Land Use Data 🏙️

Overview

Land use data provides the spatial distribution of households, population, employment, and other activities that generate and attract travel. The travel model uses two levels of spatial detail: Traffic Analysis Zones (TAZ) for regional analysis and Micro Analysis Zones (MAZ) for detailed local accessibility and mode choice modeling.

Creating Land Use Files

For detailed instructions on how to prepare land use data files for the base year, see Creating Base Year Inputs 🏙️

File Structure

Land use data consists of two main files located in the landuse\ directory:

• mazData.csv - Micro Analysis Zone level data (detailed land use characteristics)
• tazData.csv - Traffic Analysis Zone level data (regional characteristics)

Micro Analysis Zones (MAZ Data)

The mazData.csv file contains detailed land use characteristics at the micro-zone level, providing the fine-grained spatial detail needed for accessibility calculations and local travel modeling.

Geographic and Administrative Fields

Column Name	Description	Used by	Source
MAZ_ORIGINAL	Original micro zone number (renumbered during model run)	MgraDataManager	Zone system definition
TAZ_ORIGINAL	Original TAZ number (renumbered during model run)	MgraDataManager	Zone system definition
CountyID	County ID number	MgraDataManager	Administrative boundaries
CountyName	County name string		Administrative boundaries
DistID	District ID number	TourModeChoice.xls	District system definition
DistName	District name		District system definition
ACRES	MAZ area in acres	createMazDensityFile.py	Calculated from shapefile

Population and Household Data

Column Name	Description	Used by	Source
HH	Total number of households	MgraDataManager	PopulationSim allocation
POP	Total population	MgraDataManager	PopulationSim allocation

Employment by Industry Category

The model uses detailed employment data by industry sector based on NAICS codes:

Core Industries

Column Name	Description	NAICS Codes	Used by
ag	Agriculture, forestry, fishing	11	Accessibilities
const	Construction	23	Accessibilities
natres	Mining and resource extraction	21	Accessibilities
util	Utilities	22	Accessibilities

Manufacturing

Column Name	Description	NAICS Codes	Used by
man_lgt	Light manufacturing	31-33 subset	Accessibilities
man_hvy	Heavy manufacturing	31-33 subset	Accessibilities
man_tech	High-tech manufacturing	334	Accessibilities
man_bio	Biological/drug manufacturing	325411, 325412, 325313, 325414	Accessibilities

Trade, Transportation, and Utilities

Column Name	Description	NAICS Codes	Used by
ret_loc	Local-serving retail	444130, 444190, 444210, 444220, 445110, 445120, 445210, 445220, 445230, 445291, 445292, 445299, 445310, 446110, 446120, 446130, 446191, 446199, 447110, 447190, 448110, 448120, 448130, 448140, 448150, 448190, 448210, 448310, 448320, 451110, 451120, 451130, 451140, 451211, 451212, 452910, 452990, 453110, 453220, 453310, 453910, 453920, 453930, 453991, 453998, 454111, 454112, 454113	Accessibilities
ret_reg	Regional retail	441110, 441120, 441210, 441222, 441228, 441310, 441320, 442110, 442210, 442291, 442299, 443141, 443142, 444110, 444120, 452111, 452112, 453210, 454210, 454310, 454390	Accessibilities
transp	Transportation	48 (most), 49 (excluding logistics)	Accessibilities
logis	Logistics/warehousing and distribution	42, 493	Accessibilities

Information and Professional Services

Column Name	Description	NAICS Codes	Used by
info	Information-based services	51	Accessibilities
fire	Finance, insurance, real estate	52, 53 (excluding leasing)	Accessibilities
lease	Leasing	532	Accessibilities
prof	Professional and technical services	54	Accessibilities

Education and Health Services

Column Name	Description	NAICS Codes	Used by
ed_k12	K-12 schools	6111	Accessibilities
ed_high	Junior colleges, colleges, universities	6112, 6113, 6114, 6115	Accessibilities
ed_oth	Other schools, libraries, educational services	6116, 6117	Accessibilities
health	Health care	62 (excluding social services)	Accessibilities

Leisure, Hospitality, and Other Services

Column Name	Description	NAICS Codes	Used by
art_rec	Arts, entertainment, recreation	71	Accessibilities
hotel	Hotels and other accommodations	721	Accessibilities
eat	Food services and drinking places	722	Accessibilities
serv_pers	Personal and other services	53, 81	Accessibilities
serv_bus	Managerial, administrative, business services	55, 56	Accessibilities
serv_soc	Social services and childcare	624	Accessibilities

Government and Other

Column Name	Description	NAICS Codes	Used by
gov	Government	92	Accessibilities
unclass	Employment not classified	N/A
emp_total	Total employment	Sum of all categories	Accessibilities

School Enrollment Data

Column Name	Description	Used by	Source
EnrollGradeKto8	Elementary school (K-8) enrollment	MgraDataManager	School district data
EnrollGrade9to12	High school (9-12) enrollment	MgraDataManager	School district data
collegeEnroll	Major college enrollment	MgraDataManager	Higher education institutions
otherCollegeEnroll	Other college enrollment	MgraDataManager	Community colleges, trade schools
AdultSchEnrl	Adult school enrollment	MgraDataManager	Continuing education programs
ech_dist	Elementary school district	MgraDataManager	School district boundaries
hch_dist	High school district	MgraDataManager	School district boundaries

