🏢 Workplace and School Location Results (wsLocResults.csv) ¶

Long-Term Location Choices

Workplace and school locations represent the fundamental spatial decisions that anchor daily travel patterns. These long-term choices drive mandatory travel demand and shape household activity patterns.

Data Verification

Documentation verified against 2015-tm22-dev-sprint-04 model run
✅ 17 fields confirmed in actual output files

🎯 Model Context ¶

🏭 Employment Modeling

Workplace location choice considers:

Job accessibility from home location
Industry type and employment categories
Wage levels and income constraints
Transportation costs and travel times

🎓 Education Modeling

School location choice incorporates:

School type (elementary, high school, university)
Capacity constraints at education facilities
Distance preferences for different age groups
Household transportation resources

🔗 Travel Integration

Location results feed into:

Model Component	Usage	Impact
Tour Generation	Mandatory tour origins/destinations	Work/school commute patterns
Mode Choice	Accessibility calculations	Transportation mode selection
Time-of-Day	Scheduling constraints	Peak period travel timing
Route Assignment	O-D matrix generation	Network loading patterns

📊 Market Segmentation ¶

💼 Employment Categories ¶

Worker Segmentation

Employment categories reflect industry types with distinct spatial patterns

Field	Type	Description	Example Values
`HHID`	INTEGER	Household identifier linking to household.csv	17483, 17493
`HomeMGRA`	INTEGER	Home location MGRA	Valid MGRA IDs
`Income`	INTEGER	Household income (dollars)	128323, 167807
`PersonID`	INTEGER	Person identifier linking to person.csv	35180, 35190
`PersonNum`	INTEGER	Person number within household	1, 2, 3, 4

Person Characteristics ¶

Field	Type	Description	Valid Values
`PersonType`	INTEGER	CTRAMP person type classification	1-8 (see person type dictionary)
`PersonAge`	INTEGER	Person age in years	0-120+
`EmploymentCategory`	INTEGER	Employment status category	1-4 (see employment dictionary)
`StudentCategory`	INTEGER	Student status category	1-4 (see student dictionary)

Market Segmentation ¶

Field	Type	Description	Valid Values
`WorkSegment`	INTEGER	Work location choice market segment	1-4, -1 for non-workers
`SchoolSegment`	INTEGER	School location choice market segment	1-3, -1 for non-students

Location Choice Results ¶

Workplace Location ¶

Field	Type	Description	Valid Values
`WorkLocation`	INTEGER	Chosen work location MGRA	Valid MGRA ID, 0 if no work location
`WorkLocationDistance`	REAL	Home to work distance (miles)	0.0+ for workers, 0.0 for non-workers
`WorkLocationLogsum`	REAL	Work location choice logsum	Real number, -999.0 for non-workers

School Location ¶

Field	Type	Description	Valid Values
`SchoolLocation`	INTEGER	Chosen school location MGRA	Valid MGRA ID, 0 if no school location
`SchoolLocationDistance`	REAL	Home to school distance (miles)	0.0+ for students, 0.0 for non-students
`SchoolLocationLogsum`	REAL	School location choice logsum	Real number, -999.0 for non-students

Data Dictionaries ¶

Person Type Classification (PersonType) ¶

Code	Person Type	Description	Work Location	School Location
1	Full-time worker	Adult with full-time employment	Yes	No
2	Part-time worker	Adult with part-time employment	Yes	No
3	University student	College/university student	Optional	Yes
4	Non-working adult	Adult not in labor force	No	No
5	Retired	Retired adult	No	No
6	Driving age student	Student 16-19 years old	No	Yes
7	Non-driving student	Student 6-15 years old	No	Yes
8	Preschooler	Child under 6 years old	No	No

Employment Category Classification (EmploymentCategory) ¶

Code	Category	Description	Work Assignment
1	Full-time employed	Full-time worker	Required work location
2	Part-time employed	Part-time worker	Required work location
3	Not employed	Not in labor force	No work location
4	Unemployed	Unemployed but seeking work	No work location

Student Category Classification (StudentCategory) ¶

Code	Category	Description	School Assignment
1	Pre-K	Preschool age	No school location
2	K-12 student	Elementary/middle/high school	Required school location
3	University student	College/university	Required school location
4	Non-student	Not enrolled	No school location

Work Location Market Segments (WorkSegment) ¶

Code	Segment	Description	Income Level	Industry Focus
1	Low-income worker	Lower income employment	Low	Service, retail
2	Mid-income worker	Middle income employment	Medium	Mixed sectors
3	High-income worker	Higher income employment	High	Professional, tech
4	Very high-income worker	Very high income employment	Very High	Executive, specialized
-1	Non-worker	No employment	N/A	N/A

School Location Market Segments (SchoolSegment) ¶

Code	Segment	Description	Age Range	Education Level
1	K-12 student	Elementary through high school	5-18	Primary/secondary
2	University undergraduate	College undergraduate	18-25	Higher education
3	University graduate	Graduate/professional school	22-35	Graduate education
-1	Non-student	Not enrolled in school	N/A	N/A

Location Choice Modeling ¶

Workplace Location Choice ¶

Model Structure: - Uses distance decay and employment accessibility measures - Considers industry-specific employment opportunities - Accounts for transportation costs and travel time - Includes household income and person characteristics

Key Variables: - Employment by industry type at each MGRA - Transportation cost (mode choice logsums) from home to work - Distance decay parameters by income segment - Regional accessibility measures

School Location Choice ¶

Model Structure: - Uses distance decay and school capacity constraints - Considers school type (K-12 vs university) availability - Accounts for transportation access and costs - May include school quality or preference factors

