Population and community report

Region: Florida

Report generated: 2021-06-11




https://www.replicahq.com

support@replicahq.com

Table of contents

About this report

Replica uses aggregate census data to generate a full scale synthetic resident population of the region with accurate demographic characteristics.
This report details the status of Replica data relative to the census ground truth data.

Resident population by county and census tract

How many members of our synthetic population reside in each county and census tract, compared to census data.
Note: these comparisons ignore group quarters.
Comparison by County of how many households exist in our model vs. how many households the census reports.
Comparison by County of how many how many people exist in our model vs. how many people the census reports.
Choropleth with map overlay of how our population numbers compare to the census data by county.
Comparison by tract of how many households exist in our model vs. how many households the census reports.
Comparison by tract of how many people exist in our model vs. how many people the census reports.
Choropleth with map overlay of how our population numbers compare to the census data by tract.
Error Bound ✓
Bucket
By Tract 0.01 0.05 ✓
By County 0.00 0.05 ✓

Household size

This section explores the various household sizes in Replica's synthetic population. 'Group quarters' is a census designation referring to households made up of many individual units, such as a dorms, barracks, or prisons. For more information please see the census description: https://www.census.gov/programs-surveys/popest/about/glossary/housing.html
For each household size, a comparison by tract of how many households exist of that size in our model vs. the census data. Followed by all the household sizes in one chart.
Error Bound Percent within MOE ✓
Bucket
1_person_group_quarters 0.01 0.05 1.00 ✓
1_person 0.01 0.05 1.00 ✓
2_person 0.00 0.05 1.00 ✓
3_person 0.01 0.05 1.00 ✓
4_person 0.02 0.05 1.00 ✓
5_person 0.06 0.05 1.00
6_person 0.16 0.05 0.98
7_plus_person 0.30 0.05 0.79
Total 0.02 0.05 0.97 ✓

Household income

This section shows the distribution of household income brackets in our synthetic population.
For each household income range, a comparison by tract of how many households fall in that income range in our model vs. the census data.
Error Bound Percent within MOE ✓
Bucket
lte_10000 0.02 0.05 1.00 ✓
10000_40000 0.01 0.05 1.00 ✓
40000_75000 0.01 0.05 1.00 ✓
75000_125000 0.02 0.05 1.00 ✓
125000_plus 0.02 0.05 1.00 ✓
Total 0.02 0.05 1.00 ✓

Age

This section explores the ages of people in our synthetic population. Due to legal restrictions Replica sources do not include any mobile location data from minors.
For each age range, a comparison by tract of how many people exist for that age range in our model vs. the census data.
Error Bound Percent within MOE ✓
Bucket
lte_4 0.01 0.05 1.00 ✓
5_14 0.00 0.05 1.00 ✓
15_17 0.01 0.05 1.00 ✓
18_24 0.00 0.05 1.00 ✓
25_34 0.00 0.05 1.00 ✓
35_64 0.00 0.05 1.00 ✓
65_plus 0.00 0.05 1.00 ✓
Total 0.00 0.05 1.00 ✓

Sex

This section explores the distribution of sex of people in our synthetic population.
For each sex, a comparison by tract of how many people of that sex exist in our model vs. the census data.
Error Bound Percent within MOE ✓
Bucket
M 0.00 0.05 1.00 ✓
F 0.00 0.05 1.00 ✓
Total 0.00 0.05 1.00 ✓

Employment

This section looks at employed and unemployed members of our synthetic population.
A comparison by tract of how many people who live there are employed/unemployed in our model vs. the census data. Followed by both employment statuses in one chart.
Error Bound Percent within MOE ✓
Bucket
employed 0.01 0.05 1.00 ✓
unemployed 0.03 0.05 1.00 ✓
notinlf 0.00 0.05 1.00 ✓
under_16 0.01 0.05 1.00 ✓
Total 0.01 0.05 1.00 ✓

