Special Topics; Food Deserts and Open Source Webmapping

Greetings,
     Welcome to the continuation of the Special Topics look at food deserts. This is the first of two weeks of analysis type work. Although this week more effort went into analyzing how the data would be presented in a few weeks by looking into web mapping applications built around presenting maps to the public in an open source manner. Last week we started the look at open source, ie Free ways to work with GIS data such as QGIS. This week we are transforming some of that prep work into the web forum in preparation of distributing it to the masses. The overall objectives of this week are:

• Navigate through, and add layers to Tilemill
• Gain familiarity with Leaflet
• Use tiled layers and plug-ins in a Web map. 

   So what are the programs that these objectives mention?  The main theme to note with all of them before looking at them individually is that they are OPENSOURCE! FREE. That means anyone who wants can acquire them, learn about them, and in most cases contribute to the community with them.
   Tilemill is an interactive mapping software predominately used by cartographers and journalists to create interactive maps for sharing with the public.
   Leaflet is a javascripting utility which allows you to code html web maps for display, much like the one linked below.
   The layer tiling mentioned in the last objective was accomplished with some basic html code on notepad, and shared on a webmapping host.

   This http://students.uwf.edu/bd26/STGIS//EscWebMap.html is the link to the culmination of this weeks efforts. It combines the objectives mentioned above with the data we looked at last week for food deserts in the Pensacola Fl, area. Every feature or option on this map falls into one of the objectives above. Note the layers that are turn on- off-able in the upper right, or the find function in the lower left. The points, polygon and circle also are very specific. Each of these elements is an individual block or segment of code which was pre-thought out to contribute to the map in this specific manner. This was all done to get familiar with these applications and get ready to present my own specific area exploring food deserts in a couple weeks, not just the Pensacola information here. Stay tuned as I continue to work toward open source processing and food desert analysis. Thank you.

Special Topics, Food Deserts and QGIS

      Welcome to the beginning of the last multi-week module in Special Topics in GIS. The focus for the rest of the course is two fold. The focus topic for the remainder of the class is on Food Deserts and their increasing proliferation due to urbanization and expansion of the markets / grocers containing wholesome and nutritious foods to include fresh vegetables and fruits and other produce. This second large aspect of this project is that all preparation, analysis, and reporting for the focus area will be done using open source software. As the certificate program as a whole draws to a close it is a good introduction into what is available outside of ESRI's ArcGIS suite of applications. This week I specifically used Quantum GIS (QGIS) to build the base map and do the initial processing of Food Desert data for the Pensacola area of Escambia County, Florida. The overall objectives going into this week are below:

• Perform basic navigation through QGIS
• Learn about the differences of data processing with multiple data sets and geoprocessing tools in QGIS, while employing multiple data frames and similar functionality.
• Experience the differences of map creation with the QGIS specific Print Composer

 Here is a map not unlike many of the others that I have created using ESRI's ArcMAP. That is in fact the point of one huge aspect of this project. There is open source, defined as free to use software which you can personally suggest improvements for update and redistribution to the masses, applications which perform quite similar tasks and produce similar outputs. QGIS is one of these options. Given the background in ArcGIS from the rest of this certificate program there is not as steep a learning curve in picking up QGIS and running with it. There are definitely differences, but with little instruction it becomes fairly intuitive just like ArcMAP. Now you might ask why, if this thing is so similar wouldnt everybody choose it over ESRI products? There is still advanced tools and spatial analysis functions in ArcGIS that are beyond this software. Not everything is wholly interoperable. So for the basic to moderate tasks, absolutely they can be done in QGIS. But sometimes there will be no substitute for the processing ease and power of ArcGIS.
Back to this specific map, what you're looking at is two frames or two sides of the same information. You are presented with both Food Deserts and Food Oasis by census tract for the Pensacola area of Escambia County. These deserts were calculated by comparing the centroid (geographic center) of a census tract with its distance to a grocery store. Tracts without a grocery store providing fresh produce are said to be in a Food Desert. The average person in these areas has to travel farther to obtain fruits and vegetables and the like. When so doing other closer, less healthy alternatives might be taking precedence for these people. Ultimately those with less access are likely to be less healthy overall and that is the issue we are starting to get into with this subject. Keep checking for the next installments of analysis as we continue to look at this problem. The area above is just an example, as the project moves along my analysis will focus on Palm Springs California. Thank you.

