Lab 2 ~ The Census

Introduction and Background:
     Every decade, the US government spends a great deal of man-hours and money to collect the Census, which is the most comprehensive survey on the planet.  Information gathered includes age, race, ethnicity, marital status, house ownership and more organized by location; and this data is freely available to the public online.  The goal for this lab is to familiarize myself with retrieving and working with census data in GIS.  We will ultimately create one map of Wisconsin population density by county, and one map of any variable of our choice in the state.

Methods:
     The first step is to download the data desired from American Factfinder2 on the census bureau's website, which in itself is a multi-step process.  For the data I want, WI population by county, I will select out the People, Basic Count/Estimate topic and the Counties, WI geography.  This will narrow down the field of datasets, from which I choose the SF1 2010 data (ACS is the American Community Survey, which is less comprehensive than the Census.)  Finally, the data is ready to download but I must be sure to download the shapefile if I don't already have one to which I will join my data.  This is done by going to the geographies selection menu, and clicking on the "map" tab.
     After unzipping asll of the downloaded files, we are ready to work in the GIS.  First, make sure that the data is joined to the shapefile by joining the two by the GEO#id field, which is the US Census Bureau's unique identifyer for each location.  Then, creating a map is as simple as setting up your symbology and adding the neccesities like a North Arrow, Legend, and Scale Bar.

Results:

GIS II Project ~ Phase 2 Geocoding

Introduction

     If the ultimate goal of this semester-long flirtation with sand mining is to develop and analyze silica sand mining and its effects in the State of Wisconsin, a smart place to begin might be to know where the Silica Sand mines of Wisconsin are located.  To this end, the goal of this project is to geocode the addresses of as many mines as we can in the state for use in future projects.  Having only one goal may sound like small apples, but the time-intensity that can be involved with geocoding makes this exercise no joke.  In order to compile a set of addresses that can be accurately placed in a road network, the class has grouped into pods of four which will ultimately divide up the task of geocoding the data that can be found regarding Wisconsin Sand mines.  Finally, each individual's geocoding results will be compiled and projected in a single geodatabase which will act as the base data for this project as it progresses.

fig 1: Silica sand deposits and mines of 2011 in Wisconsin.

Methodology

     The first task in this project was to explore online data servers that may contain relevant information to the class, described in the previous exercise.  Eventually, the class was given data to download from http://www.wisconsinwatch.org/viz/fracmap/, which provided a list of sand mines in the state as well as their locations, owners, and some other relevant information.  In an ideal world, this table could be run through ArcMap's address locator tool and all of the locations would be output with their approximate real world coordinates attached.  Unfortunately, the table was not conducive to running through the address locator in the first place, and some address fields for mines were missing entirely but had PLSS descriptions or in some cases directions.  So a couple of steps were needed to geocode the mines.

 

fig 2: the original table. note the address field.

fig 3: the updated table, note how the address data is spaced into seperate fields.

 

     The first step was to have the software code as many addresses as possible. However, it would not work immediately because the address field was not stored in such a way as could be read by the address locator tool (fig 2).  In order to fix this, each group member went through their division of roughly 25 mines and separated the address field into a street address, state, and zip code field (fig 3).  Once this information was properly parsed, the table was fed to the address locator tool in ArcMap which generated a list of locations that could be found.  This left the group member with slightly more than half of their mines remaining to be found, on average.
     Having located several mines, the remainder needed to be geocoded by more creative methods.  As so many mines were described by PLSS, the next step was to overlay an aerial basemap of the state with a PLSS dataset in ArcMap and go visually hunt for these mines one by one.  The remaining mines, if they contained any description of location at all, were found by reading the descriptions contained given and looking around the aerials provided through ESRI.  A typical description of this caliber would read "between Knapp and Menominee, along highway xx and between yyy road and the rail line," and with such general descriptions many of these mines were not found.
     At about this point in the process, a google fusion table of this data was discovered and used to inform the geocoding process.  Also, the owner field of the data was used to look up the company websites of those running the silica mines which we were attempting to geocode.  However, the table was incomplete and websites did not provide directions to their mines, so even with collaboration among classmates and the online fusion table many addresses were still not found.
   