Vehicles

This section explores the various numbers of vehicles to which each household has access. 'Vehicles = GQ' refers to group quarters households, which have no census vehicle data (see above).
For different numbers of vehicles, a comparison by tract of how many households have that many vehicles in our model vs. the census data. Followed by all the vehicle categories in one chart.
Error Bound Percent within MOE ✓
Bucket
GQ 0.01 0.05 1.00 ✓
zero 0.03 0.05 1.00 ✓
1 0.01 0.05 1.00 ✓
2 0.01 0.05 1.00 ✓
3_plus 0.07 0.05 0.97
Total 0.02 0.05 0.99 ✓

School enrollment

This section looks at the school enrollment of our synthetic student population. A comparison by tract of how many students are enrolled in each type of school in our model vs. the census data. Followed by all school types in one chart.
Error Bound ✓
Bucket
graduate 0.01 0.05 ✓
private 0.02 0.05 ✓
public 0.01 0.05 ✓
undergraduate 0.01 0.05 ✓

LEHD

This section compares the total number of people working and workers residing within a Census block group as compared to the Longitudinal Employer-Households Dynamics (LEHD) census data product. Considerable differences among Census numbers can arise in multiple areas due to LEHD definition of the usual place of work (such as aggregations by an administrative address of the employer vs the physical place of work of an employee, etc.) and data collection and reporting particularities.
Note: Error and Bound numbers for this section are 1 - p, where p is the correlation coefficient.
Error Bound ✓
Category Subcategory Bucket
LEHD LEHD jobs Top 10% of tracts 0.14 0.05
All Tracts 0.09 nan NA

Origin-Destination Flows

This section compares overall county-to-county work commute characteristics against Census Transportation Planning Products (CTPP) data. Replica models all trips by residents of the core modeling region as well as trips by residents of the extended region who work in the core.
Note: Replica counts are scaled so that the total number of trips matches the CTPP, in order to account for discrepancies between average daily travel patterns and self-reported survey responses.
CTPP movements, Figure 1: Commute flows by mode: tract-to-tract for large tracts (top) and county-to-county
CTPP movements, Figure 2: Commute flows by industry of employment : county-to-county
CTPP movements, Figure 3: Total commute flows : tract-to-tract for large tracts (top) and county-to-county
Note: Error and Bound numbers for this section are 1 - p, where p is the correlation coefficient.
Error Bound ✓
Subcategory Bucket
By Mode Tract-to-Tract 0.21 0.20 ≈
County-to-County 0.00 0.10 ✓
By Industry County-to-County 0.01 0.10 ✓
Total Tract-to-Tract 0.17 0.20 ✓
County-to-County 0.00 0.10 ✓

Acceptance criteria

A summary of how well we've met the acceptance criteria (error bounds) for each category above.
Error Bound ✓
Category Subcategory Bucket
Population Count By Tract 0.01 0.05 ✓
By County 0.00 0.05 ✓
Household Size 1_person_group_quarters 0.01 0.05 ✓
1_person 0.01 0.05 ✓
2_person 0.00 0.05 ✓
3_person 0.01 0.05 ✓
4_person 0.02 0.05 ✓
5_person 0.06 0.05
6_person 0.16 0.05
7_plus_person 0.30 0.05
Total 0.02 0.05 ✓
Household Income Group lte_10000 0.02 0.05 ✓
10000_40000 0.01 0.05 ✓
40000_75000 0.01 0.05 ✓
75000_125000 0.02 0.05 ✓
125000_plus 0.02 0.05 ✓
Total 0.02 0.05 ✓
Age Group lte_4 0.01 0.05 ✓
5_14 0.00 0.05 ✓
15_17 0.01 0.05 ✓
18_24 0.00 0.05 ✓
25_34 0.00 0.05 ✓
35_64 0.00 0.05 ✓
65_plus 0.00 0.05 ✓
Total 0.00 0.05 ✓
Sex M 0.00 0.05 ✓
F 0.00 0.05 ✓
Total 0.00 0.05 ✓
Employment employed 0.01 0.05 ✓
unemployed 0.03 0.05 ✓
notinlf 0.00 0.05 ✓
under_16 0.01 0.05 ✓
Total 0.01 0.05 ✓
Vehicles GQ 0.01 0.05 ✓
zero 0.03 0.05 ✓
1 0.01 0.05 ✓
2 0.01 0.05 ✓
3_plus 0.07 0.05
Total 0.02 0.05 ✓
School enrollment graduate 0.01 0.05 ✓
private 0.02 0.05 ✓
public 0.01 0.05 ✓
undergraduate 0.01 0.05 ✓
LEHD LEHD jobs Top 10% of tracts 0.14 0.05
All Tracts 0.09 nan NA
Origin-Destination Flows By Mode Tract-to-Tract 0.21 0.20 ≈
County-to-County 0.00 0.10 ✓
By Industry County-to-County 0.01 0.10 ✓
Total Tract-to-Tract 0.17 0.20 ✓
County-to-County 0.00 0.10 ✓

