GEO 266. GIS ANALYSIS

Winter Term 2013

MIDTERM EXAM

Due. Monday Feb 18, 2013 (before class, 5:59 pm)

Instructions

This exam consists of multiple parts that can be completed both in-class and outside of class. The entire exam is “open book”, meaning that you can use any resource provided so far (e.g. textbook, lecture notes, ESRI Help documentation) EXCEPT YOUR PEERS. This is an individual assignment and all answers must be adequately cited or put into your own words. Email your completed midterm to the instructor. Include all responses and outputs in a single PDF document.

PART I

Feature Templates

Creating features using feature templates is a key to editing in ArcGIS.

  1. Why are feature templates useful?
  2. What information dothey store?
  3. Can you have multiple templates associated with a map layer? Provide an example.

GIS Pioneers

Several GIS Pioneers, i.e. game-changers within the GIS field, are featured here:

  1. Choose your favorite GIS Pioneer and describe how they have influenced the development of the science or industry of GIS.

Raster Analysis

Explain the following raster analysis concepts.

  1. What does it mean to resample a dataset?
  2. List the possible different ways to resample a raster dataset with a brief description.
  3. Explain what happens when you reclassify a dataset.
  4. Provide an example of when you would use one of the Surface Analysis tools.

PART II

Fill in the blanks to the following questions.

If the question represents a geoprocessing tool, identify the ArcGIS tool illustrated in the question. The gray shaded areas indicate the presence of polygon features in a dataset. Possible choices are (not all are present): Append, Buffer, Clip, Dissolve, Erase, Feature to Point, Identity, Intersect, Merge, Near, Select, Simplify, Spatial Join, Union.

  1. Determine the values for the raster at the right by adding the other two raster datasets. “N” means “no data”.
  1. ______

New output dataset created

  1. ______
  1. Reclass the values for the raster at the right. “N” means “no data”.

< 0 = 1

0 - 1.5= 2

1.5 - 3.5 = 3

3.5 – 10 = 4

Counties Cities Counties with “summarized” City attributes

  1. ______

PART III

You will create a multi-variate map of Heritage Trees in Portland, by Neighborhood.

  • Download and unzip the Midtermdata file from the course website.
  • In the “Part3” folder, find the Heritage Trees shapefile (Heritage_Trees_pdx.shp) and the Neighborhood shapefile (nbo_hood.shp) and add to your map document.
  • Be sure to save your map often, and keep a copy of all data and MXD files on your flash drive.
  • A multi-variate (multiple variables) map shows qualitative/quantitative values from more than one dataset on the same map.
  1. Create a multi-variate map showing both the frequency (count) and average height (in feet) of treeswithin each neighborhood. To include both variables on the same map, copy the layer you are interested in mapping and paste it into the same data frame–you will have two layers to symbolize together. Include all relevant basemap layers and map elements.
  1. Describethe process you used to get the tree frequency & average height, by Neighborhood. Include a workflow diagram for the datasets and analytical tools.

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: If you haven’t already done so, download and unzip the Midterm data file from the course website. You will be using the following datasets from the “Part4” folder:

-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, jefferson_roads

-Land ownership: land_ownership

-Study Area Boundary: study_area

-Land Use / Land Cover data: WesternWA_LULC

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. Keep track of all process steps, tools, parameters, and input/output datasets involved in your analysis.

  1. First you will need to prepare your datasets for analysis. Create one unified feature class from the two separate road layers.
  1. Next extract all your datasets to the study area boundary into your geodatabase.
  1. 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.
  1. 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.
  1. 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.
  1. 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’

  1. To find out which areas are ‘at-risk’, use the ‘salmon_range’ dataset to find
  2. salmon rivers within 300’ of a logged, developed, or agricultural area
  3. salmon rivers within 300’ of a road
  1. 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.

  1. 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.
  1. 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.
  1. 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.
  1. Describe the process you used to complete the analysis. Include a workflow diagram for the datasets and analytical tools.