Archive for June, 2010

Free E-Learning Module from the ArcGIS 10 Version of our GIS Programming 101 Course

Posted on June 27, 2010. Filed under: Uncategorized |

With the impending release of ArcGIS 10 we have been hard at work updating our GIS Programming 101: Mastering Python for Geoprocessing in ArcGIS e-learning course.  The scheduled release date for this course is July 19th.  In the meantime feel free to sample one of the modules from this course.

The ArcPy.Mapping module is new to ArcGIS 10 and brings some really exciting features for mapping automation including the ability to manage map documents and layer files as well as the data within these files.  Support is also provided for automating map export and printing as well as the creation of PDF map books and publication of map documents to ArcGIS Server map services.

The lecture for this module can be found at:

Important Note about Data and Exercises
Note: The data and exercises will only work with ArcGIS 10 so unless you have a pre-release copy you’ll need to wait until the final release to complete the exercises. You will also want to create the following directory structure in Windows Explorer: C:\GeoSpatialTraining\ArcGIS 10\GIS Programming 101\Exercises

It’s not absolutely necessary to create this structure, but the exercises assume that it exists.  Just keep that in mind if you unzip elsewhere.

Exercise Instructions
There are 5 exercises for this module.  Exercise instructions:

Exercise Data

Exercise data:

We will be teaching an Internet based, instructor led version of this course beginning July 19th.  For more details on this course.

Here is a list of all the modules for the complete GIS Programming 101: Mastering Python for Geoprocessing in ArcGIS course

Module 1: Getting Started with Python in ArcGIS
Module 2: Basic Python Language Features
Module 3: Introduction to ArcPy
Module 4: Environment Settings
Module 5: Accessing Geoprocessing Tools and Functions
Module 6: Tool Results, Messaging, and Error Handling
Module 7: Using the ArcPy Mapping Automation Module
Module 8: Creating Lists of GIS Data
Module 9: Getting Descriptive Information about GIS Data
Module 10:  Using Cursor to Select, Edit, and Add Records to Tables and Feature Classes
Module 11: Miscellaneous ArcPy Class
Module 12: Licensing and Extensions
Module 13:  Geoprocessing History

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Crime and Student Test Performance

Posted on June 18, 2010. Filed under: Uncategorized |

Note: This is a guest post by Joe Ramsey and Jared Garza, two members of my Spring 2010 “GIS Applications” class at San Antonio College.

One of the courses required for an Associate of Applied Science Degree in GIS at San Antonio College is titled “GIS Applications”.  In this class, students typically pair up in teams at the start of the semester and submit a proposal for a GIS project to the instructor for approval.  Once approved, the student teams begin work on what is usually a semester-long project.

My team member, Jared Garza, and I decided to study community crime within public school districts and its influence, if any, on student test performance.  Following are the steps we took and the issues that arose while completing our project.

Acquiring the Crime Data
We were fortunate to have a contact within the City of San Antonio Police Department who supplied us with the necessary crime data.  The crime statistics (Year 2009) were already assembled into tabular format with geographic coordinates for the location of each crime, so geocoding the data was not required.  We simply imported the table into ArcMap, converting it to shapefile for further analysis.

After briefly analyzing the crime data, we discussed whether certain categories of crime (major offenses, minor offenses, etc.) should be or should not be used in the study.  We wondered if “major” crime would perhaps have more of an impact than “minor” crime.  This uncertainty led us to include all categories of crime in the study because we reasoned that the “presence” of crime was as equally relevant as the “seriousness” of the crime

Acquiring School District Boundary Data
Obtaining the school district boundary information was quite simple.  The boundaries were downloaded, in shapefile format, from the Texas Education Agency website (  We knew there were approximately twenty independent public school districts in the San Antonio area, so we directed our attention to these particular school districts.

A quick display of the crime data on top of the school district boundaries began to reveal a problem.  The geographic extent of the City of San Antonio (and its related crime data) varied greatly when compared to the school district boundaries (See Figure 1).

While nearly all of the school district boundaries “touched” the City of San Antonio, not all school districts were located entirely within the city limits.  Many school districts shared their boundaries with rural Bexar County for which crime data was not available at the time of our study.

As a result, we had to eliminate several school districts from our study.  We felt that because all crime data were obtained from the City of San Antonio, only school districts whose geographic boundaries are located predominantly within the city limits would be included in our study.

What About Small Towns?
As we dug deeper into the data, another issue arose.  A few of the school districts have boundaries that encompass very small incorporated towns for which crime data was not available at the time of our study (See Figure 2).

We discussed whether trying to track down crime data for approximately a dozen small communities was a viable option, considering the time constraints of a college semester and our lack of a “contact on the inside” of their law enforcement departments.  Also entering the discussion was the speculation that some of these small towns may not even maintain electronic databases of their crime data.  (That was speculation and was not verified).

