GIS 4930 - Providence Food Deserts

After completing an analysis of Providence food deserts, I have produced a web map as well as a presentation document to communicate my results.

GIS 4930 - Further Adventures in Web Mapping

After completing the practice work with maps for Pensacola, this module moves on to doing similar work for my own particular study area - Providence, Rhode Island.

After creating an analysis of food deserts within the municipal boundaries of Providence, I created web tiles that showed the census tracts for Providence that I had determined were food deserts as well as another tileset for grocery stores in the area. I used the site mapbox.com to store tiles I created using Tilemill. This proved to be especially challenging as mapbox has undergone some updates recently and the instructions I had were out of date. However, I perservered and was able to get my data up and available. I did have to find a new set of code examples for my map page, though, before I could serve my data online. I'm quite excited at what these tools offer, although it would be good if APIs were less subject to radical change.

The image below is a simple screenshot showing my tiles appearing in a web browser.

Screenshot of Providence food deserts

GIS 4930 - Open Source Web Mapping

This week was involved in creating a web map using open source resources. Leaflet, a java mapping API, was used to create a live webmap stored on my UWF web drive. As this was practice to learn a bit about how to use leaflet with mapping tiles stored on mapbox, we were provided with extant tilesets and simply had to create an html file that would be able to load them properly as well as some bits of map functionality through plugin calls.

The map created shows Pensacola, FL and includes indicators for food deserts as well as for fictional (I believe) frisbee golf courses.

GIS 4930 - Open Source GIS - Food Desert Analysis

This module invovled using QGIS to create a map displaying food deserts in Pensacola, Florida as a preparation for creating a similar analysis for another region in the coming weeks. We were given the data that was used to process the map below, which displays food deserts and food oases in Pensacola, FL based on distance from grocery stores. Using US census tracts, a tract was determined to be 'desertified' if it's centroid was over a mile away from a grocery store.

Food Deserts in Pensacola, FL

GIS 4930 - Standard Residuals of Meth Lab Density Model

During the analysis phase of this module, a large number of potential explanatory variables were assessed to explain the density of meth labs found in and around the Charleston area. Most of them were gleaned from the US Census data for 2000 and 2010. The map below shows the standard deviation residuals for a linear regression model that includes variables for population density by census tract, percentage of households with a single male head of household and one or more children, and the ratio of males to females in the tract. The adjusted R-Squared value for this model is 0.572. The residuals below indicate that some tracts actual density did not match the model's predictions very well (the red ones were too high and the green, too low), but the distribution does not seem to be terribly biased, which is also confirmed by the Jarque-Bara result of 5.36.

Standard Residuals for Methamphetamine Density Model for census tracts in and around Charleston, WV

GIS 4930 - Statistical Analysis of Meth Lab Locations

We're starting a new project this week that involves doing statistical analysis of meth lab locations as an attempt to find correlations with population, geography and economic factors that may aid in predicting trouble spots for meth preparation.

The map below is a basic map of the study area showing methamphetamine labs that were interdicted over the course of 2004-2008 in and around Charleston, West Virginia. The study area layer has attributes for census tracts that will be correlated with the location of the labs. Preparations were made this week to create further attribute fields for use in the analysis stage of this project.

Meth Labs in Charleston, WV (2004-2008)

GIS 4930 - Completed MTR Story Map

The work of the past two weeks involved coordinating amongst my group as we all took a LandSat image from 2010 that covers a portion of our study area to analyze for evidence of mountain top removal. The analysis was fairly basic, and in all likelihood did not produce a robust set of MTR location data. See the previous entry for details of the analysis. I ended up doing my analysis over and got better, but still iffy, results.

Each of our team produced a layer package with our analysis as a polygon layer. I merged these into one unit and produced a calculation for acres covered by our MTR zones as well as an estimate of its accuracy, which was based on only a sparse number of randomly selected points. The sparsity was an artifact of the time it would take to do a thorough accuracy assessment.

Once the layers were merged, they were shared back with the entire group and we used them in our final story map.

Two layers [MTR zones & drainage] sans context - as they were uploaded to arcgis online.

GIS 4930 - Mountain Top Removal - Analyze Week - Unsupervised Classification

This week an analysis of LandSat imagery from 2010 within the study area was performed. After inspecting a raster from 2005 and a shapefile associated with it that showed mountain top removal from that time period, an unsupervised classification was performed on the 2010 imagery. With the previous data as a guide, classes were then designated as either MTR (areas where mountain top removal occurred) or non MTR. A new raster was then generated from this that only showed the MTR areas. The screen shot below shows the result, which will be integrated with reclassifications from classmates on my team as this project continues.

