Areas in Tacoma, WA

Most in Need of Youth Programs

Determining Median Income

To conduct this analysis, 2015 median income tabular data for block groups in Pierce County, WA was obtained from the US Census Bureau and, was joined to block group polygons within Tacoma, WA. Data pertaining to areas outside Tacoma's boundary was discarded. With median income data joined to Tacoma's block group polygons, it was then converted from polygons to points and the Inverse Distance Weighted (IDW) method of statistical interpolation was employed to create a raster depicting median income for Tacoma, which can be seen over-layed with the neighborhoods comprising Tacoma (Fig.1) symbolized using a red-to-green color ramp where red indicates a low median income and green indicates a high median income.

With raster data depicting median income for the City of Tacoma, ArcScene was employed to model the data in 3D by choosing median income for the possible Z-values. Fig. 2 displays median income results using the same color ramp as Fig.1 with the addition of 'peaks' and 'valleys' where green peaks represent high median income and red valleys indicate low median income.

Determining Children per Square Mile

For the purpose of this analysis 'youth' or 'children' were defined as those under 10 years of age. Tabular Census data was used to determine the number of children under 10 years old by block group in Pierce County, WA. In ArcMap the tabular data was joined to Tacoma's block group polygons and data falling outside Tacoma's boundaries was discarded. Using the Field Calculator within ArcMap, the density of children per square mile was calculated, then converted from polygon to point data located at the center of each block group and the Inverse Distance Weighted (IDW) method of statistical interpolation was employed to create a raster depicting children per square mile as seen with an overlay of Tacoma neighborhoods (Fig. 3) symbolized with the red-to-green color ramp where red indicates high child density and green indicates low child density. The red-to-green color ramp was chosen for continuity and is not meant to express any view about high or low child density being good or bad.

With raster data depicting child density for the City of Tacoma, ArcScene was employed to model the data in 3D by choosing children per square mile for the possible Z-values. Fig. 4 displays child density results using the same color ramp as Fig. 3 with the addition of 'peaks' and 'valleys' where red peaks represent high child density and green valleys indicate low child density.

Identifying Areas Most in

Need of Youth Programs

Fig. 5

Fig. 6

Background

According to Youth.gov, “(a)s youth grow and reach their developmental competencies, there are contextual variables that promote or hinder the process… The presence or absence and various combinations of protective and risk factors contribute to the mental health of youth. Identifying protective and risk factors in youth may guide the prevention and intervention strategies to pursue with them. Protective and risk factors may also influence the course mental health disorders might take if present.” Some of the greatest risk factors for youths are living in an urban setting and poverty, while some of the protective factors to safeguard against those risks are the presence of mentors, support for development of skills and interests, and opportunities for engagement within school and community. With that in mind, the purpose of this analysis is to identify areas in Tacoma, WA that have low median income and a high amount of youths to determine where youth programs (before- and after-school and community programs) have the potential to have the greatest positive impact on youth development. This analysis does not take into account the existence or number of youth programs currently operating in any area. It seeks only to serve as a guide for where youth programs could have the greatest impact.

To identify areas in Tacoma most in need of youth programs, the previously produced median income and child density data were reclassified to assign a numerical classification or 'score' representative of the data for median income and child density respectively. A raster calculation was then performed on the reclassified data in order to determine a composite 'score,' which represents the need for youth programs in Tacoma. Results of the raster calculation can be seen in Fig. 5 with an overlay of Tacoma neighborhoods along a red-to-green color ramp where red indicates the greatest need for youth programs -- those areas with the lowest median income and highest child density -- and green indicates the least need for youth programs -- areas with the highest median income and lowest child density.

Modeling the results of the raster calculation in ArcScene using the composite 'score' as the Z-value generated the 3D representation of the data in Fig. 6 using the same color ramp as Fig. 5 with the addition of 'peaks' and 'valleys' where red peaks represent the greatest need for youth programs and green valleys indicate the least need.