USE OF GIS IN JOB IDENTIFICATION

USE OF GIS IN JOB IDENTIFICATION MAGIC Annual meetingNovember 14 & 15, 2013 Angela Antipova EsraOzdenerol Department of Earth Sciences, University of Memphis antipova@memphis.edu

OBJECTIVES • 1. Job identification • Aerotropolis • 2. How good are your data? • Problems with use of data

Figure 1: the five-county study area.

The five-county study area: rationale for choosing units • Although 2 TN counties, Tipton and Fayette, are outlying, • immediately adjoining the central Shelby County, • a high degree of social and economic integration with the central core as measured through commuting (Office of Management and Budget, 2010). • Other outlying counties, part of the Memphis metro area, were not included • Benton, Marshall, Tate, and Tunica, all located in Mississippi.

1.2.Questions sought to answer. • (1)if modificationof the criteria by a previous method combined with an expansion of the study area will impact employment identification; • (2) the distribution of resident workers relative to employment centers; Figure 2: kernel density of work locations in the study area, 2011.

1.2.Questions sought to answer. • (3) the degree of job decentralization, • whether most jobs relocated to the suburbs. • By studying these patterns, important information can be gained to balance job and labor market. Figure 2: kernel density of work locations in the study area, 2011:where jobs are concentrated

Preliminary exploratory analysis • Residential locations: • Figure 3 illustrates where workers reside within the study area in 2011. • larger urban territory, while employment locations are significantly more concentrated. Figure 3: home locations kernel density, 2011

Data used to identify job areas • onthemap.ces.census.gov/‎

DATA: METHODOLOGY • STUDY AREA IS SELECTED

DATA: METHODOLOGY • We used year 2011 for the analysis of job and labor force. • We chose all workers in the labor Market Segment. • All jobs have been analyzed for 2011 • all jobs in the public and private-sector jobs.

Data: • Can select the year • And job or residence side • Can specify whether the selection area is analyzed on where workers live ("Home") or where workers are employed ("Work").

Once you have data you need: Shelby county with the work locations, 2011.

Methodology A two-step approach was used. • 1.identified those census blocks meeting the following four conditions: • (1) jobs-to-worker (JWR) ratio is at least 1.25 (Forstall and Greene, 1994); • (2) job magnitude: at least 50 per US Census block (WAS 100). • (3) the adjacency requirement: to share an edge or a boundary • However, an area/s might be identified the potential job concentration/s, however, not sufficient number of jobs. • 2. a final 4thcriterion was arbitrarily selected: • the areas meeting the above 1-3 conditions • at least 500 jobs in total to qualify as an employment area. • Based on the job magnitude, employment areas can be grouped into centers (more than 10,000 jobs in total), subcenters (at least 1,000 jobs in total), and clusters (at least 500 jobs in total) (Antipova and Ozdenerol, 2013).

Comparisons of 2 methods • The method applied for job area identification in the current project seems to be a solid improvement over the method used previously. • 2 projects: different study areas, • previous study: Shelby County, • Current study: five-county area (Shelby County & surrounding areas) • we consistently compared the same Shelby County. • TN Dep’t of Labor and Workforce (2011): report on the labor market • insignificant job fluctuations January 2010 -January 2011 in Memphis MSA and the city of Memphis

Local Area Unemployment Statistics, Bureau of Labor Statistics Memphis, TN-MS-AR Metropolitan Statistical AreaMetropolitan areas Average employment fluctuation for the year is not significant Source: http://data.bls.gov/timeseries/LAUMT47328206?data_tool=XGtable

Comparisons of 2 methods • 45 employment areas in 2011 (5-county area) • 40 areas identified in 2010 (Shelby County). • 7% more jobs, or 83.44 % of all jobs available in Shelby County in 2011. • The previous method captured 76.09% of all jobs (Antipova and Ozdenerol, 2013). • The lowered criterion of 50 jobs per block was able to find employment areas which might have been missed if the higher number-based criterion of 100 jobs per block were to be used.

Table 1: employment areas identified in Shelby County, 2011.

Figure 4 presents job areas by category (that is, center, subcenter, and cluster) identified, 2011.

How good are data? • Example 1:

onthemapdata suggested that there are 903 jobs inside these blocks, Check by using point data from the the Business Analyst data

Method Verification: Businesses added

Method Verification: Businesses added (Business Analyst, based on data from InfoUSA) Businesses selected and number of jobs summed

Example 1 • onthemap data suggested that there are 903 jobs inside these blocks, • but the Business Analyst data gives the total of 1,303 • Is OK • Can be due to seasonal variation

Table 1: estimated nonfarm employment (in thousands) in Memphis MSA (TN - Fayette, Shelby, Tipton. AR - Crittenden. MS - DeSoto, Marshall, Tate, Tunica).

How good are data? • Example 2:

Do descriptive stats of the data: min, max,…

Other example • Search for this block • "STATEFP10" = '47' AND "COUNTYFP10" = '157' AND "c000" =18588

Identify all businesses inside this block: onthemap data suggested that there are 18,588 jobs inside this block, but the Business Analyst data only gives the total of 5,425 - The largest of all businesses is 5,000

Select an entire job area, and find all businesses inside it: onthemap data suggested that there are 32,717 jobs inside these blocks, but the Business Analyst data only gives the total of 13,026

Example 2 • onthemap data suggested that there are 32,717 jobs inside these blocks, • but the Business Analyst data only gives the total of 13,026 • Is not OK • Should exercise caution

The distribution of the jobs and workers across the 5-county study area: decentralization? • most employment can be found within 15 miles. • spikes, indicating clustering of jobs in these locations. • jobs spreading away from the CBD have a general tendency of decreasing, then sharply rise again • at 5 miles(the inner midtown area), and at 8-15 miles • the peak at 11 miles, capturing among all jobs found there most of the Aerotropolis • job decrease at 15 miles • suburbs begin: Germantown and Bartlett, and Millington, TN. • local suburban job concentrations • the number of resident workers (lighter square-patterned line) is much higher than the job opportunities Figure : the distribution of the employment and resident workers across the 5-county study area.

Job Decentralization? • Germantown and Bartlett are the largest suburban job locations, totaling 1,581 and 1,263 job facilities in 2010, • compare with 14,698 establishments in Memphis • Census data and Bureau of Labor Statistics to report the representative establishment by NAICS Code (industries whose share accounts for at least 10% in a place’s job structure): • Germantown: retail trade, finance and insurance, professional and scientific, and health care • Bartlett: construction, retail, and other services.

Other spikes in jobs are located at (1) 23 miles, (2) 35 miles, and (3) 38 miles away from the CBD, • indicate the presence of local, secondary suburban job locations. • 1st secondary spike at 23 miles, captures the jobs in Collierville, TN, • predominant white population of 28,643 out of the total of 31,872 persons, with the Black population accounting for just 2,337 people (7.3%). • Jobs found there form a job center with the total employment of 12,318 in 2011: retail and professional and scientific • Arlington, TN: construction, retail, and administrative support • Collierville: large job-producing area with 1,065 job establishments, • Arlington is the smallest with just 44 establishments.

THANK YOU! • ANY • FOR THE FUTURE RESEARCH? SUGGESTIONS QUESTIONS IDEAS

USE OF GIS IN JOB IDENTIFICATION