘Heritage Trees’ dataset from the CivicApps website (www.civicapps.org). Unzip the shapefile and add it to an empty map. 2. Go to the T: drive on the classroom computers, and in the GISdata folder you will find the RLIS data from Metro. Copy the Neighborhood (nbo_hood) shapefile on your flash drive or import into a geodatabase, and add to your map document. 3. Create a multivariate (multiple variables) map that shows the frequency (count) and average height of trees within each neighborhood on the same map. You can do this by copying the layer you are interested in mapping and pasting it into the same data frame. This gives you two layers to symbolize at the same time. Include all relevant map elements. 4. Explain what tools/process you use to get the frequency & average height, by Neighborhood. PART IV Problem Statement: You are interested in understanding the spatial distribution of salmon in the Hoh River watershed on the western Olympic Peninsula of Washington state. To do this, you will create maps that show high salmon diversity, salmon rivers at risk based on development & logging, and level of protection for salmon rivers. Datasets: Download the Sp11Midterm_Data from the course website containing the following datasets: - Salmon distribution datasets: chinook_distr, chum_distr, coho_distr, sockeye_distr (Note: although they look like lines, each of these datasets is actually a five-foot buffer polygon around a section of the watercourse) - Road datasets: clallam_roads, jeffereson_roads - Land ownership: land_ownership - Study Area Boundary: study_area - Land Use / Land Cover data: WesternWA_LULC Deliverables: You need to turn in a written description of what tool(s) you use and the steps you take to answer each question (steps 1,2,4-8). Include any maps or graphics that you think will enhance your descriptions. Getting started: Start by downloading the files and importing into a geodatabase (you will need to create a blank geodatabase – call it salmon). Start ArcMap, open a new empty map, set your ‘Salmon’ geodatabase as the default geodatabase for the map. Add all the datasets to your new blank map. Use the following analysis techniques/tools (not necessarily in this order): Clip, Buffer, Export, Select by attribute, Merge, Feature to Line, Intersect, Union 1. First you will need to prepare your datasets for analysis. Create one unified feature class from the two separate road layers. 2. Next extract all your datasets to the study area boundary into your geodatabase. 3. Since you are working within a geodatabase, your feature classes should already have an area and length field included. Check your attribute tables to make sure this is the case. 4. Next, you want to create a new dataset that shows areas of high salmon diversity. This will include the watercourses that contain all four salmon species (where the four datasets overlap). Call this new feature class, ‘high_salmon_diversity.’ This will be difficult to see on your map – convert to a line feature class. 5. You also need to have a layer that shows the combined range of all salmon species. Create a single dataset that shows all the watercourses where any of the salmon species can be found. Call this new feature class, ‘salmon_range.’ This will be difficult to see on your map – convert to a line feature class. 6. Now you want to find which sections of the salmon ranges are most at risk because they are adjacent to logged areas, developed areas (consider agriculture to be a developed area), or roads. In the WesternWA_LULC layer, the field “PRIM” provides land use/land cover codes: > Everything in the 200’s are developed areas > Everything in the 300’s are agriculture > Everything in the 610’s are logged areas Export these areas into a new feature class and call it ‘developed_logged’ 7. To find out which areas are ‘at-risk’, use the ‘salmon_range’ dataset to find salmon rivers within 300’ of a logged, developed, or agricultural area salmon rivers within 300’ of a road 8. Finally, find out what level of protection exists for salmon in the study area. Use the Land Ownership layer with the following assumptions: > Olympic National Park = highest level of protection > Olympic National Forest/Native American Reservations/Spokane District = moderate protection > private land = low protection In the ownership layer, if the field “AGENCY_NM” is blank, the land is privately owned. 9. Create a map of both the “high salmon diversity” and “salmon range” you created in steps 4 and 5. Be sure to include all of the standard map elements (title, legend, scale bar, etc.). Export the map as a .jpg and paste it in midterm document. 10. Create a map of the “at-risk” salmon ranges you created in step 7. Be sure to include all of the standard map elements (title, legend, scale bar, etc.). Export the map as a .jpg and paste it in midterm document. 11. Create a map that shows the level of protection from step 8. Be sure to include all of the standard map elements (title, legend, scale bar, etc.). Export the map as a .jpg and paste it in midterm document.
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