Screenshot showing reclassified raster image

GIS 4930 - Project 02: Addressing Mountain Top Removal (Hydrology & Remote Sensing)

Preparation

This week sees the beginning of a new project, the creation of a story map that shares information on the landscape footprint of an extreme coal mining practice - Mountain Top Removal.

In addition to the description of the practice shared above, we will be performing analyses of the how a regions hydrology and topography is impacted by this kind of mining. Preparations for this week included generation of hydrological rasters and shapefiles from an assigned study area (see below) and setting up a placeholder site for a story map that will be used to share the analysis once it is complete.

Preparatory Base Map for MTR Analysis in the Appalachians

GIS 4930 - Creation of Route Maps

This week saw the creation of a lot of maps, each with a specific audience in mind and intended to describe routes for those audiences.

One of the maps below is an inset map from a larger document showing a basic route from a hospital expected to be inundated by an oncoming storm to one on higher ground. The second map shows a driving route for emergency services personnel delivering supplies from the National Guard armory to one of the emergency shelters set up throughout the city of Tampa.

Route to St. Joeseph's Hospital

Route from National Guard Armory to Tampa Bay Blvd Elementary School (temporary emergency shelter)

GIS 4930 - GIS Network Analyst Route Generation

This week I took the road network I generated last week and created routes for the ostensible emergency hurricane situation in Tampa that's the conceit of this current mapping project. Routes were generated to evacuate a hospital, to direct national guard troops from the local armory to evacuation shelters to deliver emergency supplies, to show routes out of downtown to an evacuation center and a set of polygons was generated that shows residents which evacuation center is closest from their location.

All of these routes are shown on the map below, which makes things quite busy. Next week routes will each be featured individually on maps generated with a specific target audience in mind.

Tampa Metro Area Basic Evacuation & Emergency Services Routes

GIS 4930 - Network Analyst Data Preparation

This is the first installment of a multi-week project. The conceit of the project is that optimal evacuation routes need to be generated for the Tampa, Florida metro area just days ahead of an expected hurricane. These routes will then be produced in maps for use by both the general public and by emergency personnel. This week's was creating the base map from which a network dataset will be generated that will be used to generate routes in upcoming weeks. This involved prepping feature datasets for that upcoming work and also generating a basic map that shows the location of emergency services and also the expected flood zone of the area. This prep also included creating attributes in feature datasets that will be used for generating a network dataset.

Basic Map of 5 foot flood zone for Tampa, Florida metro area

Special Topics - Network Analysis & Route Generation

Continuing on from the previous week, this portion of the project involved taking the prepared data files - especially the roads feature dataset that had flood areas, drive times and distances added to its attributes - and generating a network dataset from them, i.e. a dataset that can be used to generate routes from one location within the network to another with minimized costs (usually in distance or time) to find optimal routes. In this case, once the dataset was generated, routes were found for the purposes of evacuating a hospital and also routes were found to be used for delivery of relief supplies to designated hurricane shelters across the city. These routes were generated using expected flood areas as a restriction, although routes for emergency personnel were generated that could move through flood zones, but with a high avoidance factor.

In addition to these routes, the network dataset was also used to generate service areas representing which shelters were closest for any particular portion of the road network across the network. These were shown as polygons around each shelter area, where any part of the network within a particular polygon was closest to the shelter location contained within it.

The map below shows all the routes and the service area generated during this week's analysis. Further routes could be generated at need. Also shown are hospitals, fire and police stations.

Basic Tampa Hurricane Evacuation Map

Special Topics - Data Preparation for Network Analyst

This is just the first part of a multi-week project. The conceit of the project is that optimal evacuation routes need to be generated for the Tampa, Florida metro area just days ahead of an expected hurricane. These routes will then be produced in maps for use by both the general public and by emergency personnel. This week's work was simply to create the base map from which a network dataset will be generated that will be used to create the routes in upcoming weeks. This involved prepping feature datasets for that upcoming work and also generating a basic map that shows the location of emergency services and also the expected flood zone of the area. This prep also included creating attributes in feature datasets that will be used for generating a network dataset.

Base Map of Tampa Metro Area with Expected Flood Zone & Emergency Services