RESULTS AND DISCUSSION

•  When looking at the density of wells per square mile, results are similar to what would be expected from the Wells Locations in Weld County map. •  When mapping population density, there is a clear center of population distribution in the county, with population density decreasing fairly quickly

outside of the urban center. •  Well density divided by population density combines the two factors and offers a way to compare the relative impact of the oil and gas industry on the

population per square mile. When looking at the relative impact, there is a distinct pattern essentially in reverse of the well and population density. The impact of the oil and gas industry relative to the population per square mile is much higher the further away from the urban population center of the county, even though both well density is lower in the less populated census tracts.

BACKGROUND Weld County, Colorado is a 3,996 square mile county in northeastern Colorado that includes both highly developed cities and rural communities. The county is rapidly growing and developing, and is the most productive agricultural county in the state, producing $1.5 billion in agricultural products yearly. It is also one of the most active counties for the oil and gas industry, with 70 current companies with operations, and over 33,000 wells on file. In 2009, the first well that combined horizontal drilling and hydraulic fracturing technologies was drilled in the county. Hydraulic fracturing is the process of drilling and injecting fluid into the ground at a high pressure in order to fracture shale rocks to release previously inaccessible stocks of oil and natural gas. There are growing concerns, however, about the safety of industry operations and their impacts on groundwater, human health, and geological stability of regions around injection sites. Now, the combination of technologies is used at virtually every well across the state.

RESEARCH OBJECTIVES This analysis considers the spatial distribution of oil and gas industry activity within the census tracts of Weld County, Colorado. Population per square mile and number of wells per square mile are calculated for each census tract and values are mapped across the county as a whole. Potential for industry impact within each census tract (either negative or positive) is calculated by dividing the number of wells per square mile by population per square mile. Larger values represent more wells per population per square mile than smaller values—meaning that impacts from the oil and gas industry will be felt more strongly across the population in those tracts with larger values. The potential for impact is mapped and compared to two demographic characteristics for each census tract: the percent of the population that falls below the poverty line and the percent of the population that lives in a rural area. This is a relevant analysis because oil and gas operations only continue to grow in Weld County, and impacts of the industry are not necessarily felt equally by all groups of the population. Comparing the distribution of relative industry impact to poverty levels may offer insight into the contribution of the oil and gas industry to local economies, and comparing the relative industry impact to the percent of population that lives in rural vs. urban areas may indicate the types of communities that may be hit hardest in the case of negative environmental and human health consequences.

METHODS: DATA DOWNLOAD AND CREATION 1.  Download and unzip the following four source data sets stored as shapefiles: a. Well Locations (statewide, vector points): cogcc.state.co.us b. Colorado State Boundary (vector polygon): census.gov/geography.html c. Colorado County Boundaries (vector polygon): census.gov/geography.html d. 2010 Census Tract Data (vector polygon): census.gov/geography.html 2. Set up a new geodatabase with the following design: Carmack_Final_Project.gdb Feature Dataset: Base_Data (in NAD1983 UTM Zone 13N)

Feature Class: CO_Boundary (polygon) Feature Class: County_Boundaries (polygon)

Feature Dataset: Weld_Data (in NAD1983 UTM Zone 13N) Feature Class: Weld_Boundary (polygon) Feature Class: Weld_Wells (point) Feature Class: Weld_Census_Tracts (polygon)

3. Source data and processing for each feature class within the geodatabase: CO_Boundary: imported from Colorado State Boundary shapefile County_Boundaries: imported from Colorado County Boundaries shapefile Weld_Boundary: select by location from County_Boundaries feature class Weld_Wells: clip from Well Locations shapefile to Weld_Boundary

Weld_Census_Tracts: clip from 2010 Census Tract Data shapefile to Weld_Boundary feature class Create the following fields in the Weld_Census_Tracts attribute table: 1.  Name: TOTAL_WELLS Type: Double Values: number of wells in each tract 2.  Name: AREA_SQ_MI Type: Double Values: Calculate Geometry > Area 3.  Name: WELLS_PER_SQ_MI Type: Double Values: Field Caluculator WELLS_PER_SQ_MI= [TOTAL_WELLS] / [AREA_SQ_MI] 4.  Name: WELLS_V_POP_PER_SQ_MI Type: Double Values: Field Calculator

> Write and run the following expression: WELLS_V_POP_PER_SQ_MI= [WELLS_PER_SQ_MI] / [POP_PER_SQ_MI]

CONCLUSIONS

This analysis identifies a correlation between higher relative industry impact and lower poverty rates in census tracts. Future research should focus on exploring this relationship in more detail, and throughout time. It would be interesting to further study the relationship and see if over time the poverty level in tracts with high industry involvement goes up or down. This could aid in the understanding of long term impacts of industry presence on local economies. This analysis also shows that rural areas have a much higher relative impact from industry activity than urban areas. This is important when policy makers are considering what types of emergency management plans are in place when dealing with negative externalities from industry activity. Because the industry has a larger presence in rural communities, policy formation and planning should address the unique challenges rural areas pose– such as transportation challenges and distances to emergency amenities such as hospitals or community centers. Overall, this analysis is useful in helping to identify what areas of the county could be affected most by potential impacts of the oil and gas industry– both positive, such as possible lower poverty rates, and negative, such as groundwater contamination. Because the industry only continues to grow in the county, this information should be expanded on in the future to aid policy makers and community members in making educated decisions about how to deal with oil and gas industry activity.

•  When comparing the percent of households living below the poverty line per census tract in comparison to the relative industry impact as represented in the Well Density divided by Population Density map, we see that the tracts with the lowest relative industry impact have the highest percentages of households living below the poverty line. Tracts with the highest relative industry impact do not have the lowest poverty rates in the county, but there is a definite correlation between higher relative industry impact and lower levels of poverty.

•  When comparing the percent of population that lives

in a rural area per census tract and the relative industry impact, we see that tracts with a higher percentage of rural population also are the tracts that have higher relative industry impact, as shown in in the well density divided by population density map. The tracts with the lowest relative impact are also the tracts with the lowest percentage of rural population.

Created By: Linde Carmack GEG 240 Spring 2015

Data Provided By: COGCC & US Census

(Per Square Mile)