FOSS4G NA 2015 is going on this week in the Bay Area, and so far, it has been a great conference.

Monday had a great line-up of tutorials (including mine on PySAL and Rasterio), and yesterday was full of inspiring talks.  Highlights of my day: PostGIS Feature Frenzy, a new geoprocessing Python package called PyGeoprocessing, just released last Thurs(!) from our colleagues down at Stanford who work on the Natural Capital Project, and a very interesting talk about AppGeo's history and future of integrating open source geospatial solutions into their business applications. 

The talk by Michael Terner from AppGeo echoed my own ideas about tool development (one that is also shared by many others including ESRI) that open source, closed source and commercial ventures are not mutually exclusive and can often be leveraged in one project to maximize the benefits that each brings.

In fact, at the end of my talk yesterday on Spatial Data Analysis in Python, someone had a great comment related to this: "Everytime I start a project, I always wonder if this is going to be the one where I stay in Python all the way through..."  He encouraged me to be honest about that reality and also about how Python is not always the easiest or best option.

Similarly, in his talk about the history and future of PostGIS features, Paul Ramsey from CartoDB also reflected on how PostGIS is really great for geoprocessing because it leverages the benefits of database functionality (SQL, spatial querying, indexing) but that it is not so strong at spatial data analysis that requires mathematical operations like interpolation, spatial auto-correleation, etc. He ended by saying that he is interested in expanding those capabilities but the reality is that there are so many other tools that already do that.  PostGIS may never be as good at mathematical functions as those other options, and why should we expect one tool to be great at everything?  I completely agree.

ESRI's Developer Summit in Palm Springs is taking place this week. It is a much smaller, more tightly focused event than the User Conference which takes place later in the summer. While the UC covers everything ESRI has to offer, the Dev Summit is all about developing applications within the ArcGIS environment.

I am fortunate enough to be attending this year; here are some highlights from Day One:

The opening plenary session was jam-packed with updates to the ArcGIS platform, some recently implemented and others still in development. One of the most interesting from my point of view is the new Smart Mapping feature built into ArcGIS Online. Smart Mapping is a simple, yet powerful data symbolization tool which presents the user with a set of "smart" defaults driven by analysis of the data loaded into the map. Users with little to no experience in cartographic design can load a dataset into the map and then easily apply styling to that data to tell the story that they want. Heat maps, attribute based transparencies and continuous color ramps with variable range sliders are all available with just a click of the mouse. This is another step in the devlopment of ArcGIS Online to make it painless for anyone to create rich, meaningful maps.

Another great new technology showcased today are vector tile maps. Unlike tradition raster-based map tiles, vector tiles are subdivided based on data density rather than a fixed grid overlay. This allows for much smaller tile sets with near instantaneous draw speeds. Watching one of the ESRI devs pan and zoom around a vector tile based webmap and seeing almost no lag in data refresh was really incredible. ESRI plans on relaseing this technology progressively over the course of 2015.

One of the more interesting aspects of the conference so far was the way ESRI seem to be pushing ArcGIS Pro. They're still maintaining that it's not going to replace the traditional ArcGIS Desktop environment (which is scheduled for a new release at the end of the year) but the fact that they're pushing it so hard and that certain features (such as the aforementioned vector tile maps) are only available through Pro lead me to believe the traditional desktop environment is going to be slowly phased out.

All in all, this was a great start to the conference. I can't wait to see what else is in store for the next couple of days. Stay tuned!

Discovering the World Through GIS

November 19, 2014, 5PM-8:30PM

UC Berkeley, Mulford Hall

It's GIS Day 2014! Come visit Mulford Hall Wednesday Nov 19th at 5pm-ish to participate in workshops, listen to talks, see posters, and chat with other like-minded GIS-enthusiasts.

See the agenda here: http://gif.berkeley.edu/gisday.html.

We look forward to seeing you!

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).