Patty Frontiera from the D-Lab went to the Geospatial Computational Social Sciences conference at Stanford on Monday 10/20 (https://css-center.stanford.edu/geospatial-computational-social-science-conference).

Here is her summary:

1. GIS for Exploratory Data Analysis

• The presentations showed that geospatial analysis and mapping using desktop GIS, R and python are an extremely important part of exploratory data analyses in the social sciences.

2. GIS for Communication / Visualization

• Digital maps and web maps are an important part of communicating the results of scientific analysis. However effective communication with any kind of graphic / visualization tool may require additional funding for design professionals which researchers typically are not.

3. Garbage in Garbage out - or good computational tools don't replace good thinking.

• Ed Chi a research scientist at Google gave a great talk on the analysis of implicit location data in twitter content. One point he made is that there are great tools for parsing and analyzing these data but bad data can creep in when the tools are used without thoughtful consideration of the data inputs and outputs. For example, just because someone entered "Donkey-Kong, Texas" as their home town and a geocoder parsed that and returned a valid coordinate pair does not mean that that town exists. He would be a great speaker to get at Berkeley.

4. Uses of GIS in Social Science

• A panel discussed the uses of GIS in social science research. The key points they made were:
  • GIS is an important tool for linking social and environmental data.
  • GIS is important for exploring data at different geographic and temporal scales.
  • The use of GIS in social science research requires and benefits from an interdisciplinary approach.

5. Academia-Industry Collaboration

• There were three industry speakers, one each from Facebook, Google, and EBay. They discussed collaboration with academia, making the following points:
  • Because it takes so long to establish a working relationship and because a tremendous amount of effort goes into creating data sets that can be made available for social science research, universities should work on developing long term relationships with industry rather than come ask for data for one-off projects.
  • Academia should participate in the development of open standards for space-time geospatial data formats.
  • Academia should not insist on overly restrictive licensing terms.
  • Industry likes collaborating with social scientists (as opposed to computer scientists) because they have different goals from industry and thus it is more of a mutually beneficial rather than competitive relationship.

6. Social Science Research Needs with regard to Geospatial computational Analysis:

1. Social scientists need training in the following areas: GIS, R, Python, SQL, data cleaning, geospatial data file formats, and computing infrastructure.
2. Data reuse and research replication: because of how long it takes to obtain and clean data, social scientists need infrastructure to facilitate data sharing and reuse.
3. Academia needs to recognize the value of data intensive social science though the use of alt metrics. Stanford Press just received a Mellon grant to explore alt metrics movement.

7. Social scientist..data scientist..computer scientist?

• There was a heated discussion on whether or not a social scientist needs to become a computer scientist and what the nature of the relationship should be between these two fields.  This was a really good discussion which may be worth having at Berkeley.
  • Do social scientists need to become data scientists?
  • What level of computational training is enough?
  • Do computer scientists need social scientists too?
  • There is a tension in the disciplines of applied computer science (maker culture) and social sciences/humanities (idea culture).

Planet Mapping: The Science of 3D Maps. Find out what tools and techniques are enabling today’s modern cartographers to render 3D maps.

Location: swissnex San Francisco
730 Montgomery St., San Francisco, 94111

Our world is constantly being captured through GPS, cameras, satellites, and scanners and rendered by algorithms into navigable maps of Planet Earth. But how are 3D maps really made? How is the data collected?
Hear from some of the hottest startups in the field about the science and technology behind 3D map making—from data collection, to processing, to display—and discover how you can make your own 3D maps.
During the event, enjoy the visual stimulation of the PLACEMAKERS exhibit on view at swissnex San Francisco.

Program:

• 6:30 pm doors open
• 7:00 pm intro
• 7:10 pm talks + Q&A
• 8:45 pm networking reception

See more at: http://www.swissnexsanfrancisco.org/event/planetmapping/#sthash.G5iIInIJ.dpuf

IMAGE as LOCATION

Wednesday, October 22, 9:00 AM – 5:30 PM. Banatao Auditorium, 310 Sutardja Dai Hall, UC Berkeley

Tickets available online: http://www.eventbrite.com/e/image-as-location-conference-tickets-12860529189

When man-made images constitute the evidence of our environment and even our existence, how is our perception of the world manipulated and shaped?  The IMAGE as LOCATION conference brings together artists, technologists, and theorists to discuss how images define our understanding of our environment by allowing us to access the inaccessible. Beginning at the microscopic scale and moving through our human dimensions into planetary orbits, we will discover what it means to wrap our world in visual artifacts both from a cultural and public policy perspective.

Stanford’s Geospatial Computational Social Science Conference

Monday, October 20, 2014, 8:30 - 5:15, Mackenzie Room (#300), Huang Engineering Center, 475 Via Ortega, Stanford, CA 94305

Speakers join us from Airbnb, Facebook, Google, and more. 

• Learn more: https://css-center.stanford.edu/geospatial-computational-social-science-conference
• Lunch provided for all confirmed attendees. Reception to follow.

I spoke yesterday at the CEGA-DIME* co-sponsored event: Measuring Development: Energy & Environment.  This was a terrific day of interesting talks, thoughtful conversations and great networking.

This workshop brought together engineers, social scientists, donors, and practitioners to discuss the use of novel measurement tools--including sensors, sensor networks, microsatellite imaging, and other remote sensing technologies--in energy and environment research. 

I presented an "ignite" talk on some of our mapping work and talked about the idea of "Spatial Data Science". There were a number of highlights. Matt Hancher from Google gave a great overview of Google Earth Engine and asked: "What if the micro-satellite imagery revolution works. What will you do with the data?” Great and timely question. Big Spatial Data workshop to the rescue! We heard from people at the Energy Institute at Haas who are looking at smart sensors, iButtons and billing networks to understand energy usage around the world; Ronald Cohen from the Climate Readiness Institute spoke; there was tons of discussion about low earth orbit micro-satellites from Skybox and the Spire company (they monitor AIS beacons on ships yet they also can still find them as they move back and forth through fishing zones and turn off their beacons); there was a great idea from Tony Vodacek from RIT on the need to develop “a remote sensing playbook”: What are the sensors, resolutions, bands that are needed for a particular task?. David Lobell from Stanford highlighted some of his great work in remote sensing of crop yields; and Sol Hsiang from the Goldman School outlined his fascinating work on natural disasters, economies and violence.

Background: Technologies for measuring the adoption and impact of development interventions have seen substantial innovation over the past several years—examples include the use of microsatellite data for mapping weather patterns and agricultural yields, sensors for tracking behavior change, smart meters for recording real-time energy use, continuous emissions monitoring systems for measuring particulate matter, and platforms for smartphone- and tablet-based survey data collection. At the same time, network protocols for data management, visualization, and analysis have drastically improved.

*CEGA =UC Berkeley's Center for Effective Global Action; DIME = World Bank's Development Impact Evaluation Initiative