Your current filters are…
Datums! Coordinate systems! Map projections! Topologies! Spatial applications are a nebulous, daunting concept to most Pythonistas. This talk is a gentle introduction into the concepts, terminology and tools to demystify the world of the world.
This talk will have multiple parts:
by Zain Memon
Python makes it easy to store, query, and transform geodata. We will run through a handful of useful GIS libraries and patterns that let you do magical things with your maps. If you want to make maps that are more interactive and more interesting, this talk is for you.
This talk will demystify the different parts of a usual map stack, including:
GeoSpatial Datastores (RDBMS & NoSQL)
Map servers (that query the geodata)
Tile servers (that chunk the data into tiles and cache it)
Exploring and analyzing data can be daunting and time-consuming, even for data lovers. Python can make the process fun and exciting. We will present techniques of data analysis, along with python tools that help you explore and map data. Our talk includes examples that show how python libraries such as csvkit, matplotlib, scipy, networkx and pysal can help you dig into and make sense of your data.
Learn about powerful python libraries for analyzing all types of data, including spatial data, through the following illustrated examples.
Example 1: Explore data
Problem: I have a large voter data file in CSV format. I want to examine it, check the column headings and data types, and do some basic stats, but I don’t want to pull it into Excel or Access. What are my options?
Solution: csvkit - I can explore my data, chop it up, sort it, summarize it, and prepare it for import to postgis.
Bonus: Developers and journalists have been working hard to add functionality to csvkit. You can contribute!
Example 2: Analyze data
Problem: I have a bunch of data points from Twitter. How do I make sense of what I have in front of me, and where do I start?
Solutions: matplotlib, networkx
Bonus: Learn about how python libraries are plug and play with each other.
Example 3: Map data
Problem: I have a year’s worth of crime incidents for a large city. I want to explore global and local patterns in the data and identify clusters.
Solutions: PySal (Numpy, Scipy)
Bonus: We’ll look at the full ESDA (Exploratory Spatial Data Analysis) module in PySal, and we’ll briefly touch on a selection of the rest of PySal’s functionality.
To wrap up the talk, we'll give some tips on using postgis and geodjango to go from data analysis and mapping to building a web application.
by Paul Smith
Spatial data are often seen as opaque to most developers, and while dealing with them does require a shift in approach from the data types we most regularly handle, they needn’t be the domain of specialists. High-quality Python libraries and Python-based applications exist for operating on and transforming spatial data, and for creating visualizations, including maps for presentation on the web.
This talk will be an overview of the Python libraries and applications available for handling spatial and geospatial data and creating maps for the web. It will cover libraries for open and transforming spatial data formats and representations, spatial operators and predicates for queries and relationships, spatial indexes for efficient queries, and compositing and rendering map tiles, as well as desktop applications extensible with Python that replace much of the functionality of "enterprise" GIS software.
7th–15th March 2012