Urbanization Parameter Measurements

Smucygz (2010) uses population statistics and the percent impervious surface area at the

basin-level to define urbanization. Other studies generalize urbanization as being land cover change

(Poelmans, et. al, 2011; Guo, et. al, 2008; Schoonover, et. al, 2006). According to Jarnagin (2006) a direct

and quantifiable result of urbanization is the transformation of natural land cover to impervious surfac-

es, which include roads, rooftops, parking lots, driveways and sidewalks.

Population growth is one of the most obvious and greatest drivers in land cover change includ-

ing increased imperviousness. The environmental impact to an area from urbanization can be seen

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Environmental Impact = Population * (Consumption/Efficiency)

This equation shows, quantifiably, that there are magnifiers and modifiers from population growth that

affect the environment. Every person has a minimum requirement for space, food, water, and air and

land use will change in association with those requirements. More people need more resources which,

in turn, results in a greater conversion of natural land-cover to a human-altered land-cover or to a more

intensive use of existing land cover that has already been altered (Jarnagin, 2006).

This particular study, like Smucygz (2010), used spatial analysis to evaluate both changes in

urban land use and population to understand the degree of urbanization then relate that, statistically, to

historical stream data. Multiple GIS layers were downloaded from a variety of sources, but the most

suitable used were from Atlanta Regional Commission, US Census Bureau, University of Georgia’s

Natural Resources Spatial Analysis Lab and Minnesota Population Center National Historic Geographic

Information System. All urbanization parameters (land use raster files and population shapefiles) were

added by year to each delineated watershed. A map was made of each parameter for each year of data

availability. For the population maps and analysis, Census data tables for 1980, 1990, and 2000 were

added and joined to the respective shapefiles through the ArcGIS join function. The 2010 TIGER

shapefile already had Census population data in the attribute table. The shapefiles were clipped using

the ArcMap clip tool within the delineated watershed. The population data were displayed from the

joined attribute table. Since the population census tracts change over time, it is difficult to visually

assess the change in time that is occurring because larger census tracts are broken down into smaller

census tracts within the watershed areas from 1980 to 2000. The only two maps with the same census

tracts are the population maps for 2000 and 2010. Because, visually, they don’t provide much, the maps

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Land Use Trends shapefiles for 1974, 1985, 1991, 2001, 2005, and 2008 were downloaded from

the University of Georgia’s Natural Resources Spatial Analysis Lab (narsal.uga.edu) and added to a map

with all of the delineated watersheds in ArcGIS. The 1974 land use trend was selected and used in order

to establish the degree of urbanization already present within each watershed. The land use raster files

already had appropriate data within the attribute table. Land use was selected and displayed for each

land use map. According to UGA’s Natural Resources Spatial Analysis Lab (NARSAL), low-intensity urban

land use is defined as “single family dwellings, recreational areas, cemeteries, playing fields, campus-like

institutions, parks, and schools” (narsal.uga.edu/glut/classdescriptions/, 6/5/2014). High-intensity ur-

ban land use is defined as “multi-family dwellings, commercial/industrial areas, prisons, speedways,

junkyards, confined animal operations, transportation, roads, railroads, airports and runways, and utility

swaths” (narsal.uga.edu/glut/classdescriptions, 6/5/2014).

The clip tool was used to isolate and define the land use trends for each year within the bounda-

ries of each watershed. A Count Attributes was created for each year for each watershed to understand

the total amount of each land use. The rows with unique identifiers of 22 and 24 represent low intensity

urban land use and high intensity urban land use, respectively. Those values were isolated and recorded

with the watershed area used to define each clip. A percent low intensity urban land use, high intensity

urban land use and total urban land use were calculated for all watersheds and all years and recorded in

a spreadsheet. A Mann-Kendall test was performed on each of the land cover data sets for each stream

to determine any trends.

Population by Census Tract was collected for years 1980, 1990, 2000 and 2010 from the U.S.

Census Bureau (www.census.gov). Census Tract shapefiles were downloaded from the U.S. Census Bu-

reau as well as from Minnesota Population Center National Historic Geographic Information System

(www.nhgis.org) and were added to the watersheds map in ArcMap. The shapefiles were lacking popu-

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dered and downloaded from the Minnesota Population Center’s National Historic Geographic Infor-

mation System. The data files were converted into dbf data tables suitable for ArcMap. The table and

shapefiles attribute tables were put together using a join by attribute.

In ArcMap, the Census Tract shapefiles were clipped using the watershed delineations. Popula-

tion data for all Census Tracts within the watershed were collected and summed up from the attribute

table. The population by Census Tract within the watershed was plotted against the runoff for each per-

centile for each stream. A Mann-Kendall test was run on the population data set for each watershed to

identify any long-term trends in population. Then a correlation analysis was used to determine any cor-

relation between population growth and baseflow.

Individual maps of each stream were created displaying the watershed delineations and stream

flow accumulations, land use trends by year and population data by year.

After initial review of the results, it seemed that additional statistical analysis was needed to fur-

ther understand the relationships. Because the parameter data are limited in the scope of availability

relative to stream discharge data, parameter data was interpolated between years of available data.

With the additional interpolated values, the linear correlation was run with a one-to-one relationship

with the stream discharge values for each percentile.

Population data was interpolated from 1980 to 1990, from 1990 to 2000, and from 2000 to

2010. A correlation analysis was run on all of the population values from 1987 to 2010 and stream dis-

charge data from 1987 to 2010. Similarly, land use change data was interpolated for additional statisti-

cal strength. A correlation analysis was run for each urban land use parameter (total, high-intensity, and

low-intensity) with the stream discharge from 1987 to 2008. Because the latest land use data point is

2008, the correlation stopped at that year. These correlation analyses were done for each percentile

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RESULTS