book

Public Policy Analytics

Name: Public Policy Analytics
Author: Ken Steif
ISBN: 9781000401615

by Ken Steif

August 2021

Beginner to intermediate

228 pages

5h 56m

English

CRC Press

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Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
About the Author
Preface
Introduction
How governments make decisionsContext as the foundationData science as a planning toolThe importance of spatial thinkingLearning objectives
1 Indicators for Transit-oriented Development
1.1 Why start with indicators?1.1.1 Mapping and scale bias in areal aggregate data1.2 Set up1.2.1 Downloading and wrangling census data1.2.2 Wrangling transit open data1.2.3 Relating tracts and subway stops in space1.3 Developing TOD indicators1.3.1 TOD indicator maps1.3.2 TOD indicator tables1.3.3 TOD indicator plots1.4 Capturing three submarkets of interest1.5 Conclusion: Are Philadelphians willing to pay for TOD?1.6 Assignment - Study TOD in your city

2 Expanding the Urban Growth Boundary
2.1 Introduction - Lancaster development2.1.1 The bid-rent model2.1.2 Set up Lancaster data2.2 Identifying areas inside and outside of the Urban Growth Area2.2.1 Associate each inside/outside buffer with its respective town.2.2.2 Building density by town and by inside/outside the UGA2.2.3 Visualize buildings inside and outside the UGA2.3 Return to Lancaster's bid-rent2.4 Conclusion - On boundaries2.5 Assignment - Boundaries in your community
3 Intro to Geospatial Machine Learning, Part 1
3.1 Machine learning as a planning3.1.1 Accuracy and generalizability3.1.2 The machine learning process3.1.3 The hedonic model3.2 Data wrangling - Home price and crime data3.2.1 Feature engineering - Measuring exposure to crime3.2.2 Exploratory analysis - Correlation3.3 Introduction to ordinary least squares regression3.3.1 Our first regression model3.3.2 More feature engineering and colinearity3.4 Cross-validation and return to goodness of fit3.4.1 Accuracy - Mean absolute error3.4.2 Generalizability - Cross-validation3.5 Conclusion - Our first model3.6 Assignment - Predict house prices
4 Intro to Geospatial Machine Learning, Part 2
4.1 On the spatial process of home prices4.1.1 Set up and data wrangling4.2 Do prices and errors cluster? The spatial lag4.2.1 Do model errors cluster? - Moran's I4.3 Accounting for neighborhood4.3.1 Accuracy of the neighborhood model4.3.2 Spatial autocorrelation in the neighborhood model4.3.3 Generalizability of the neighborhood model4.4 Conclusion - Features at multiple scales
5 Geospatial Risk Modeling - Predictive Policing
5.1 New predictive policing tools5.1.1 Generalizability in geospatial risk models5.1.2 From broken windows theory to broken windows policing5.1.3 Set up5.2 Data wrangling: Creating the fishnet5.2.1 Data wrangling: Joining burglaries to the fishnet5.2.2 Wrangling risk factors5.3 Feature engineering - Count of risk factors by grid cell5.3.1 Feature engineering - Nearest neighbor features5.3.2 Feature Engineering - Measure distance to one point5.3.3 Feature Engineering - Create the final_net5.4 Exploring the spatial process of burglary5.4.1 Correlation tests5.5 Poisson Regression5.5.1 Cross-validated Poisson regression5.5.2 Accuracy and generalzability5.5.3 Generalizability by neighborhood context5.5.4 Does this model allocate better than traditional crime hotspots?5.6 Conclusion - Bias but useful?5.7 Assignment - Predict risk
6 People-based ML Models
6.1 Bounce to work6.2 Exploratory analysis6.3 Logistic regression6.3.1 Training/testing sets6.3.2 Estimate a churn model6.4 Goodness of fit6.4.1 Roc curves6.5 Cross-validation6.6 Generating costs and benefits6.6.1 Optimizing the cost/benefit relationship6.7 Conclusion - Churn6.8 Assignment - Target a subsidy
7 People-based ML Models: Algorithmic Fairness
7.1 Introduction7.1.1 The specter of disparate impact7.1.2 Modeling judicial outcomes7.1.3 Accuracy and generalizability in recidivism algorithms7.2 Data and exploratory analysis7.3 Estimate two recidivism models7.3.1 Accuracy and generalizability7.4 What about the threshold?7.5 Optimizing ‘equitable' thresholds7.6 Assignment - Memo to the mayor
8 Predicting Rideshare Demand
8.1 Introduction - Rideshare8.2 Data wrangling - Rideshare8.2.1 Lubridate8.2.2 Weather data8.2.3 Subset a study area using neighborhoods8.2.4 Create the final space/time panel8.2.5 Split training and test8.2.6 What about distance features?8.3 Exploratory Analysis - Rideshare8.3.1 Trip_Count serial autocorrelation8.3.2 Trip_Count spatial autocorrelation8.3.3 Space/time correlation?8.3.4 Weather8.4 Modeling and validation using purrr::map8.4.1 A short primer on nested tibbless8.4.2 Estimate a rideshare forecast8.4.3 Validate test set by time8.4.4 Validate test set by space8.5 Conclusion - Dispatch8.6 Assignment - Predict bike share trips
Conclusion - Algorithmic Governance
Index

Overview

Public Policy Analytics teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. This book will appeal to researchers and data scientists at the intersection of data analytics and public policy, and readers who wish to understand how algorithms will affect the future of government.

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