LabActivity8

Lab Activity 8 | Advanced Spatial Analysis Objective: In this lab activity, you will implement the following:  Define a problem, formulate and solve the problem using data and map visualizations  Interpolation to compute unknown values from known values  Writing SQL queries (attribute queries) spatial queries (selection), and performing buffer analysis  Identifying and extracting external data from various data sources (geodatabases, ArcGIS online data repository) to provide a mappable solution  Working with vector data models, geodatabases, data types (spatial and no-spatial data, quantitative and qualitative data)  Overlay multiple shapefiles to visualize data  Create technically correct maps (correctly populated data, use correct map types, colors, symbols and visual elements) with visual hierarchy  Analyze, describe, and apply the algorithm (methodology)  Technical writing (answering technical questions) S ubmit a single word document with the map and the answers to all the questions. Part A: Interpolation [8 Points] (1) Create a folder called LabActivity_8 on your flash drive or hard drive. (2) Download the LabActivity8.zip file into this folder. (3) Unzip the LabActivity8.zip file into the folder LabActivity_8 you created. (4) Open ArcGIS Pro, navigate to LabActivity_8 folder. Add the shapefiles "CCA_Boundary.shp" and "PublicSchools_2000.shp". (5) Make the CCA_Boundary shapefile layer hollow (no fill color, only a thick outline). Then make the PublicSchools_2000 shapfile layer to about 2 points (very small). (6) Click on “Analysis” tab on top menu. Click on “Analysis Gallery” tool. (7) Select “Empirical Bayesian Kriging” under the “Analyze Patterns” section. (8) You will see the “Empirical Bayesian Kriging” pane load on the right hand side. Enter the following values:  Input Features  PublicSchools_2000 shapefile  Z-Value  ENROLLEMNT  Output geospatial layer  Type Enrollment_Interpolation  Output raster  Type Enrollment_Interpolation Do not click RUN Yet. Next, we have to set the “Environments” to ensure the processing extent and masking is set. Click on “Environments” on the right hand side pane.  Scroll down until you see “Processing Extent”. Select “CCA_Bounday” from the drop down.  For the “Mask” select CCA_Bounday from the drop down. Click “Run” button. It will take a few minutes to run. (9) Deselect (uncheck) the lower layer (2 nd layer” named “Enrollment_Interpolation” under Contents. _________________________________________________________________________________ © Nandhini Gulasingam 1

(10) Complete the map. Overlay schools shapefile on top of the interpolated layer. Make sure that the map is technically correct with all the necessary map elements and variables/shapefiles relabeled. Include the completed map in the word document. Data source is Chicago Public Schools, 2000. Part B: Working with Geodatabase [8 Points] (1) Download the Housing_Schools.zip file into your current lab activity folder. (2) Unzip the Housing_Schools.zip file into your current lab activity folder. (3) Create a new map within your existing project (or start a new project). i.e. got “Insert” tab, select “New Map”. (4) Then click “Add” and then “Data” to add the data (similar to adding shapefiles). Navigate to the folder you have unzipped the files. See screen below. Double click on “Housing_Schools” folder. (5) Now you will see the “Housing_Schools.gdb” like the one below. Highlight it and click “Open”. (6) You will see 4 shapefiles. Add all 4 shapefiles. (7) Using the “Community_Housing” shapefile layer, create a map to show population in poverty (“In_Poverty”). Change the symbols/colors for the reaming 3 school shaplefile layers. (8) Overlay the shapefile layers. Complete the map, add all map elements. Data source is “GIS Class Data, 2021”. Make sure that the map is technically correct with all the necessary map elements and variables/shapefiles relabeled. Include the completed map in the word document. Data source is “GIS Class Data, 2021”. Part C: Add data from ArcGIS Online [8 Points] (1) Create a new map within your existing project (or start a new project). i.e. got “Insert” tab, select “New Map”. (2) Then click “Add” and then “Data” to add the data (similar to adding shapefiles). You will see the following window. It does not matter what is visible on the right hand side. From the left hand side click “ArcGIS Online” You will see data files load into that window. If you want to look for more click on the “Find more items…” link at the bottom of that window. It will load more data files to this window. _________________________________________________________________________________ © Nandhini Gulasingam 2

