SJSU GIS Day
Geographic Information System (GIS) software is available for faculty, staff and students at SJSU. Join us for SJSU GIS Day where we will review available resources and discuss new features, showcase faculty and student research projects that use GIS applications, and make connections to build our GIS Community.
2019 SJSU GIS Day took place on November 13 from 11:00 a.m. to 1:00 p.m. in the Library Room 255 and online.
Download the SJSU GIS Day event flyer.
11:00 a.m. Welcome and Introduction to the Day
Jennifer Redd, eCampus Director
11:10 a.m. Introductory Web Mapping Activity
Richard Kos, Urban and Regional Planning Faculty Member
11:20 a.m. What's new with ArcGIS?
Amadea Azerki, ESRI Solution Engineer
Student Lightning Talks
11:40 a.m. GIS and Ballot Dropoff Box Locations
Judi Heher, Geography
11:48 a.m. Clustering Analysis Crime Hot Spot Detection in Chicago
Ai-linh Alten, Computer Science
11:56 a.m. The Crookedest Railroad in the World: Unearthing the route of the Mount Tamalpais and Muir Woods Railway
Nick Frey, Urban & Regional Planning
12:04 p.m. Proposed Urban Growth Boundary in Winter Park and Fraser, Colorado
Ryan Forster, Urban & Regional Planning
12:12 p.m. How Active is Downtown, San Jose? Active Zones Present within 5, 10, 15 Minutes of
Walk Buffer from Downtown, San Jose’s Light Rail Stations
Roomin Parikh, Urban & Regional Planning
12:20 p.m. Analyzing Homelessness in California Using GIS
Kristin Bergwitz, Zachary Carlson, and Elham Chopan, Economics
SJSU GIS Program Information
12:30 p.m. SJSU GIS-Connected Courses and Programs
Kerry Rohrmeier, Geography Faculty Member
Sharing Professional Experiences
12:40 p.m. ArcGIS Web Apps Demonstration: Weed Abatement and More
Jay Van Biljouw, Geographic Systems Specialist - City of San Jose (SJSU Alumnus)
1:00 p.m. Event Concludes
Big Data visualization poses many challenging limitations in GIS because of the large quantity and variety of data types. In this presentation, you will learn about Big Data, Google’s Kaggle and BigQuery, a machine learning clustering algorithm, and using a web map API like Mapbox for visualization. This project focuses to apply a Big Data analysis solution on a real-world problem for determining crime hot spots in Chicago.
The presentation examines homelessness data using GIS files provided by the US Department of Housing and Urban Development (HUD). The analysis focuses on the causes of homelessness including income, income inequality, housing prices, climate factors, and other community characteristics.
GIS and Ballot Dropoff Box Locations
Short discussion of how GIS is used to integrate the state criteria in determining the locations of ballot dropoff boxes for the 2020 election.