Draft 3/18/2017URPL-GP.4651 Spring 2017 Syllabus
URPL-GP.4651: Advanced GIS: Topics in Spatial Analysis
Spring 2017 Syllabus
New York University
Wagner Graduate School of Public Service
Instructor Schedule
Sean Capperis Mondays, 3/27 – 5/8/2017, 4:55-6:35 p.m.
194 Mercer Street, Room 304
Office Hours: by appointment
Prerequisites
URPL-GP.2618, CUSP-GX.3007, or equivalent required.
Course Description
Sophisticated analysis of spatial data in geographic information systems (GIS) has become increasingly important to understand emerging issues and support decision-making in urban planning and policy. GIS software facilitates our ability to answer questions about spatial relationships: presence, absence, access, exposure, composition, and connectivity. This course will examine the concepts and tools used to measure conditions and manipulate data by spatial relationship. Together we will cover the modifiable areal unit problem, aggregation, neighborhood definition, and polygon apportionment; data types, visualization, and geoprocessing in three dimensions; spatial distributions and cluster identification; and the network data model and network analysis. We will explore each set of concepts through specific applications in urban policy and planning with practical, problem-solving approaches. We will cover a variety of domains: neighborhood indicators, demographic and socioeconomic conditions, land use regulation, environment, resilience, public safety, and transportation. Lab exercises and assignments will focus on current issues in New York City and reinforce visualization and data management practices introduced in earlier classes. The course will culminate in a student-defined, portfolio-quality final project.
Objectives
By the end of this course, you will be able to:
· Understand various spatial relationship concepts, as well as how and when to apply them
· Identify and account for common methodological challenges using GIS in urban applications
· Critique and defend the selection of spatial analysis methods and spatial relationship models
· Use the following new techniques and tools effectively: neighborhood definition, polygon apportionment, 3D visualization in ArcScene, 3D geoprocessing tools, hot spot analysis, Network Analyst, and Model Builder
· Further develop skills in cartography, data management, and spatial problem solving; further use spatial evidence to make a sound argument about an issue relevant to planning/policy
· Gain more exposure to commonly used spatial data in New York and other U.S. cities
Class Format and Preparation
Each class period will include a lecture that summarizes key concepts, lab exercises that demonstrate new GIS tools, and discussion. Our time in class is very limited, and so completing assigned pre-class reading will help us make the most of lecture and instructor-led labs. Out-of-class assignments are designed to reinforce content we explore in class and introduce new tools or potential applications. You may need to consult additional online documentation to complete an out of class assignment.
Required Materials
No textbook is required for this course.
We will be using Esri ArcGIS Desktop for all in-class instruction and exercises, and many out-of-class assignments will require it as well. You can use ArcGIS in NYU computer labs or install it on your personal computer, but it requires Windows. (Special note for Mac users: you can run ArcGIS in Bootcamp or in a virtual environment like Parallels. Parallels is easier to use, but may not perform as well as Bootcamp). You may obtain a free educational license for ArcGIS from the NYU Data Services website. When installing and using ArcGIS, request the “ArcGIS for Desktop Advanced” product and ensure that the extensions 3D Analyst, Network Analyst, and Spatial Analyst are installed.
You will need to maintain a data library that we will use for weekly in-class exercises. Please have a USB drive (16 GB minimum and formatted as NTFS or exFAT) or cloud storage available for every class session. I will send more details on how to build your data library prior to our first class meeting.
Grading and Assignments
Final grades will be weighted based on the following components:
Component / Weight / DeadlineClass participation / 5% / n/a
Assignment 1 / 15% / April 10
Assignments – choose two / 15% each
Assignment 2 / April 17
Assignment 3 / April 24
Assignment 4 / May 1
Final presentation / 5% / May 7
Final project / 45% / May 8
Deadlines and submission: All assignments are due by the start of class on the specified deadline date. Except in case of emergency, I will not accept late assignments, because we will discuss the solution in class. Students should submit all assignments electronically via NYU Classes.
Software: Some analysis techniques that we will cover can only be done in Esri ArcGIS (e.g., network analysis). However, for out of class assignments, if you are more comfortable using other packages like QGIS for common GIS functionality (e.g., data processing, cartography), you are welcome to do so.