Results

     Unfortuntely, the group was not able to geocode all ~125 mines in one swipe, however we were proud to say that we did in fact successfully geocode 75 mines across the state.

Conclusions

     The first and most noticeable conclusion drawn from this exercise is that geocoding can take a lot of time.  Even simply running the tools was a time-consuming task during which I was grateful to have useful tasks that I could complete while the computer worked in the background.  The coder was also somewhat glitchy, as several students had to run the address locator tool multiple times in order to arrive at a useful dataset.
     The feature class developed is incomplete because of an inability to find several mines on the orthophotos which were missing addresses, and while for our purposes this dataset will be adequate I would not accept this work in a professional setting.  The data provided/collected simply needs to be of a higher caliber if detailed analysis is going to be completed on a project such as this.  By the same token, having a messy data set allowed for practice with a number of different methods for locating mines, which could prove useful in the future.
     However, for the purposes of this project it will be adequate to have a

GIS II Project ~ Phase 1

Introduction:

                Hydraulic fracturing, or “Sand Fracking,” is one of many methods by which the energy industry in the United States has been expanding its access to fossil fuels over the past two decades.  The process can be simplified as blasting a rock formation in order to knock loose trapped petroleum or natural gas.  An energy company drills a hole to the rock and then pumps fracturing fluid to the rock through a metal pipe with enough pressure to split the rock along a pressure gradient.  This fluid could be any of a number of materials: gel, foam and detergent could all be included in a mixture.  Along with the fracking fluid is a material called a proppant, which is small grains such as sand or ceramics suspended in a fluid solution.  This is designed to keep the fractures developed by the fluid open, by lodging particles in the small cracks opened up in the rock this material “props” the rock open when the fluid is gone.  If this didn’t happen, the fissures opened by fracking could close under the pressure of the earth!  In Wisconsin, the procurement of this proppant is a rapidly growing industry.  The state happens to be blessed, or cursed, with extremely accessible and pure silica sands which are used as proppant by energy companies performing fracking around the world.

Over the course of this semester, our geography class will step through the process of a land suitability/risk analysis of Wisconsin land for the development of sand mines in order to both expand our technical skills and examine the relationships that this industry has on the local landscape.  Today’s post is the first in this process.

Today’s goal is fairly straightforward: we are going to download geospatial data from various sources and import it into a Geospatial Information System.  This is the preliminary step in in our analysis where we are simply going to prepare data on land cover, railroads, soils, elevation, and cropland.  Once finished, we have all of this information projected into the same coordinate system and compiled in a single geodatabase.

Methods:

                The first task is to download the data that will be used for this project:

                From       Trempealeau County – we will download a land records geodatabase
                                National Atlas – railroads
                                USGS – digital elevation model
                                USDA – cropland data and a drainage index
                                NRCS SSURGO – soil data

                All of this data is in fact free to the public.  Anyone with internet access can use the information that will be used in this task.  Before the data is useable, a few things need to be changed.  The elevation data came in two separate topographic files, which need to be mosaicked together (using the mosaic tool in ArcMap, then mosaic to new raster).  The SSURGO data was downloaded as one database which contained only tables that referenced a half dozen georelational shape files; when importing to my personal geodatabase the shapefile required a component table to work.

 After this was accomplished, a new personal geodatabase was developed to house the data.  The soil feature class was combined into the same table as the drainage index, in order to keep organized and preserve data space and integrity (join tool).  The new soils/drainage information was stored with the data downloaded from Trempealeau County in a feature dataset.  Finally, all of this data was projected into UTM zone 15N, selected because of this exercise’s focus on western Wisconsin which is entirely within this UTM zone.

Results:

                As of yet, most of what has been accomplished is basic data management.  There is not much to see here really, but here are four images of Trempealeau County with their features titled.  They are missing a number of essential map elements, but hopefully they can convey a sense of what is being worked with on a statewide basis.