Key Variables: - School enrollment capacity at each MGRA - Transportation cost from home to school - Distance decay parameters by student type - Age-appropriate school availability

Data Quality and Validation ¶

Work Location Consistency ¶

-- Workers should have work locations
SELECT COUNT(*) FROM wsLocResults 
WHERE PersonType IN (1, 2) 
AND WorkLocation = 0;

-- Non-workers should not have work locations
SELECT COUNT(*) FROM wsLocResults
WHERE PersonType NOT IN (1, 2, 3) 
AND WorkLocation > 0;

-- Work distances should be reasonable
SELECT COUNT(*) FROM wsLocResults
WHERE WorkLocation > 0 
AND (WorkLocationDistance <= 0 OR WorkLocationDistance > 200);

School Location Consistency ¶

-- Students should have school locations
SELECT COUNT(*) FROM wsLocResults
WHERE PersonType IN (3, 6, 7)
AND SchoolLocation = 0;

-- Non-students should not have school locations  
SELECT COUNT(*) FROM wsLocResults
WHERE PersonType NOT IN (3, 6, 7)
AND SchoolLocation > 0;

-- School distances should be reasonable
SELECT COUNT(*) FROM wsLocResults
WHERE SchoolLocation > 0
AND (SchoolLocationDistance <= 0 OR SchoolLocationDistance > 100);

Segmentation Consistency ¶

-- Work segments should align with employment
SELECT COUNT(*) FROM wsLocResults
WHERE (EmploymentCategory IN (1, 2) AND WorkSegment = -1)
OR (EmploymentCategory IN (3, 4) AND WorkSegment != -1);

-- School segments should align with student status
SELECT COUNT(*) FROM wsLocResults  
WHERE (StudentCategory IN (2, 3) AND SchoolSegment = -1)
OR (StudentCategory IN (1, 4) AND SchoolSegment != -1);

Analysis Examples ¶

Commute Distance Analysis ¶

import pandas as pd
import numpy as np

# Load workplace location data
wsloc = pd.read_csv('wsLocResults.csv')

# Filter workers with work locations
workers = wsloc[(wsloc['PersonType'].isin([1, 2])) & (wsloc['WorkLocation'] > 0)]

# Commute distance statistics by person type
commute_stats = workers.groupby('PersonType')['WorkLocationDistance'].agg([
    'count', 'mean', 'median', 'std'
]).round(2)

print("Commute distance statistics:")
print(commute_stats)

# Distribution of work market segments
work_segments = workers['WorkSegment'].value_counts().sort_index()
print("\nWork market segment distribution:")
print(work_segments)

School Location Analysis ¶

-- School enrollment by distance bands
SELECT 
    CASE 
        WHEN SchoolLocationDistance <= 1 THEN '0-1 mile'
        WHEN SchoolLocationDistance <= 3 THEN '1-3 miles' 
        WHEN SchoolLocationDistance <= 5 THEN '3-5 miles'
        WHEN SchoolLocationDistance <= 10 THEN '5-10 miles'
        ELSE '10+ miles'
    END as distance_band,
    COUNT(*) as students,
    AVG(SchoolLocationDistance) as avg_distance
FROM wsLocResults 
WHERE SchoolLocation > 0
GROUP BY distance_band
ORDER BY avg_distance;

Accessibility Analysis ¶

# Analyze accessibility by market segment
accessibility = wsloc[wsloc['WorkLocation'] > 0].groupby('WorkSegment').agg({
    'WorkLocationLogsum': ['mean', 'median', 'std'],
    'WorkLocationDistance': ['mean', 'median']
}).round(3)

print("Work accessibility by market segment:")
print(accessibility)

# Income and commute relationship
income_commute = workers.groupby(pd.cut(workers['Income'], bins=5))['WorkLocationDistance'].mean()
print("\nAverage commute distance by income quintile:")
print(income_commute)

Spatial Distribution ¶

-- Top work destinations
SELECT 
    WorkLocation as mgra,
    COUNT(*) as workers,
    AVG(WorkLocationDistance) as avg_commute_distance,
    AVG(Income) as avg_worker_income
FROM wsLocResults
WHERE WorkLocation > 0
GROUP BY WorkLocation
HAVING COUNT(*) >= 10
ORDER BY workers DESC
LIMIT 20;

-- School concentration analysis
SELECT
    SchoolLocation as mgra,
    COUNT(*) as students, 
    AVG(SchoolLocationDistance) as avg_distance,
    COUNT(CASE WHEN PersonType = 3 THEN 1 END) as university_students,
    COUNT(CASE WHEN PersonType IN (6,7) THEN 1 END) as k12_students
FROM wsLocResults
WHERE SchoolLocation > 0  
GROUP BY SchoolLocation
HAVING COUNT(*) >= 5
ORDER BY students DESC;

Model Applications ¶

Land Use Planning ¶

Employment and school location patterns
Transportation infrastructure needs assessment
Regional accessibility evaluation

Transportation Planning ¶

Commute flow estimation for network assignment
Transit route planning based on work/school destinations
Mode choice model calibration and validation

Economic Analysis ¶

Labor market accessibility by location
Educational facility service area analysis
Regional economic impact assessment

Special Considerations ¶

Non-mandatory Locations ¶

File only contains mandatory (work/school) locations
Shopping, recreation, and other discretionary destinations modeled separately
Some university students may also have work locations

Model Sensitivity ¶

Location choice is sensitive to transportation network changes
Results reflect base year land use and employment data
Future year forecasting requires land use projections

Data Limitations ¶

Represents modeled locations, not observed behavior
May not capture all workplace flexibility (telework, multiple locations)
School choice constraints may be simplified compared to actual policies

This documentation is part of the CTRAMP Output Files specification suite.