People in the Landscape - USEPA/ATtILA2 GitHub Wiki

Introduction

Human land uses and associated changes to land cover are one source of environmental stressors. For example, agriculture usually produces higher levels of nutrients like nitrogen and phosphorus than natural land cover. Excess export of nitrogen and phosphorus to streams leads to eutrophication of streams, lakes, bays, and other water bodies. Most of these nutrients come from non−point sources like animal waste from livestock operations and fertilizer applications on agricultural fields, golf courses, and residential lawns. Urban land uses increase the amount of impervious surfaces (roads, rooftops, parking lots, etc.), which increases the volume and force of precipitation runoff. Increased runoff leads to higher risk of flooding and also carries larger amounts of pollutants and sediment into surface waters.

Population density and change can be used to assess the potential magnitude of human impacts on the environment. Population growth can contribute to changes to land cover and landscape patterns, such as forest fragmentation, loss of interior forest, and increased impervious surface, which can put pressures on ecosystems. Looking at population change can be helpful in identifying reporting units experiencing high rates of growth, making them vulnerable to adverse impacts, or to analyze the history of land use changes in a region.

Roads are another type of human-related stressor. Density of roads may be a surrogate for population density. Roads near streams or other water bodies increase the potential for pollution from runoff. Bridges or other stream crossing may alter the flow hydrology of streams as well as provide access points for dumping.

The People in the Landscape toolset includes the following metric tools:

  • Facility Land Cover Views counts the number of facilities in each reporting unit polygon that have less than a certain percentage of selected land cover classes within a given distance and creates an output table. The percentage threshold value and buffer distance are provided by the user.

  • Intersection Density estimates the intersection density of roads within a given search radius for any pixel in the output raster. Intersections are defined as any point where three or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections.

  • Land Cover Coefficient Calculator encompasses the nutrient (nitrogen and phosphorus) loading and the impervious surface estimate metrics contained in an earlier release of ATtILA. For nitrogen (N), phosphorus (P), and impervious surface, coefficients for various land cover classes are applied (multiplied) to a grid of land cover. Coefficient values based on literature reviews are included as default values for the default land cover classification schemes, but the user is free to alter these values to suit his/her individual assessment needs. The user may also use a land cover classification other than the ones supplied with this release of ATtILA, which then requires the user to enter their own custom coefficients. The land cover coefficient calculator may also be used for other custom purposes beyond land cover. The only required input datasets are a grid with discrete values and a polygon theme of reporting units. Custom coefficient values are also required if the default values are not to be used.

  • Population Density Metrics calculates the population count and density by reporting unit; it can also be used to calculate population change. Population density is reported as number of people per km². Population change is reported as percentage change in population by reporting unit. The population change tool is quite versatile, like many of the metrics in ATtILA, and can be used beyond its stated purpose. For example, instead of selecting population counts from different time periods, the user could select fields containing counts of male and female members of a population at a given time. The POPCHG metric could then be used to show the percentage difference between the number of males and females for the given reporting unit. Another possible application would be to use ATtILA to calculate the total area of a land cover class in a reporting unit for two different time periods. Using these two themes as inputs into the POPCHG metric, the user could then calculate the percentage change in the land cover type over the given time period.

  • Population in Floodplain Metrics calculates percentage of total population living in floodplain areas for each reporting unit polygon and creates an output table. Counts for the total population in the reporting unit and within the floodplain area are also reported.

  • Population Land Cover Views estimates the percentage of the population in each reporting unit that have potential views of selected land cover classes within a given distance from their location and creates an output table. A land cover class is visible if it is found within the neighborhood defined by the View radius and in a group of cells larger than or equal to the Minimum visible patch size. The Minimum visible patch size and View radius are provided by the user. Additional fields for total population count within the reporting unit, the total population count in the reporting unit with potential views, the total population count in the reporting unit without potential views, and the percentage of the total population in the reporting unit without potential views are also output.

  • Road Density Metrics includes road density, roads crossing streams, and roads near streams metrics. The road density metrics require only an input reporting unit polygon theme and a line theme of roads, although any other line theme could be substituted. To calculate roads near streams and roads crossing streams in addition to road density, a line theme of streams is also required as an input. Again, any line theme which makes sense for the analysis could be substituted. For example, this metric could be used to assess the density of power lines or to determine the frequency of railroad tracks crossing roads.

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