Economic Development: Future Employment

Opportunities in Neighborhoods of Low

Socio-Economic Status in Tacoma, WA

Natasha Boyde

Initial Goals

• Is Poverty in Tacoma under-served by employment opportunities?• Identify areas of concentrated poverty• Find vacant commercial parcels within walking and

biking distance of these areas to create jobs• Reduce commuting to jobs• Increase walkability of a neighborhood

self-sustaining community

Literature ReviewFinding the Problem• America’s “car culture” has decentralized jobs• Deindustrialization, gentrification, economic decline• Suburban sprawl causing decline in neighborhoods into low-

income and poverty• Spatial mismatch of jobs to people• Unemployment relates to more variables than poverty and

income• Lack of jobs exacerbates economic problems and concentrates

poverty in an area• Lack of mobility

Literature ReviewIdentifying Solutions• Factors of accessibility to jobs:• Education• Discrimination• Transportation• Physical accessibility• Mixed Use development• Commercial AND Industrial development

• Walkable neighborhoods• Dense, compact, urban cities

Revised Goals

• Expand demographic research from poverty to Socio-economic Status• Commercial AND Industrial parcel analyses• Biking and Walking networks• Hot Spot Analysis

Data Collection• Census.gov• 2000 Census data (SES variables)• Block Group Polygons

• WAGDA• Tacoma Streets• Parcel Polygons & Land Use Attributes

• City of Tacoma• Bike Paths & Bike Ways

• GIS Labs• Pierce County Basemap• City of Tacoma Boundary

MethodsSES Index• Variables: • Race• Poverty• Linguistic Isolation• Education• Single mother households• Unemployment• Kitchen & Plumbing facilities

Standardizing SES Variables• Calculate a rate, the mean and standard deviation of each

variable• Create a standard score for each variable:

• Example: Mean poverty rate is .08 and standard deviation is .02

• ((PovRate - .08) / .02)

Calculating SES score• Classify each variable’s Z-score• Create a new field called SES Index• Field calculator to add each variable’s Z-score

Total SES score for each block group•Join to Block Group polygons•Create Choropleths •Convert Block Groups into points •Hot Spot Analysis

Issue #1: Hot Spot Analysis

IDW Interpolation of SES

Find Low SES Neighborhoods:Re-Classify

Creates new polygons to extract low SES neighborhood polygons

Network Analyses

• Create new fields -“WalkTimeMinutes”-“DriveTimeMinutes”

•Create Network Datasets for Walking & Biking•Create start points in each neighborhood•Create Service Areas:-Bike Time: 10 minutes-Walk Times: 10, 15, 20 minutes

Hilltop Bike Service Area

Salishan Walk Service Area

Parcel Analyses• Add Parcel polygons and

attribute table to map -JOIN

• Create new fields in parcel attributes:-Commercial, Industrial, Vacant-0 for no, 1 for yes

• Used SQL queries to select vacant parcels within each service area network

Issue #2:Salishan Parcel Analysis

• Not enough vacant parcels in Salishan

• Decided to find under-utilized parcels

Under-utilized Parcel Analysis

• Got Zoning polygons from Natasha H.

• Used SQL Queries to find parcels that were being used for something other than what they were zoned for.

Results

• Lowest SES neighborhoods: Hilltop & Salishan• Several vacant parcels in Hilltop• Not many vacant parcels in Salishan• Many under-utilized parcels in Salishan, mainly

Industrially zoned

Reflections• Index skills• Introduced me to Salishan• Urban planning and housing projects• Cluster analysis• Use of many tools• Last-minute analyses

Future Research• 2010 Census data• Sidewalks• Car Ownership Data• Tacoma Housing Authority

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