UAP 5114, Fall 2019
Computer Application in Planning |
Spatial Data Analytics and Visualization

Virginia Tech, College of Arhictecture and Urban Studies | School of Urban Affairs & Planning

3:00 - 5:00 PM, Architecture Annex Room 1, Friday
Instructor: Wenwen Zhang

Overview

We are in the era of big data, with 2.5 exabytes - that's 2.5 billion gigabytes (GB) - of data generated every day. Planners are expected to be equipped with quantitative analysis skill sets and data visualization techniques to take advantage of urban big data to address/monitor/measure complex urban problems. This class offers an introduction to spatial analytics to planners, with a focus on structured urban data acquisition and processing. Additionally, the class also provides fundamentals regarding GIS model builder to automatcially process large spatial dataset. The course also emphasizes on data visualization and communication skillsets to enhance planners public engagement skills in the age of big data. By the end of the semester, students should be familiar with adapting GIS tools to obtain and process spatial data, develop GIS model builder, and effectively visualize model outputs and communicate to local stakeholders.

Office Hours

Wenwen Zhang @ Architecture Annex, Room 215
Wed 12:15 - 1:00 PM or by Appointment

Class Objective

The objective of this course is to familiarize students with modern spatial data acquisition, processing, and GIS model builder techniques. The course also demonstrates how these tools can be applied in real-world context. The course is practice oriented, i.e., concepts and models are motivated and illustrated by application to urban problems and datasets. By the end of the semester, the students will be able to:
  1. Basic concepts in GIS data
  2. Cleaning, (spatial) query, and edit attirbute data
  3. Perform basic spatial analysis using GIS tools
  4. Develop GIS model builder
  5. Visualize model outputs

Course Procedure and Organization

In the course, students will work on a REAL-WORLD PROJECT: Developing infographics for rural health. Studnets are expected to get familiar with the project and develope a series of infographics describing rural health using spatial and tabulated data
The course is organized in a series of lectures, hands-on exercises, and readings. The lectures and readings are designed to allow students to have basic knowledge of spatial data, statistics analysis, GIS model buidler, and implementations. The exercise session will provide students with hands-on experience using different types of GIS tools, such as mapping sptial data, data quiry, spatial joining, geoprocessing, raster analysis, and geodatabase development. During the implementation process, students will learn the skills to apply the GIS tools to analyze different types of spatial data, evaluate results, and visualize/interpret spatial analysis results. Students will use Google Classroom to submit assignments and discuss questions on class materials and homework. The class materials and schedules are published on the Google Classroom website.

The challenge in learning spatial analytics lies in absorbing a conceptual understanding of different data acquisition and processing techniques with GIS training in implementation. Therefore, every attempt has been made wherever possible, to provide a “hands-on” exercise application of the concepts demonstrated in the previous lecture sessions. Additionally, case-studies (i.e., Rural Health Infographics Design for this semester) in homework assignment will provide examples of the data and models learned in class may be applied in real-world practices, in data preparation, and in problem-solving, to bridge the gap between academics and technicalities.

Readings and Materials

ArcGIS
Visualization
Readings will be posted to Google Classroom. Students are expected to finish readings before coming to classes.

Class Schedule (Tentative)

Date Topic Reading & Lecture In-class Exercise Reminders
Aug. 30 * Welcome and Overview
Spatial Data Analytics and Visualization Thinking
GIS Chapter 1 & 2
Functional Art Section I
Get Familiar with GIS data
 