Parking Data

Detailed parking supply and cost information for mode choice modeling:

Column Name	Description	Used by	Source
parkarea	Parking area type (see codes below)	MgraDataManager	Parking inventory
hstallsoth	Hourly stalls for trips to other MAZs	MgraDataManager	Parking inventory
hstallssam	Hourly stalls for trips within same MAZ	MgraDataManager	Parking inventory
hparkcost	Average hourly parking cost (dollars)	MgraDataManager	Parking fee data
numfreehrs	Hours of free parking before charges begin	MgraDataManager	Parking regulations
dstallsoth	Daily stalls for trips to other MAZs	MgraDataManager	Parking inventory
dstallssam	Daily stalls for trips within same MAZ	MgraDataManager	Parking inventory
dparkcost	Average daily parking cost (dollars)	MgraDataManager	Parking fee data
mstallsoth	Monthly stalls for trips to other MAZs	MgraDataManager	Parking inventory
mstallssam	Monthly stalls for trips within same MAZ	MgraDataManager	Parking inventory
mparkcost	Monthly parking cost amortized over 22 workdays	MgraDataManager	Parking fee data

Parking Area Type Codes (parkarea)

Code	Description
1	Downtown: trips may park in different MAZ, charges apply
2	Downtown buffer: quarter-mile buffer, charges might apply
3	Outside downtown paid: only destination trips park here, charges apply
4	Outside downtown free: only destination trips park here, no charges

Built Environment and Density Measures

Column Name	Description	Used by	Source
TotInt	Total intersections	MgraDataManager, AutoOwnership	createMazDensityFile.py
DUDen	Dwelling unit density	MgraDataManager	createMazDensityFile.py
EmpDen	Employment density	MgraDataManager	createMazDensityFile.py
PopDen	Population density		createMazDensityFile.py

Traffic Analysis Zones (TAZ Data)

The tazData.csv file contains zone-level data used for specific model components, particularly the transponder ownership model.

Required Fields

Column Name	Description	Used by	Source
TAZ_ORIGINAL	Original TAZ number (renumbered during model run)		Zone system definition
AVGTTS	Average travel time savings for transponder ownership	TazDataManager	Highway network analysis
DIST	Distance for transponder ownership model	TazDataManager	Highway network analysis
PCTDETOUR	Percent detour for transponder ownership model	TazDataManager	Highway network analysis
TERMINALTIME	Terminal time	TazDataManager	Highway network analysis

Data Integration and Processing

Zone System Coordination

• MAZ to TAZ Mapping: Each MAZ must be assigned to exactly one TAZ
• Numbering Convention: Original numbers preserved, but model renumbers zones during execution
• Consistency Checks: Population and employment totals must be consistent between MAZ and TAZ levels

Employment Allocation

1. Industry Classification: Employment data classified by detailed NAICS codes
2. Spatial Distribution: Employment allocated to MAZ level for accessibility calculations
3. Validation: Total employment should match regional control totals
4. Special Generators: Major employers (airports, universities) require special treatment

Density Calculations

Density measures calculated using createMazDensityFile.py:

• Dwelling Unit Density: Households per acre
• Employment Density: Jobs per acre
• Population Density: Persons per acre
• Intersection Density: Total intersections (walkability measure)

Model Applications

Accessibility Calculations

Land use data drives accessibility calculations used throughout the model:

• Employment Accessibility: By industry sector for location choice
• Population Accessibility: For service and retail accessibility
• Education Accessibility: For school location choice
• Mixed-Use Measures: Combined residential/commercial accessibility

Mode Choice Integration

• Parking Supply: Available spaces by type and duration
• Parking Costs: Hourly, daily, and monthly rates
• Built Environment: Density measures for walk/bike mode choice
• Activity Density: Combined employment and population measures

Location Choice Models

• Work Location: Industry-specific employment accessibility
• School Location: Enrollment and capacity by education level
• Non-Mandatory Activities: Retail, service, and recreational accessibility

Data Quality Requirements

Validation Checks

1. Completeness: No missing values in required fields
2. Consistency: Employment totals match across classification levels
3. Geographic Integrity: All MAZ assigned to valid TAZ
4. Logical Relationships: Enrollment consistent with education employment
5. Density Calculations: Consistent with zone area measurements

Common Issues

• Missing Employment: Zones with population but no employment data
• Inconsistent Totals: MAZ totals not matching TAZ aggregations
• Parking Data Gaps: Missing parking supply or cost information
• Enrollment Mismatches: School enrollment not aligned with capacity
• Density Anomalies: Unrealistic density calculations

Update Procedures

1. Base Year Preparation: Align with most recent Census/survey data
2. Forecast Year Development: Apply land use forecasts and development scenarios
3. Validation Process: Compare against observed patterns and trends
4. Sensitivity Testing: Verify model response to land use changes
5. Documentation: Maintain metadata and processing documentation

This comprehensive land use data structure supports detailed spatial analysis and realistic travel behavior modeling in the CT-RAMP framework.