LabActivity8

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DePaul University *

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241

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Computer Science

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Jun 11, 2024

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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
None of these correspond to the same location, if you load one, you may have to click on the shapefile layer under contents (left hand side) and then select “Zoom to Layer”. Experiment with a few different ones. Use option-1 or option-2. Option-1: Pick any one you like and create a map. Complete the maps with all map elements. OR Option-2: Try what we added during the lab: Try some of the files we added in the lab video. Please note that you may not find the same files, if that is the case, revert to option-1. The goal is for you to learn how to look for data on ArcGIS Online and add the data to create a map, so you can pick anything that is available. Complete the map. 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. Part D: Attribute Queries (Select by Attribute) [20 Points] For this activity, we will use the following shapefiles. We will write queries to see how many schools satisfy the conditions stated below. For each question, you will include a screen print of the GUI (graphical user interphase), the SQL (Query), the answer and a couple of lines explaining what the command does as discussed in the video and shown below. Exercise: (1) [ 5 pts] How many schools are in Austin Community? Field Name OPERATOR Value _________________________________________________________________________________ © Nandhini Gulasingam 3
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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
(2) [4 pts] How many Communities have at least one CTA Station? (3) [4 pts] How many big parks are within 1/2 mile from a CTA station? Part F: Buffer Analysis [8 Points] For this activity, we will use the following shapefiles. Exercise: (1) [7 pts] Create ½ mile buffer around big parks. Complete the map with all the map elements. Source: Chicago Data Portal, 2020. Include the map in a separate page. (2) [1 pt] Compare the answer you provided under Part-E (3) with the number of CTA stations that fall under the buffer you created. Does it match? If it doesn’t match, you will have to redo Part-E (3) again. Rubric: Meet expectation In progress of reaching the benchmark Needs improvement 1. Comply with guidelines 100% of the points : the report/map are written/made clearly and complies with the guidelines. 80% of the points: the report/map are not written/made clearly while following the guidelines over 80% OR the report/map are written/made clearly following the guidelines only 60-90%. 50% or less of the points: the report/map follows the guidelines less than 50% regardless of clarity. 2. Use computational thinking (CT) * concepts and skills 100% of the points : clear demonstration that you correctly used CRS as CT concepts and skills. 70% of the points: vague demonstration that you used CRS correctly as CT concepts and skills. 25% of the points: you incorrectly used CRS as CT concepts and skills 3. Analyze, describe Algorithm ** 100% of the points : clear demonstration that you correctly used the terminologies, included relevant screen captures and explained all the steps 70% of the points: vague demonstration that you explained the steps or missed critical steps in explain the process of creating the map. 25% of the points: you incorrectly explained or missed most of the steps in explaining how the map was created step-by-step. 4. Design solutions to 100% of the points : a workflow you devised led 70% of the points: a workflow you devised led to correct results, 25% of the points: a workflow you devised _________________________________________________________________________________ © Nandhini Gulasingam 6
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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