Remote Sensing and Supervised Classification

This weeks focus is on supervised classification, particularly with the Maximum Likelihood method. This is a continuation of last week that started the classification discussion with unsupervised classification. The exercise and assignment culminated in the map below revolved around imagery provided by UWF with the following objectives: 

• Create spectral signatures and AOI features

• Produce classified images from satellite data

• Recognize and eliminate spectral confusion between spectral signatures

 Supervised classification revolves around the creation of training sites to train the software in what to look for when conducting the classification. This is accomplished by creating a polygon type area of spectrally similar pixels. Examples would be dense forest, grassland, or water. Each area has a distinct spectral signature. These signatures are used to evaluate the whole of the image and allow the software to automatically reclassify all matching spectral areas. The overall process is usually in 4 steps, get your image, establish spectral signatures, run the classification based on the signatures, then reclassify or identify rather what your class schema is.

The process is fairly straightforward with only a couple things to watch out for when establishing the spectral signatures. Being sure to avoid spectral confusion. This is where multiple features exhibit similar spectral signatures. This usually occurs most frequently in the visual bands, and can be avoided by doing a good check using tools such as band histograms or spectral mean plotting which shows you the mean spectral value of one or more bands simultaneously. We can see some of the results of this below, such as the merging of the urban / residential and the roads/urban mix.

This is a Land Use derivative for Germantown Maryland. It was created using a base image and supervised classification looking for the categories displayed in the legend. This map shows the acreage of areas as they currently exist and is intended to provide a baseline for change. As areas get developed the same techniques can be used on more and more current imagery to map the change and gauge which land uses are expanding / shrinking most and by how much.
This was an excellent introduction to one method of supervised classification, there are many other types and reasons to conduct it, but those are for another class. Thank you.

Special Topics and Meth and Analyzation

        Welcome to the continuation of our look at statistical analysis with ArcMAP. Recall that the theme being explored with statistics is methamphetamine lab busts around Charleston West Virginia. These past two weeks of analysis have been the bulk of the work for this project. The overall objectives of the analysis portion of this project are to review and understand regression analysis basics, and a couple key techniques. Define what the dependent and independent variables for the study are as they apply to the regression analysis. Perform (multiple renditions) of an Ordinary Least Squares (OLS) regression model. Finally, complete 6 statistical sanity checks based on the OLS model outcomes.
       In the previous post we looked at a big overview of the area that is being analyzed. There are 54 lab busts from the 2004-2008 time frame from the DEA's National Clandestine Laboratory data. Decennial census data from 2000 and 2010 at the census tract level was spatially joined to these 54 lab busts. The data was then normalized into a percentage by census tract into 31 categories for analysis in the OLS model. These 31 categories of data were then fed into the model and systematically removed while analyzing their affect on the model. Ultimately as good a model as possible was arrived at with some results below.

        This is an extract of the table that I put together from the ArcMAP generated output to depict the OLS results. This was a cleaner format than the straight screen shot because it incorporates the descriptions of the individually labeled data elements. Key things to note for the table are that there are now only 11 variables being incorporated into the OLS model of the original 31. How were variables removed you might ask? There are six checks or questions to answer to determine the validity of a variables use in the OLS model: does an independent variable help or hurt the model; is its relationship to the dependent variable as expected; are there redundant explanatory variables; is the model biased; are there variables missing or unexplained residuals; how well does the model predict the dependent variable? The first three of these were generally grouped into 1 solid check for determining if a variable should stay or go. The remaining checks were applied to the model results as a whole. The key attributes to look at for a variable fall in line with [a], [b], [c],  as depicted on the table. As long as you had a coefficient not near zero, probability lower than .4, and VIF less than 7.5 a variable could stay. Not all of these match this criteria now, however you have to look at model functionality as a whole. The R-Squared [d] value is right at .7 (rounded up) which means that the model as it is accounts for 70% of the meth labs location based on the variables in use. This is pretty good when working with sociological data of this type. After looking at this data table its time to transition to the visual interpretation seen below.

     This map depicts the standard residual for the OLS model depicted in the table. It symbolizes areas using a standard deviation style outlook. However rather than wanting a more Gaussian curve style of data showing some of every color you ideally want values to be in the -0.5 to +0.5 range because that is said to be highly accurate. Darker browns indicate areas that the model predicted less meth labs than there actually were, and darker blues indicate high value areas where the model expected more meth labs than were actually present. Remember though that from our table above we are only doing a good job of predicting 70% of the total meth labs and the majority of the study area is still within 1 standard deviation.
    This weeks focus was not to describe the data results, but to accomplish the analysis leading up to it. Please follow up next week for a look at the finalized product. Thank you.