Class participation: Because this is an advanced class, and there is no authoritative course text for us to depend on, it can be tricky to learn GIS software without the opportunity for guidance and discussion with your classmates and with me. Thus, it is critical that you come to class prepared to fully participate in lecture and lab exercises. Each class session contributes one percentage point to your final grade. Because some schedule conflicts are unavoidable (illness, family care, capstone client meetings, etc.) I will permit one absence without penalty. If you attend class on time, complete requested pre-class assignments (beyond graded assignments), and participate to the best of your ability in discussions and labs, you can expect to receive full credit. It is perfectly understandable if you fall far enough behind in a lab exercise that you cannot catch up; if so, you can receive full participation credit as long as you work together or follow along with your neighbor.
Graded assignments: Students must complete three graded assignments. I have designed the graded assignments to test your understanding of concepts and tools introduced in class, and to prepare you for the final course project. The first graded assignment, covering sessions one and two, is required of all students. For the second and third graded assignments, students can choose to complete two assignments of three possible assignments covering sessions three, four, and five. All graded assignments will require a memorandum and some combination of display maps and/or output data files.
Ungraded assignments: I will occasionally assign ungraded assignments to be done outside of class. These can serve two purposes. The first is to prepare data for lab exercises, so that we can focus our class time on new tools and discussion of methods and results. They can also serve as practice for new techniques as well as a low-stakes assessment of your understanding.
Final project: You will propose and complete a final project posing and answering an urban planning/policy question using spatial analysis techniques. Students will first submit a proposal to the instructor by the start of session 5. Similar to graded assignments, the final project deliverable will consist of display maps/data files as well as a memo setting up a research question, describing the GIS methodology (including data sources, tools, and any other methods), and discussing the results in an urban planning/policy context. Students will present their final projects during the final class session. Further final project specifications will be shared in class.
Grading criteria: I will score graded assignments using the following criteria and their respective weights. Each criterion receives a score of 0-4: advanced understanding of course objectives, 4; competent work, showing basic understanding of concepts, 3; inadequate, showing significant flaws or gaps in understanding of material, 2; incomplete or ungradable, 0. A score of 3 or higher indicates understanding of course objectives and expectations.
· Methods (50%): Use of methods and tools demonstrate understanding of the underlying concepts, principles, and application of tools to the situation. The student designed a process appropriate to the challenge or question, selected the right tool for the task, sequenced tools properly and efficiently, entered the correct parameters, selected the correct input data, and output the correct results (both spatial and numerical).
· Display map design (25%): The maps tell a clear story and present an appropriate amount of spatial context. The layers shown in the maps support the narrative in the written deliverable. Symbology, extent, layer order, and hierarchy are appropriate to the features in the map. Please refer to map style guide for expectations.
· Written content (25%): Written materials deliver a clear, coherent, well-structured argument, and directly address the question posed in the assignment prompt. The student uses appropriate evidence (visual and numerical) to support their conclusions. Methods are described clearly and accurately. The tone is appropriate for the audience.
Optional Resource Text
If you need a refresher on basic GIS techniques, especially as they are employed in urban planning and policy contexts, I recommend the following optional text: GIS Tutorial 1: Basic Workbook, 10.3 Edition by Wilpen Gorr and Kristen Kurland.
Academic Integrity
Unless otherwise specified, all assignment deliverables must be new, independent work. You are not permitted to share data or models that you create for assignments/projects with your colleagues, use data created by your peers in an assignment/project, or turn in the work of someone else, including data, maps, and text, as your own. Additionally, you may not submit any work for this class that you created/completed for other classes; this would count as self-plagiarism. In your memos, if necessary, please properly cite text and ideas of others as well as yourself (again, failing to cite yourself would be self-plagiarism). However, I do permit and encourage you to consult your peers and outside resources for advice using GIS techniques and data. If you have any questions about academic integrity as it applies to this course, please contact me. For more information, please refer to NYU Wagner’s academic code.
Course Schedule
Note: Readings and pre-class assignments are subject to change.