Additional metrics

School grade

This section explores the school grades attended by Replica's synthetic population. The grade level can be one of: 'kindergarten', 'school' (grades 1-12), 'undergraduate', 'graduate', or 'not_attending_school'. For each grade level, a comparison by tract of how many people attend that level in our model vs. the census data. Followed by all the grade levels in one chart.
Error Bound Percent within MOE ✓
Bucket
graduate 0.06 nan 0.98 NA
kindergarten 0.06 nan 0.99 NA
not_attending_school 0.01 nan 1.00 NA
school 0.01 nan 1.00 NA
undergraduate 0.04 nan 0.99 NA
Total 0.01 nan 0.99 NA

Household tenure

This section explores the various household tenures in Replica's synthetic population. Tenure can be one of: 'Owner', 'Renter', or 'Group Quarters'. For each household tenure type, a comparison by tract of how many households exist of that type in our model vs. the census data. Followed by all the household sizes in one chart.
Error Bound Percent within MOE ✓
Bucket
GQ 0.01 nan 1.00 NA
owner 0.02 nan 0.96 NA
renter 0.04 nan 0.98 NA
Total 0.03 nan 0.98 NA

Ethnicity

This section explores the ethnicities of our synthetic population.
For each ethnicity, a comparison by tract of how many people exist for that ethnicity in our model vs. the census data. Followed by all the ethnicities in one chart.
Error Bound Percent within MOE ✓
Bucket
hispanic_or_latino 0.04 nan 0.94 NA
not_hispanic_or_latino 0.01 nan 0.99 NA
Total 0.02 nan 0.96 NA

Commute mode

This section looks at the various commute modes among our synthetic population. A person's commute mode is his/her primary method of getting to work.
For each commute mode, a comparison by tract of how many people primarily use that commute mode in our model vs. the census data.
Error Bound Percent within MOE ✓
Bucket
biking 0.05 nan 1.00 NA
carpool 0.02 nan 1.00 NA
driving 0.00 nan 1.00 NA
not_working 0.00 nan 1.00 NA
transit 0.04 nan 1.00 NA
walking 0.05 nan 1.00 NA
worked_from_home 0.03 nan 1.00 NA
Total 0.01 nan 1.00 NA

Race

This section explores the distribution of races by census tract in our synthetic population.
For each race, a comparison by tract of how many people of that race exist in our model vs. the census data. Followed by all the races in one chart.
Error Bound Percent within MOE ✓
Bucket
american_indian_alaska_native 0.14 nan 0.98 NA
asian 0.10 nan 0.89 NA
black_african_american 0.06 nan 0.91 NA
hawaiian_pacific 0.15 nan 0.99 NA
other_race_alone 0.09 nan 0.93 NA
two_or_more_races 0.08 nan 0.95 NA
white 0.02 nan 0.97 NA
Total 0.03 nan 0.95 NA

Industry type

This section looks at employment industry types of our synthetic population.
A comparison of how many people of a given employment industry type live/work in each tract/county, as compared with ACS/LEHD. See https://en.wikipedia.org/wiki/North_American_Industry_Classification_System#Codes for descriptions of each NAICS sector code.
For each industry, a comparison by tract of how many people work in that industry in our model vs. the census data.
Industry by Home Tract (ACS)
Industry by Home County (LEHD)
Industry by Work Tract (LEHD)
Industry by Work County (LEHD)

