Metadata-Version: 1.1
Name: pandana
Version: 0.2.0
Summary: Pandas Network Analysis - dataframes of network queries, quickly
Home-page: https://udst.github.io/pandana/
Author: Autodesk
Author-email: UNKNOWN
License: AGPL
Description: Pandana
        =======
        
        .. image:: https://travis-ci.org/UDST/pandana.svg?branch=master
           :alt: Build Status
           :target: https://travis-ci.org/UDST/pandana
        
        .. image:: https://coveralls.io/repos/UDST/pandana/badge.svg?branch=master&service=github
           :alt: Coverage Status
           :target: https://coveralls.io/r/UDST/pandana
        
        A nice slideshow showing example code is available
        `here <http://bit.ly/1tivyjw>`__.
        
        In this case, a picture is worth a thousand words. The image below shows
        the distance to the *2nd* nearest restaurant (rendered by matplotlib)
        for the city of San Francisco. With only a few lines of code, you can
        grab a network from OpenStreetMap, take the restaurants that users of
        OpenStreetMap have recorded, and in about half a second of compute time
        you can get back a Pandas Series of node\_ids and computed values of
        various measures of access to destinations on the street network.
        
        .. figure:: https://raw.githubusercontent.com/udst/pandana/master/docs/img/distance_to_restaurants.png
           :alt: Distance to Restaurants
           :width: 800
        
           Distance to Restaurants
        
        Beyond simple access to destination queries, this library also
        implements more general aggregations along the street network (or any
        network). For a given region, this produces hundreds of thousands of
        overlapping buffer queries (still performed in less than a second) that
        can be used to characterize the local neighborhood around each street
        intersection. The result can then be mapped, or assigned to parcel and
        building records, or used in statistical models as we commonly do with
        `UrbanSim <https://github.com/udst/urbansim>`__. This is in stark
        contrast to the arbitrary non-overlapping geographies ubiquitous in GIS.
        Although there are advantages to the GIS approach, we think network
        queries are a more accurate representation of how people interact with
        their environment.
        
        We look forward to creative uses of a general library like this - please
        let us know when you think you have a great use case with the hashtag
        ``#udst``.
        
        Docs
        ----
        
        `Documentation <http://udst.github.io/pandana>`__ for Pandana is
        now available.
        
        Thorough `API
        documentation <http://udst.github.io/pandana/network.html>`__ for
        Pandana is also available.
        
        Acknowledgments
        ---------------
        
        None of this would be possible without the help of Dennis Luxen (now at
        MapBox) and his OSRM (https://github.com/DennisOSRM/Project-OSRM). Thank
        you Dennis!
        
        Nearest neighbor queries are performed with the fastest k-d tree around,
        i.e. ANN (http://www.cs.umd.edu/~mount/ANN/).
        
        Academic Literature
        -------------------
        
        I'm currently working on getting a `complete description of the
        methodology <https://github.com/fscottfoti/dissertation/blob/master/networks/Foti%20and%20Waddell%20-%20Accessibility%20Framework.pdf?raw=true>`__
        published in an academic journal. Please cite this paper when referring
        to the methodology implemented by this library.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 2.7
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