ANS: GUI : SQL: COMMUNITY = 'AUSTIN' ANS: 21 Schools Explanation of what it does : The query is issued from the public schools attribute table. The query allows us to select all the public schools that have COMMUNITY = AUSTIN, i.e. all schools that have the COMMUNITY field value equal to AUSTIN. (2) [ 5 pts] How many schools are Charter Schools? (3) [ 5 pts] How many schools have enrollment greater than 500? (4) [ 5 pts] How many schools have percent Hispanics is greater than 20%? Part E: Spatial Queries (Select by Location): [12 Points] For this activity, we will use the following shapefiles. _________________________________________________________________________________ © Nandhini Gulasingam 4

We will perform the selection (spatial query) to answer the following questions. For each question, you will include a screen print of the your window with the 3 input parameters (information), the answer and a couple of lines explaining what the command does as discussed in the video and shown below. Exercise: (1) [4 pts] How many Communities (CCAboundary) have at least one Park (Big_Parks)? Input Features: Result  shapefile  CCAboundary Relationship: Method  at least one – intersect Intersect Within the distance of Completely contains Selecting Features: With respect to which shapefile  shapefile  Big_Parks Selection Type: “New selection” ANS: Screen capture of the input parameters: Answer: 27 communities have big parks Explanation of what it does : The selection is issued between the CCAboundary.shp and Big_Parks.shp files. The selection allows us to select all the communities that have at least 1 big park. _________________________________________________________________________________ © Nandhini Gulasingam 5

problems to correct results and products you created (such as maps) help solve a problem at hand. but it is unclear that products you created (such as maps) help solve a problem at hand OR products you created could have helped solve a problem if a workflow you devised had led to correct results. led to incorrect results OR products you created (such as maps) does not help solve a problem at hand. MCD Learning Outcome-1: Computational thinking* Computational Thinking (CT) refers to abilities to formulate and solve problems in a way that can be computationally carried out . Most recurring CT concepts include: Note: Parts of MCD Learning Outcome represented in blue and underlined is evaluated in this assignment. 1. Decomposition (i.e. breaking down into smaller, manageable parts) Examples: Examples of decomposition is breaking down the main problem into smaller, manageable parts . For example, ‘creating a map’ includes, extracting and adding the spatial data, manipulating the spatial data to visually represent the data and add the visual elements to be able to read the data in map format by both technical and non-technical folks . 2. Abstraction (i.e., reducing a complex problem or reality to essential components) Examples: Examples of abstraction as it relates to GIS, are datum for unambiguously referencing locations on the Earth’s surface, map projection for portraying geographic features on the ground in the 2D surface, data model for representing spatial and non-spatial aspects of the reality ( e.g., vector, raster, table ), and visual variables (e.g., color hue, size, color value, shape) for cartographically representing attributes of geographic features on the Earth’s surface . 3. Algorithms (i.e., a step-by-step procedure to solve a problem) Examples: Examples of algorithm is breaking down the problem smaller steps and following them step- by-step as in creating the map from extracting the data to adding the data, manipulating the spatial data to visually represent information and create the final map with map elements . It also includes use of operations such as data classifications , selection and query , table join, overlay , interpolation , buffer , etc. or use of programming concepts like conditional statement or loop to control workflow. 4. Programming Concepts (i.e. using data, functions, conditions, loops, formulas, etc.). Examples: Examples of programming concepts includes using different types of data ( spatial , attributes – quantitative and qualitative, and pixel-based raster data), working with databases , classifications , computations using formulas, functions, conditional statements , and loops for manipulating data, representing information or control workflow. MCD Learning Outcome-2: Analyze/Describe Algorithm** Analyze/Describe algorithm refers to abilities to develop, express, trace, and analyze algorithms. Algorithms include decomposing the problem into step-by-step method and explaining each step and its purpose. Note: Parts of MCD Learning Outcome represented in blue and underlined is evaluated in this assignment. 1. Explain step-by-step process / methodology with screen capture so that someone else can follow the same process and replicate the map 2. Analyze and explain the algorithm (See Parts D and E) 3. Explain the purpose of each step (See Parts D and E) MCD Writing Expectations _________________________________________________________________________________ © Nandhini Gulasingam 7

Writing Includes: a. Technical writing (Parts D, E, F) _________________________________________________________________________________ © Nandhini Gulasingam 8