Sept. 06 * Intro. 2 GIS
Guest Speaker Beth O'Connor from Virginia Rural Health Association [Class Client]
Spatial References
Projections and & Coordinate Systems
GIS Chapter 3
Assigning and Correcting Projection and Coordinate Systems HW 1 OUT
13 * Cartography Mapping Data
Cartography
GIS Chapter 4 & 5
Mapping GIS data HW 2 OUT
HW 1 DUE, Sept. 13th, 2019, 11:59 PM
20 * Tabular Data
Guest Speaker Ph.D. Student Tianjun (Luke) Lu
Tabular Data
Attritube Queries
GIS Chapter 6 & 8
Tabular Data Queries HW 3 OUT
HW 2 DUE, Sept. 20th, 2019, 11:59 PM
Oct. 27 Spatial Joining Spatial Joining
GIS Chapter 9
Spatial Query and Joining HW 4 OUT
HW 3 DUE, Oct. 27th, 11:59 PM
Oct. 4 Fall Break | No Class Fall Break | No Class Fall Break | No Class HW 4 DUE, Oct. 4th, 11:59 PM
11 * Geoprocessing / Overlays Geoprocessing Tools
GIS Chapter 10
Geoprocessing (Model Builder) using Rural Health Data HW 5 OUT
18 * Raster Data Raster Data Concepts and Analysis Tools
Final Project - Introduction to Infographics Design
GIS Chapter 11
Functional Art Section II
Raster Data Processing Final Project - Introduction to Infographics Design HW 6 OUT
HW 5 DUE, Oct. 18th, 11:59 PM
25 * Final Project Group Exercise ACSP | NO LECTURE
Group Meeting to Discuss Rural Health Infographics Design
Final Project - Rural Health Infographics Template Design HW 6 DUE, Oct. 25th, 11:59 PM
Nov. 1 * Final Project Studio Final Project - Rural Health Infographics Template Revision based on Client's Feedbacks Final Project - Rural Health Infographics Template Revision in Power Point
8 * Final Project Studio Final Project - Rural Health Infographics Template Content Populating Final Project - Rural Health Infographics Template Content Mapping using GIS
15 * Final Project Studio Final Project - Rural Health Infographics Template Content Populating Final Project - Rural Health Infographics Template Content Mapping using GIS
22 * Interactive Visualization Introduction to Tableau Rural Health Dashboard Making
29 Thanks Giving | No Class Thanks Giving | No Class Thanks Giving | No Class
Dec. 06 * Final Project Deliverables Finalization Tentative In-class Presentation for Final Project None
10 * Project Wrapping Up (Final Project Due)

Course Requirements and Grading

Homework Assignments [60%]

The BEST way to get help with homework assignments is to post your questions to Piazza. (Please kindly review the posted questions for potential answers) If you prefer that keep your questions to only the TA and the instructor, you may use the private post feature (i.e., check the "Individual Students(s) / Instructors(s)" radio box).
Teams of 3-4 should be formed at the beginning of the semester. Most assignments are team projects. VT students MUST observe the Graduate honor code manual.
We plan to have SIX individual assignments (Check Schedule for Due time):
  • HW1 [10%]: Spatial References and CPS
  • HW2 [10%]: Cartography
  • HW3 [10%]: Data Query
  • HW4 [10%]: Spatial Joining
  • HW5 [10%]: Geoprocessing
  • HW6 [10%]: Raster Analysis
The detailed grading criteria will be provided in assignment descriptions. Students will have approxiatmely 1 week to finish the assignment. The in-class exercise sessions will demonstrate how to use/adjust Python Scripts to finish the assignment!

Class Project [30%]

The objective of the final project is to develope a series of INFOGRAPHICS on different aspects of rural health for the Virginia Rural Health Association. . The teams are expected to use the second half of the semester to develop (1) infographics template, (2) visualize rural health data, (3) populate the designed infographic templates.


Participation [10%]

Hint Piazza does track students' contributions
Attendance, participation in class discussions, and out of class discussions (on Piazza), and motivation may affect your grade and could potentially influence any borderline grade. Roll will be called in class.

Final Evaluation

The breakdown of final grades:
  • A : [94,100]
  • A- : [90, 94)
  • B+ : [87, 90)
  • B : [83, 87)
  • B- : [80, 83)
  • C+ : [77, 80)
  • C : [73, 77)
  • C- : [70, 73)
  • D : [60, 70)
  • F : [0 , 60)

Late Submissions Policy

Class Accessibility

Any student with a disability requiring accommodations in this course is encouraged to contact me after class or during office hours. In addition, students should contact the Services for Students with Disabilities for Academic Success.

Finally, a couple of house keeping things

  1. Check the class website frequently to see any changes, updates, tips, and announcements.
  2. When you submit your final work, please include all your works into one PDF file, following file naming convention like this: ‘HW1_Lastname.pdf’, ‘major1_Lastname.pdf”, ‘Exam_Lastname.pdf’. Word and Excel NOT allowed for submission
  3. When you have questions, please e-mail me with a heading of “[UAP5114]” in the subject.

Welcome to the world of Planning Analytics and Visualization and good luck!