In lab group meetings we have been discussing the evolution and future of Spatial Data Science as a discipline.
Therefore when I recently stummbled upon a article about a reserarch project looking at the evolution of Geography based on a database of Doctoral Dissertation Titles, I couldn't help but be excited, and intrigued by the connection.
This reasearch from Kent University Professors David Kaplan and Jennifer Mapes is also reminiscent of Kelly Lab's own Shufei Lei's recent work analyzing and mapping textual data in the context of ecological systems and adapative managment!
Just like spatial data science, Geography as a discipline has struggled with defining its' complex identity, its principles and concepts spanning and borrowing from several established discipinces.
From the article:
"Geography is a relatively young discipline in terms of university academics, and for much of its history, geographers have struggled to define what exactly the discipline includes, said Keith Woodward, an assistant professor of geography at the University of Wisconsin at Madison.
So for a historical perspective, this type of database would be helpful looking at a timeline of how geographers defined their field, Woodward said."
The database is a compliation of 10,290 dissertations ranging back to the late 1800's with the goal of understanding the trends, concentrations, and expansion of Geography as a discipline over time.
Although currently unpublished (look out for an article in Geographical Review early 2015) there are a few preliminary findings and possibilites that sound immensly interesting:
"The study maps which universities have high percentages of dissertations focused on domestic or foreign regions, and also shifts in which regions of the world were popular topics for dissertations."
"A database of dissertations could provide a glimpse into what academics are interested in and how their focuses shifts as de-colonization and globalization occurs, Woodward said."
"Much of the focus so far has been on the words within the dissertation titles and how they’re used. Geographers today like to explain the field as a study of space and place, Mapes said. But those words didn’t become popular in dissertations until the 1960s."
Read the full article here and look out for an article in Geographical Review early 2015.
Workshop: HOW TO CREATE MAPS FOR EBOLA's FIRST RESPONDERS which are used by Doctors Without Borders, Red Cross, WHO and others. The workshop quickly shows how to easily use a humanitarian mapping freeware so anyone can contribute to this global mapping collaboration.
Easy‐to‐Learn and Then Map on Your Own Time
Volunteer now! Join contributors world‐wide and help create the maps which are being used in the fight against Ebola. Easy to make, these maps are for first responders including the World Health Organization, Red Cross, Doctors Without Borders and others.
Come and Learn how to map rooftops in cities and the paths and roadways to remote villages using Open Street Map and satellite images. First responders need your help today.
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You’ll learn about this global collaboration
- You’ll see how to use OpenStreetMap’s humanitarian mapping freeware which is quickly becoming the communication tool used by international first
responders
- And, you can share your ideas on how you can participate in this collaborative effort.
D‐LAB (356 Barrows Hall)
Thursday, October 30th, 1:00 – 2:00 p.m.
Friday, October 31st, 10:00 – 11:00 a.m.
Register: http://dlab.berkeley.edu/training
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:
- Social scientists need training in the following areas: GIS, R, Python, SQL, data cleaning, geospatial data file formats, and computing infrastructure.
- 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.
- 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
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
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
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
Technical Tidbits From Spatial Analysis & Data Science. This nice blog highlights many technical aspects of web mapping: Leaflet, D3, R... lots of neat examples.