So, on a different tack, we carefully compared (using ArcMap) the school districts’ larger geographical size against the much smaller towns.  In the end, we decided that because of the towns’ miniscule geographic extent, the unavailability of crime data from the small towns should have no significant impact on the study.

Which School Test Data to Use?
Once we knew specifically which school districts we were going to study, we began to focus on what test performance data to use.  Ultimately we used school test data taken from published results of the 2009 Texas Assessment of Knowledge and Skills (TAKS) test.  Again, this data was readily available on the TEA website.

The TAKS consists of six different subject-area tests, administered at different times throughout a student’s academic career (3rd grade through 11th grade).  In order to accurately compare each school districts’ test performance, results for individual subject-area tests were averaged across all grade levels to arrive at a final test performance value for each school district (See Figure 3 below).  This value represents the  percentage of students within each school district that met the test standard.

Analyzing the Data
With most of the data issues ironed out and our focus narrowed on the relevant school districts, we began to analyze the data.  Initially, we created a crime density surface and displayed the raster on top of the school district boundaries (See Figure 4).

This revealed the districts with crime “hotspots”, however, it was difficult to visually determine how the districts “ranked” against each other in terms of the extent of crime.  As a solution, we decided to normalize the crime data into number of crimes per square mile within each school district boundary.  Then we were able to categorize and symbolize the districts thematically, allowing us to visually rank the extent of crime among the districts (See Figure 5).

In order to similarly rank student test performance, we computed an average test performance value for the school districts as a whole.  This permitted us to compare and rank the individual districts against their average.  We also discussed comparing the individual districts against the region average and against the state average, but ultimately decided that the “area” average was perhaps a more accurate figure to use.  At any rate, we symbolized the districts using their relationship to the average, thereby ranking the districts on their student test performance (See Figure 6).

Pulling it All Together
With the two main data frames created, we began to assemble all the map elements into a coherent map document (See Figure 7).  The usual cartographic elements were included: title, scale, legends, data sources, etc.

We placed the crime map and the test performance map adjacent to each other so the users could visually compare the two maps and determine for themselves if there is a relationship between localized crime and its impact, if any, on student test performance.

Additional maps included on the map document are an inset map showing the Bexar County/ San Antonio region highlighted on a state map of Texas.  This was done simply for viewers of the map who may not be familiar with where the area of study is located.  We also inserted a small map of the crime density surface to help reinforce where the most extensive crime is observed. Other map elements include the crime data and test performance data in tabular format and a general notes section.

Any Surprises?
As we surmised before the project began, the data, in general, seems to show that the more extensive the crime is within a school district, the further below average that student test performance becomes.

However, we can only state “in general” because of the apparent anomaly concerning the Harlandale ISD.  Of the five school districts studied, Harlandale had the 2nd highest crime rate but had the 2nd best test performance.  A more involved study would perhaps determine the reasons for this apparent inconsistency.

Final Thoughts
Although the data seems to show a relationship between the presence of crime and school test performance, we fully recognize that crime is only one of many factors that impact school test performance.  Future studies could include possible additional factors: demographics, family make-up, unemployment rates, etc.

Special thanks to our instructor for the GIS class, Eric Pimpler GISP, for his guidance during the completion of the project.

Joe Ramsey and Jared Garza

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Last Week to Register for ArcGIS Server Summer Classes

Posted on June 1, 2010. Filed under: AGIS Server API for Flex, ArcGIS Server, ArcGIS Server for Silverlight, ESRI, GeoSpatial Training Services |

Next week, three of our instructor guided, Internet based courses on ArcGIS Server are beginning. You still have time to register, but we’re running out of seats.

We still have 6 seats available for our ArcGIS Server Bootcamp. Beginning June 7th and ending in late August this self-paced, instructor led, Internet based course will teach you all the fundamental skills you need to fully take advantage of the new ArcGIS Server 10 platform. The course is self-paced and is designed to accomodate busy work and family schedules as well as summer vacations.

Also beginning next Monday, June 7th are our Programming the ArcGIS Server API for Flex and Mastering the ArcGIS Server JavaScript API courses. We only have 4 seats remaining for the Flex course!

Also beginning in June is the first session of our Programming ArcGIS Server with Silverlight course which begins June 21st. These seats are selling rapidly. The course is still on sale for $667.00 through June 5th.

Other Internet based, instructor led courses this summer:

GIS Programming 101: Mastering Python for Geoprocessing in ArcGIS (Updated for ArcGIS 10).
Begins July 19th.

Programming ArcObjects with .NET
Begins July 19th.

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