Mobility is an important metric in modeling of community population dynamics and community resilience. It is directly associated with the inorganic changes in a community during and after a disruption (e.g., city gentrification, refugee migration from a war zone, flash mobs in an online community, etc.). Mobility is driven by socioeconomic, demographic, geographical, psychological, and legal parameters. Not all of these parameters are mutually independent (orthogonal). For proper modeling, it is important to avoid collinearity, as otherwise the model will not generalize well. We discuss how machine learning can be used to avoid it by identifying the mutually orthogonal metrics (factors)

1. Using Machine Learning to Identify
the Factors of People's Mobility
INFORMS2020
Alexander Gilgur
Jose Emmanuel Ramirez-Marquez
The research performed by Jose E. Ramirez Marquez leading to these results has received funding from the National Science Foundation, CRISP Type 2 /
Collaborative Research: Resilience Analytics: A Data-Driven Approach for Enhanced Interdependent Network Resilience, Award number 1541165.

2. Motivation and Problem Statement
Migration is one of the key factors of population growth in US counties.
Modeling it enables planning local infrastructure and its supply chains.
2

3. Why do people pick up and leave and
go to the new places?
● Opportunities & Employment
● Infrastructure
● Affordability
● Stability
● Happiness & Unhappiness
● ... ... ...
Migration is one of the key factors of population growth in US counties.
Modeling it enables planning local infrastructure and its supply chains.
Motivation and Problem Statement
3

4. Modeling the effects of migration factors is complicated
● Confounded Causality
● Multicollinearity
● Nonlinear relationships
● Differences between Domestic & International migration
Migration is one of the key factors of population growth in US counties.
Modeling it enables planning local infrastructure and its supply chains.
Motivation and Problem Statement
4
Why do people pick up and leave and
go to the new places?
● Opportunities & Employment
● Infrastructure
● Affordability
● Stability
● Happiness & Unhappiness
● ... ... ...

5. Modeling the effects of migration factors is complicated
● Confounded Causality
● Multicollinearity
● Nonlinear relationships
● Differences between Domestic & International migration
We present a way to identify the factors that have
an effect on domestic, international, and overall
migration as first step to modeling migration
Migration is one of the key factors of population growth in US counties.
Modeling it enables planning local infrastructure and its supply chains.
Motivation and Problem Statement
5
Why do people pick up and leave and
go to the new places?
● Opportunities & Employment
● Infrastructure
● Affordability
● Stability
● Happiness & Unhappiness
● ... ... ...

6. P.C.
Importance
R.F.R.
Variance
Contribution
%
P.C.A.
Workflow
E.D.A.
6
Mobility
Factors

7. Data Collection
1. Community Commons Map
2. Bureau of Labor Statistics Employment Data
3. Census Bureau Income Inequality Data
4. Census Population Data
5. CDC Cause of Death Data: Suicide Mortality
6. USDA Economic Research Service Education Data
Government APIs are not very easy to use
Data were downloaded as Excel or CSV files and saved to Dropbox
They were later preprocessed for analysis
7

8. EDA: Target Variables’ Correlation Matrix
0: NETMIG
1: DOMESTICMIG
2: INTERNATIONALMIG
0: year
1: BIRTHS
2: CENSUSPOP
3: DEATHS
4: EMPLOYED
5: ESTIMATESBASE
6: GDP
7: GDP_LOG
8: LABOR_FORCE
9: NATURALINC
10: NPOPCHG_
11: POPESTIMATE
12: POP_CDC
13: RBIRTH
14: RDEATH
15: RNATURALINC
16: SCDEATHS
17: SCRATE
18: SC_R_DEATH
19: UNEMPLOYED
20: UNEMPLOYMENT_RATE_PCT
21: bachelors_degree_plus
22: high_school_diploma
23: lt_high_school_diploma
24: mean_income__dollars
25: mean_to_median_income_ratio
26: median_income__dollars
27: pct__bachelors_degree_plus
28: pct__high_school_diploma
29: pct__lt_high_school_diploma
30: pct__some_college_or_assoc_degree
31: some_college_or_assoc_degree
8

9. EDA: Target Variables’ Correlation Matrix
0: NETMIG
1: DOMESTICMIG
2: INTERNATIONALMIG
0: year
1: BIRTHS
2: CENSUSPOP
3: DEATHS
4: EMPLOYED
5: ESTIMATESBASE
6: GDP
7: GDP_LOG
8: LABOR_FORCE
9: NATURALINC
10: NPOPCHG_
11: POPESTIMATE
12: POP_CDC
13: RBIRTH
14: RDEATH
15: RNATURALINC
16: SCDEATHS
17: SCRATE
18: SC_R_DEATH
19: UNEMPLOYED
20: UNEMPLOYMENT_RATE_PCT
21: bachelors_degree_plus
22: high_school_diploma
23: lt_high_school_diploma
24: mean_income__dollars
25: mean_to_median_income_ratio
26: median_income__dollars
27: pct__bachelors_degree_plus
28: pct__high_school_diploma
29: pct__lt_high_school_diploma
30: pct__some_college_or_assoc_degree
31: some_college_or_assoc_degree
USCB models population by migrations, births, and
deaths
=> collinearity & data leakage
Factors positively correlated with international
migration are negatively correlated with domestic
migration
=> low correlation with overall migration
These variables are not all independent; we need to make them such.
9

