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Two Presentations, One Session! Using PostgreSQL to manage ArcGIS spatial data The objective of this presentation is to show the effective use of PostgreSQL to manage Geographic Information System data (hereby referred to as GIS). The bulk of the data for Mecklenburg County, NC is edited and created with the use of ESRI‘s ArcGIS software. However, in the recent years the county has made a bold switch from SQL server to PostgreSQL for the backend database. This was a very challenging decision since we were one of the first in the country to make this switch. This was evident three years ago; during the ESRI International User Conference when there were only six people attending the session titled “Database Administration with PostgreSQL”. There was next to little documentation or help available either online or over the phone. Mecklenburg County had decided to maintain the production GIS databases with the help of ESRI’s SDE and PostgreSQL. Most of the data we receive comes from SQL server. The ETL packages which were in SSIS had to be rebuilt in Kettle. Charlotte is the largest metro within Mecklenburg County with a population of over a million. The GIS unit within the county has been one of the pioneers in using and implementing GIS since the beginning of the 1990s. In spite of being very open to new ideas, this kind of transition was met with a little apprehension since many of our internet applications get over a million hits. Every upgrade to ArcGIS and PostgreSQL is met with a further challenge, since often all jobs/scripts and ETL packages need to rewritten. This is due to a compatibility issue with the versions of ArcGIS. Many of the PostgreSQL Tools available in the open source market cannot be used since they do not work with the ArcGIS software. This paper and presentation will outline the different formats in which GIS data is obtained and the different methods in which they are made usable to the public. The special focus will be on the python scripts, PG SQL scripts, and ETL packages done using Spoon and Kitchen and PG Admin. This presentation will demonstrate a very different use of the PostgreSQL database and also how efficient it is. --------------- Taking PostgreSQL and Analytics to the Next Level with Python Through PostgreSQL’s support for user-defined procedural language functions, the possibilities for data analysis within a database is greatly expanded. Specifically, using PL/Python, one can bring in countless Python libraries to process data close to the database. Here I will talk about my efforts to bring in the functionality of PySAL, a spatial analytics library written in Python and developed largely by Serge Rey, et al. at Arizona State University. PySAL makes available robust exploratory spatial data analysis related to spatial cluster and outlier detection, hotspot detection, spatial regression, and much more. Besides the integrations we created for PySAL, we have written classes for bringing in machine learning methods such as random forest, linear regression, support vector machines,

Date:

Duration:

30 min

Room:

Conference:

PGConf US 2016 [PgConf.US]

Language:

Track:

Use Cases

Difficulty:

Medium