Arsenic, Lead, and Children of Pierce County
The ramifications of exposure to poisons such as arsenic and lead can be severe, especially when considering children. This analysis shows the spread of children in Pierce County, WA, as well as the concentrations of arsenic and lead in the soils. Combined, these display where the need for toxic remediation is highest within the county.
Note: The data used for this analysis is from 2010, and thus is not current. It was meant for use in an exercise to build GIS skills.
Initial Steps
Beginning with a Pierce County base map, Pierce County census blocks, two tables of combined sample points for lead and arsenic concentrations in Pierce County, and sex by age demographics, the first step was to concert the lead and arsenic data into points using the “display XY data” to create two new point feature classes of the sample data.
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Another new feature class was then created, to which the sample point feature classes were appended.
Pierce County base map and census blocks.
Table of appended arsenic/lead feature class and resulting map.
The appended point feature class was split by selecting by attributes between arsenic and lead samples and exporting each into new layers. These new layers were subsequently dissolved using their unique location names and taking the mean of each sample location’s data, creating one output point for each sample location.
Separating Arsenic and Lead
Interpolating Arsenic and Lead Concentrations
The dissolved samples were interpolated using the Kriging method in order to create a raster layer that predicts lead and arsenic concentrations across Pierce County. This resulted in the final concentration output maps.
These new raster layers were then reclassified by standard deviation into new severity values for use in later analysis.
Resulting maps and concentrations (mg/kg) of the Kriging interpolation.
Reclassification of Lead using standard deviation.
Reclassification of Arsenic using standard deviation.
Child Demographics
Attention shifted to preparing the demographic data in Excel in order to obtain totals by census blocks of children age 10 or younger, which were imported into the geodatabase and joined to the census blocks feature class. This data was then exported to create a new permanent feature class of demographics by census block (PierceBlock_DemoJoin).
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Child density per square mile was determined in the ‘PierceBlock_DemoJoin’ attribute table using new fields and the Field Calculator.
Demographic data preparation in Microsoft Excel.
Imported demographic data with new fields and calculated child density.
Interpolating Child Density into a Raster
The PierceBlock_DemoJoin layer was converted to a point feature class using the “feature to point” tool, resulting in a layer of block centroids. This was followed by creating a new raster layer of child density using the inverse distance weighted (IDW) method and was reclassified by standard deviation as was done with the arsenic and lead samples.
Pierce County block centroids.
Interpolated child density raster using the IDW method.
Reclassified child density using standard deviation.
Reclassifications and
Final Calculations
Finally, the reclassified values of arsenic concentration, lead concentration, and child density were then added using the Raster Calculator, creating a new raster layer showing where remediation is most required in Pierce County. All raster layers were symbolized with the “surface” color ramp.
Composite of reclassified arsenic, lead, and child density layers showing where remediation need is greatest in Pierce County.