Assignment4 (1)

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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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4

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Assignment 4 | Creating Attribute Data [Add your name here] Objective: In this assignment, you will implement the following: Working with external data in .CSV or excel file format to perform table joins Store spatial data and attribute data in geodatabase and/or in external shapefiles (spatial data) Understand and use correct data types (long integers, short integers, float, double, text) to store various types of data without truncating values, rows, columns, field/variable and naming rules, key fields, joining rules Create attribute data (quantitative and qualitative) using manual data entry and field calculations Use python scripts to compute field values Create technically correct maps (correctly populated data, classifications , use use correct map types, colors, symbols and visual elements) with visual hierarchy Analyze and describe the algorithm (methodology) Technical writing (answering technical questions) S ubmit a single word document with the map and the answers to all the questions. For this assignment, you will need to explain the steps you took from beginning until the end to create the map. Make sure to keep screen captures of each step and write down what steps you took. This process is similar to what you see in the lab activity videos. The goal is for you to be able to explain the process to someone so that they can replicate your work using the steps you provided. Q1: Mapping County Data (30 Points) In this question you will join the data, export it to a shapefile and then using a field calculator, you will write a formula using python code to compute population density. Q1.1: Create a Map [8 Pts] 1. Add the Selected_IL_Counties.shp and SchoolEntollment_2013.xls to ArcGIS Pro. If the SchoolEntollment_2013.xls doesn’t work, use the SchoolEntollment_2013.csv file instead. 2. Examine both tables, identify the files to join. Once identified, join the fields. 3. After joining it, export the data into a new shapefile and save it to the geodatabase (.gdb). Provide a suitable name for the new shapefile. 4. Add only the new shapefile to ArcGIS Pro. 5. Open the attribute table for the new shapefile, create a new field called “SchEnDen” with data type as Double. Save the changes you have made. 6. Using the field calculator, compute School Enrollment Density. The formula to compue this is: (note: you will have to translate this formula to match the actual fields in your table) School Enrollment Density = Scholl Enrollement / Sq. Miles ____________________________________________________________________________________ © Nandhini Gulasingam 1
7. Create a map to should the School Enrollment Density for 2013. Source: U.S. Census 2013. Add all map elements. Label the map using County names. Q1.2: Answer the following questions 1. [1 pt] Which county has the highest density? 2. [1 pt] Which county has the lowest density? Q1.3 : Technical Questions: 1. [1 pt] Which fields from both tables did you use to join? 2. [2 pts] What was the reason to use these two fields? (Hint: explain using joining rules discussed in the lectures) 3. [1 pt] Include the screen capture of the newly exported shapefile table. It should include the newly computed field. 4. [1 pt] How many rows are in the newly exported shapefile table (or the exported shapefile table)? 5. [1 pt] How many columns are in the newly exported shapefile table? 6. [2 pts] Explain why we used the data type ‘Double’ to compute and store the new variable value “School Enrollment Density” (SchEnDen)? Could have used any other data type? 7. [12 pts] Explain the methodology - Explain step-by-step on how you created this map from downloading and extracting the shapefiles to completing your final map (For example, the first step is to unzip the compressed shapefiles and save it into a folder, and add the shapefiles to ArcGIS application) . Include screen captures while you explain each step. For each step, include a line indicating the purpose of the step (For example, extracting the shapefile was to uncompress the spatial data so that the GIS application can read and display the file). 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, but it is 25% of the points: a workflow you devised led ____________________________________________________________________________________ © Nandhini Gulasingam 2
problems to correct results and products you created (such as maps) help solve a problem at hand. 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. 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 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. Explain the purpose of each step MCD Writing Expectations Writing Includes: a. Technical writing (Q1.3) ____________________________________________________________________________________ © Nandhini Gulasingam 3
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