Public policy analytics : code and context for data science in government /: code and context for data science in government. (2021)

• Record Type: Book
• Further Information: Note: Ken Steif.
• Authors: Steif, Ken
• Contents: Preface Introduction ; Indicators for Transit Oriented Development ; 1.1 Why Start With Indicators? 1.1.1 Mapping & scale bias in areal aggregate data 1.2 Setup 1.2.1 Downloading & wrangling Census data 1.2.2 Wrangling transit open data 1.2.3 Relating tracts & subway stops in space 1.3 Developing TOD Indicators 1.3.1 TOD indicator maps 1.3.2 TOD indicator tables 1.3.3 TOD indicator plots 1.4 Capturing three submarkets of interest 1.5 Conclusion: Are Philadelphians willing to pay for TOD? 1.6 Assignment - Study TOD in your city ; ; Expanding the Urban Growth Boundary ; 2.1 Introduction - Lancaster development; 2.1.1 The bid-rent model; 2.1.2 Setup Lancaster data 2.2 Identifying areas inside & outside of the Urban Growth Area 2.2.1 Associate each inside/outside buffer with its respective town; 2.2.2 Building density by town & by inside/outside the UGA 2.2.3 Visualize buildings inside & outside the UGA; 2.3 Return to Lancaster’s Bid Rent 2.4 Conclusion - On boundaries 2.5 Assignment - Boundaries in your community Intro to geospatial machine learning, Part 1 ; 3.1 Machine learning as a Planning tool 3.1.1 Accuracy & generalizability 3.1.2 The machine learning process 3.1.3 The hedonic model 3.2 Data wrangling - Home price & crime data 3.2.1 Feature Engineering - Measuring exposure to crime 3.2.2 Exploratory analysis: Correlation; 3.3 Introduction to Ordinary Least Squares Regression 3.3.1 Our first regression model; 3.3.2 More feature engineering & colinearity 3.4 Cross-validation & return to goodness of fit; 3.4.1 Accuracy - Mean Absolute Error 3.4.2 Generalizability - Cross-validation 3.5 Conclusion - Our first model 3.6 Assignment - Predict house prices Intro to geospatial machine learning, Part 2 ; 4.1 On the spatial process of home prices 4.1.1 Setup & Data Wrangling 4.2 Do prices & errors cluster? The Spatial Lag; 4.2.1 Do model errors cluster? - Moran’s I; 4.3 Accounting for neighborhood 4.3.1 Accuracy of the neighborhood model 4.3.2 Spatial autocorrelation in the neighborhood model 4.3.3 Generalizability of the neighborhood model; 4.4 Conclusion - Features at multiple scales Geospatial risk modeling - Predictive Policing ; 5.1 New predictive policing tools 5.1.1 Generalizability in geospatial risk models 5.1.2 From Broken Windows Theory to Broken Windows Policing 5.1.3 Setup 5.2 Data wrangling: Creating the fishnet; 5.2.1 Data wrangling: Joining burglaries to the fishnet 5.2.2 Wrangling risk factors 5.3 Feature engineering - Count of risk factors by grid cell 5.3.1 Feature engineering - Nearest neighbor features 5.3.2 Feature Engineering - Measure distance to one point 5.3.3 Feature Engineering - Create the final_net 5.4 Exploring the spatial process of burglary 5.4.1 Correlation tests 5.5 Poisson Regression 5.5.1 Cross-validated Poisson Regression 5.5.2 Accuracy & Generalzability 5.5.3 Generalizability by neighborhood context; 5.5.4 Does this model allocate better than traditional crime hotspots? 5.6 Conclusion - Bias but useful? 5.7 Assignment - Predict risk People-based ML models ; 6.1 Bounce to work; 6.2 Exploratory analysis 6.3 Logistic regression; 6.3.1 Training/Testing sets 6.3.2 Estimate a churn model 6.4 Goodness of Fit 6.4.1 Roc Curves 6.5 Cross-validation 6.6 Generating costs and benefits 6.6.1 Optimizing the cost/benefit relationship 6.7 Conclusion - churn 6.8 Assignment - Target a subsidy People-Based ML Models: Algorithmic Fairness ; 7.1 Introduction 7.1.1 The spectre of disparate impact 7.1.2 Modeling judicial outcomes 7.1.3 Accuracy and generalizability in recidivism algorithms 7.2 Data and exploratory analysis 7.3 Estimate two recidivism models 7.3.1 Accuracy & Generalizability 7.4 What about the threshold? 7.5 Optimizing ‘equitable’ thresholds 7.6 Assignment - Memo to the Mayor ; Predicting rideshare demand ; 8.1 Introduction - ride share 8.2 Data Wrangling - ride share 8.2.1 Lubridate; 8.2.2 Weather data 8.2.3 Subset a study area using neighborhoods 8.2.4 Create the final space/time panel 8.2.5 Split training and test; 8.2.6 What about distance features? 8.3 Exploratory Analysis - ride share 8.3.1 Trip_Count serial autocorrelation 8.3.2 Trip_Count spatial autocorrelation 8.3.3 Space/time correlation? 8.3.4 Weather; 8.4 Modeling and validation using purrr::map; 8.4.1 A short primer on nested tibbles 8.4.2 Estimate a ride share forecast 8.4.3 Validate test set by time 8.4.4 Validate test set by space 8.5 Conclusion - Dispatch; 8.6 Assignment - Predict bike share trips Conclusion - Algorithmic Governance Index
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2021
• Extent: 1 online resource, illustrations (colour)
• Subjects: 352.380285; Public policy -- Data processing; United States -- Politics and government -- Data processing
• Languages: English
• ISBNs: 9781000401615; 9781000401578; 9781003054658
• Related ISBNs: 9780367516253; 9780367507619
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.