March 27 – Session 1
Advanced polygon aggregation – Demographic/socioeconomic data as neighborhood indicators
Concepts / ToolsTobler's First Law
Modifiable areal unit problem (MAUP)
Ecological fallacy / Select by location (review)
Summary statistics (review)
Dissolve (review)
Spatial join (review)
Required readings:
· Lloyd, Christopher D. 2010. “Key Concepts 3: Spatial Data Analysis.” Spatial Data Analysis: An Introduction for GIS Users. Oxford: Oxford University Press. 43-64. [ebook link; focus on sections 4.6, 4.9, and 4.10; also focus on concepts rather than math]
· Koschinsky, Julia. 2014. “Beyond Mapping: Spatial Analytics and Evaluation of Place-Based Programs.” In Strengthening Communities With Neighborhood Data, edited by G. Thomas Kingsley, Claudia J. Coulton, and Kathryn L. S. Pettit. Washington, DC: Urban Institute. [ebook link; note: section “Spatial Data, Methods, and Tools for Evaluation” is optional]
Optional readings:
· Kingsley, G. Thomas, Claudia J. Coulton, Kathryn L. S. Pettit. 2014. “Advances in Analytic Methods for Neighborhood Data.” In Strengthening Communities With Neighborhood Data. [link]
· Van Ham, Maarten, and David Manley. 2012. “Neighbourhood Effects Research at a Crossraods. Ten Challenges for Future Research.” Environment and Planning A 44: 2787-2793. [link; focus on challenges section, and challenges 7-8 in particular]
In-class exercise: Advanced aggregation – redefining concentrated poverty
Due: Pre-class assignment
April 3 – Session 2
Polygon apportionment – Demographic/socioeconomic data in custom study areas
Concepts / ToolsPycnophlactic principle
Apportionment algorithms
Continued discussion of concepts from session 1 / Intersect (review)
Field calculator (review)
Calculate geometry (review)
Required readings:
· Beale, Laurie. Apportioning Population Between Areas. 2012. (presented at Esri International User Conference) [link]
· NYU Furman Center. 2015. “Focus on Density.” State of New York City’s Housing and Neighborhoods in 2014. [link] – focus on section “4. Population density in New York City is associated with certain positive neighborhood amenities and largely unrelated to many negative attributes,” pp. 18-20
In-class exercise: Demographics in the Greenpoint-Williamsburg rezoning
April 10 – Session 3
Visualization and geoprocessing in 3D – Environmental impact
Concepts / ToolsData types in 3D (multipatch, raster digital elevation model)
Representing height / ArcScene
Sun shadow volume
Intersect 3D
Layer 3D to feature class
Multipatch footprint
Time
Required readings:
· Kennedy, Heather. 2010. Introduction to 3D Data: Modeling with ArcGIS 3D Analyst and Google Earth. Hoboken: John Wiley & Sons. 1-11, 27-32, 52-57, 62-77. [ebook link through NYU ebrary; do not complete the exercises]
· Esri. 3D Analyst.
o Sun Shadow Volume [link]
o Intersect 3D [link]
o Multipatch Footprint [link]
Optional readings:
· City Planning Commission, City of New York. 2012. NYU Core Final Environmental Impact Statement. “Chapter 6 – Shadows.” [link]
· Esri. 3D Analyst.
o Essential 3D Analyst vocabulary [link]
o Understanding feature-based heights in 3D [link]
o Multipatches [link]
o Using extrusion as 3D symbology [link] -- read all subtopics
o 3D polygon features [link]
· Kennedy 2010. [read pages 169-178]
In-class exercise: Shadows over Central Park
Due: Assignment 1
April 17 – Session 4
Clustering – Public safety incidents
Concepts / ToolsSpatial distribution
Spaitial heterogeneity
Spatial autocorrelation
MAUP (revisited) / Average nearest neighbor index (review)
Geary’s c
Create fishnet
Hot spot analysis (Getis-Ord Gi*)
Required readings:
· Lloyd, Christopher D. 2010. “Key Concepts 3: Spatial Data Analysis.” Spatial Data Analysis: An Introduction for GIS Users. Oxford: Oxford University Press. 43-64. [link; focus on sections 4.7 and 4.8, review section 4.9]
· Esri. ArcMap [documentation].
o How Average Nearest Neighbor works [link]
o How Hot Spot Analysis (Getis-Ord Gi*) works [link; “Calculation” section is optional]
o How Optimized Hot Spot Analysis works [link]
· Review Koschinsky 2014 [session 1].
Optional readings:
· McGlone, Daniel. 2014. “Analyzing Philadelphia Crash Data.” Azavea Atlas. [link]
· Eck, John E., Spencer Chainey, James G. Cameron, Michael Leitner, Ronald Wilson. 2005. Mapping Crime: Understanding Hot Spots. Washington, DC: U.S. Department of Justice. [link; see chapters 1 and 2]
In-class exercise: Detecting crime clusters