Building type

This section looks at building types of our synthetic population.
For each building type, a comparison by tract of how many buildings of that type exist in our model vs. the census data. Followed by all the building types in one chart.
Error Bound Percent within MOE ✓
Bucket
mobile 0.49 nan 0.43 NA
multiple_units 0.35 nan 0.38 NA
several_units 0.34 nan 0.58 NA
single_family 0.12 nan 0.37 NA
Total 0.21 nan 0.44 NA

Additional metrics summary

Error Bound ✓
Subcategory Bucket
School Grade Attending graduate 0.06 nan NA
kindergarten 0.06 nan NA
not_attending_school 0.01 nan NA
school 0.01 nan NA
undergraduate 0.04 nan NA
Total 0.01 nan NA
Tenure GQ 0.01 nan NA
owner 0.02 nan NA
renter 0.04 nan NA
Total 0.03 nan NA
Ethnicity hispanic_or_latino 0.04 nan NA
not_hispanic_or_latino 0.01 nan NA
Total 0.02 nan NA
Commute Mode biking 0.05 nan NA
carpool 0.02 nan NA
driving 0.00 nan NA
not_working 0.00 nan NA
transit 0.04 nan NA
walking 0.05 nan NA
worked_from_home 0.03 nan NA
Total 0.01 nan NA
Race american_indian_alaska_native 0.14 nan NA
asian 0.10 nan NA
black_african_american 0.06 nan NA
hawaiian_pacific 0.15 nan NA
other_race_alone 0.09 nan NA
two_or_more_races 0.08 nan NA
white 0.02 nan NA
Total 0.03 nan NA
Industry naics11 0.11 nan NA
naics21 0.16 nan NA
naics22 0.09 nan NA
naics23 0.05 nan NA
naics31_33 0.06 nan NA
naics42 0.07 nan NA
naics44_45 0.05 nan NA
naics48_49 0.06 nan NA
naics51 0.07 nan NA
naics52 0.05 nan NA
naics53 0.06 nan NA
naics54 0.05 nan NA
naics55 0.10 nan NA
naics56 0.06 nan NA
naics61 0.05 nan NA
naics62 0.03 nan NA
naics71 0.07 nan NA
naics72 0.06 nan NA
naics81 0.07 nan NA
naics92 0.05 nan NA
Total 0.05 nan NA
Industry Lehd naics11 0.21 nan NA
naics21 0.36 nan NA
naics22 0.30 nan NA
naics23 0.15 nan NA
naics31_33 0.14 nan NA
naics42 0.22 nan NA
naics44_45 0.09 nan NA
naics48_49 0.21 nan NA
naics51 0.15 nan NA
naics52 0.14 nan NA
naics53 0.17 nan NA
naics54 0.12 nan NA
naics55 5.48 nan NA
naics56 0.16 nan NA
naics61 0.08 nan NA
naics62 0.07 nan NA
naics71 0.23 nan NA
naics72 0.10 nan NA
naics81 0.24 nan NA
naics92 0.12 nan NA
Total 0.14 nan NA
Industry By Work Lehd naics11 0.22 nan NA
naics21 0.36 nan NA
naics22 0.29 nan NA
naics23 0.13 nan NA
naics31_33 0.12 nan NA
naics42 0.21 nan NA
naics44_45 0.05 nan NA
naics48_49 0.19 nan NA
naics51 0.10 nan NA
naics52 0.09 nan NA
naics53 0.15 nan NA
naics54 0.07 nan NA
naics55 5.98 nan NA
naics56 0.14 nan NA
naics61 0.08 nan NA
naics62 0.04 nan NA
naics71 0.16 nan NA
naics72 0.11 nan NA
naics81 0.22 nan NA
naics92 0.11 nan NA
Total 0.11 nan NA
Building Type mobile 0.49 nan NA
multiple_units 0.35 nan NA
several_units 0.34 nan NA
single_family 0.12 nan NA
Total 0.21 nan NA