10. Making Variables Independent: Principal Component Analysis
PCA finds the linear combinations (principal components, or PCs) of original variables that maximize
the variances of the principal components
This results in covariance being 0 => principal components are independent.
10

11. Principal Component Analysis and Dimensionality Reduction
Problem - thresholds are arbitrary
EV Threshold = 0.01
25 PCs 13 PCs
EV Threshold = 0.05
5 PCs
11

12. Dimensionality Reduction: Random Forest Regression
Random Forest Regression (RFR)
is one of the most robust methods
for modeling multidimensional
data. It builds a forest of
numerical decision trees and
ensembles the decision paths from
all trees by majority voting.
It is non-parametric => it does not
rely on knowledge of an underlying
model.
We can calculate feature importance
for any model, enabling
dimensionality reduction by
identifying the critical features for
further analysis.
Due to the way RFR operates,
feature importance evaluation based
on RFR is model-agnostic, ensuring
that if further modeling fails to
identify a feature as important, we
can rely on RFR-based feature
importance for model validation.
12

13. EDA:
● Identify Correlated Features
PCA:
● Combine Features into PCs
RFR:
● Identify Important PCs
Features
Principal Components
The EDA to PCA to RFR Daisy Chain
13

14. EDA:
● Identify Correlated Features
PCA:
● Combine Features into PCs
RFR:
● Identify Important PCs
Features
Principal Components
Ytest
PredictiononXtest
R2
test
= 0.78
The EDA to PCA to RFR Daisy Chain
14

15. EDA:
● Identify Correlated Features
PCA:
● Combine Features into PCs
RFR:
● Identify Important PCs
Features
Principal Components
Ytest
PredictiononXtest
R2
test
= 0.78
Important
Features
Ytest
PredictiononXtest
R2
test
= 0.78
R2
stabilized
?
The EDA to PCA to RFR Daisy Chain
15

16. The EDA to PCA to RFR Daisy Chain
EDA:
● Identify Correlated Features
PCA:
● Combine Features into PCs
RFR:
● Identify Important PCs
Features
Principal Components
Ytest
PredictiononXtest
R2
test
= 0.78
Important
Features
PC Importances
Principal Component
PC Importance
R2
stabilized
?
16

17. EDA:
● Identify Correlated Features
PCA:
● Combine Features into PCs
RFR:
● Identify Important PCs
Features
Principal Components
Ytest
PredictiononXtest
R2
test
= 0.78
Important
Features
PC Importances
Principal Component
PC Importance
R2
stabilized
?

18. Going Back to Original Features
18
We have come full circle:
1) Identified the correlated variables
2) Applied PCA to rotate the data into a form where principal components are orthogonal.
3) Used Random-Forest Regression (RFR) to perform final PC selection based on their
importance.
Now we need to
4) Transform the selected PCs back to the original features.
5) Use these features in modeling.

19. Further Work
19
● With the features identified by the PCA->RFR process, build the model of migration in the form of
Poiseuille's Equation:
● Infer the pressure and the resistance terms and model them based on the features:
○ Discontent
○ Socioeconomic data
○ GDP
○ Education level
○ Population volume
● Build a model predicting movements of large masses of population

20. Conclusions
20
● Mass migration is a high complexity multivariate problem
● Its features interact, overlap, and exhibit multicollinearity in many ways
● Properly modeling mass migrations requires understanding of the underlying processes, which is
impossible to achieve without dimensionality reduction
● We have described how this can be achieved based on publicly available data and using the
standard Machine-Learning techniques in a creative manner.

21. Thank You!
21

22. Appendix
22

23. Emigration: Mass Transfer Out
Information
about other
places
Possibility
of “Flight”
Affordability
of “Flight”
“Flight”
Discontent
High
Stress
Levels
Navier-Stokes Equation:
Poiseuille’s Equation:
-------
Effluent rate is proportional to
pressure difference and inversely
proportional to resistance 23

24. Information
about other
places
Possibility
of “Flight”
Affordability
of “Flight”
“Flight”
Immigration: Diffusion into New Host Community
Affordability
of Living
Media
Sentiment
New Host
Community Acceptance
of Strangers
24

25. Absorption of Community Characteristics
characteristics of the incoming community
Newcomers bring with them features that can be new to the host community. As time passes by, they retain fewer of
such characteristics, while members of the community that has adopted them become penetrated with the new
features. Likewise, features of the host community become characteristics of its new members.
In the new members of
the community
In the members of
the host community
Time
Saturation
Time
Saturation
characteristics of the host community
In the members of
the host community
In the new members of
the community
(a) (b)
25