Public policy analytics: (Record no. 5438)

MARC details
000 -LEADER
fixed length control field 06357nam a22002177a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230922105450.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780367507619
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 352.380285
Item number STE
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Steif, Ken
245 ## - TITLE STATEMENT
Title Public policy analytics:
Remainder of title code and context for data science in government 
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. CRC Press
Place of publication, distribution, etc. Boca Raton
Date of publication, distribution, etc. 2022
300 ## - PHYSICAL DESCRIPTION
Extent xxi, 206 p.
365 ## - TRADE PRICE
Price type code GBP
Price amount 44.99
490 ## - SERIES STATEMENT
Series statement Data science series
500 ## - GENERAL NOTE
General note Table of Contents:<br/><br/>Preface <br/>Introduction <br/><br/>Indicators for Transit Oriented Development <br/>1.1 Why Start With Indicators? <br/>1.1.1 Mapping & scale bias in areal aggregate data <br/>1.2 Setup <br/>1.2.1 Downloading & wrangling Census data <br/>1.2.2 Wrangling transit open data <br/>1.2.3 Relating tracts & subway stops in space <br/>1.3 Developing TOD Indicators <br/>1.3.1 TOD indicator maps <br/>1.3.2 TOD indicator tables <br/>1.3.3 TOD indicator plots <br/>1.4 Capturing three submarkets of interest <br/>1.5 Conclusion: Are Philadelphians willing to pay for TOD? <br/>1.6 Assignment - Study TOD in your city <br/><br/><br/>Expanding the Urban Growth Boundary<br/>2.1 Introduction - Lancaster development<br/>2.1.1 The bid-rent model<br/>2.1.2 Setup Lancaster data <br/>2.2 Identifying areas inside & outside of the Urban Growth Area <br/>2.2.1 Associate each inside/outside buffer with its respective town<br/>2.2.2 Building density by town & by inside/outside the UGA <br/>2.2.3 Visualize buildings inside & outside the UGA<br/>2.3 Return to Lancaster’s Bid Rent <br/>2.4 Conclusion - On boundaries <br/>2.5 Assignment - Boundaries in your community <br/><br/>Intro to geospatial machine learning, Part 1 <br/>3.1 Machine learning as a Planning tool <br/>3.1.1 Accuracy & generalizability <br/>3.1.2 The machine learning process <br/>3.1.3 The hedonic model <br/>3.2 Data wrangling - Home price & crime data <br/>3.2.1 Feature Engineering - Measuring exposure to crime <br/>3.2.2 Exploratory analysis: Correlation<br/>3.3 Introduction to Ordinary Least Squares Regression <br/>3.3.1 Our first regression model<br/>3.3.2 More feature engineering & colinearity <br/>3.4 Cross-validation & return to goodness of fit<br/>3.4.1 Accuracy - Mean Absolute Error <br/>3.4.2 Generalizability - Cross-validation <br/>3.5 Conclusion - Our first model <br/>3.6 Assignment - Predict house prices <br/><br/>Intro to geospatial machine learning, Part 2<br/>4.1 On the spatial process of home prices <br/>4.1.1 Setup & Data Wrangling <br/>4.2 Do prices & errors cluster? The Spatial Lag<br/>4.2.1 Do model errors cluster? - Moran’s I<br/>4.3 Accounting for neighborhood <br/>4.3.1 Accuracy of the neighborhood model <br/>4.3.2 Spatial autocorrelation in the neighborhood model <br/>4.3.3 Generalizability of the neighborhood model<br/>4.4 Conclusion - Features at multiple scales<br/><br/>Geospatial risk modeling - Predictive Policing <br/>5.1 New predictive policing tools <br/>5.1.1 Generalizability in geospatial risk models <br/>5.1.2 From Broken Windows Theory to Broken Windows Policing <br/>5.1.3 Setup <br/>5.2 Data wrangling: Creating the fishnet<br/>5.2.1 Data wrangling: Joining burglaries to the fishnet <br/>5.2.2 Wrangling risk factors <br/>5.3 Feature engineering - Count of risk factors by grid cell <br/>5.3.1 Feature engineering - Nearest neighbor features <br/>5.3.2 Feature Engineering - Measure distance to one point <br/>5.3.3 Feature Engineering - Create the final_net <br/>5.4 Exploring the spatial process of burglary <br/>5.4.1 Correlation tests <br/>5.5 Poisson Regression <br/>5.5.1 Cross-validated Poisson Regression <br/>5.5.2 Accuracy & Generalzability <br/>5.5.3 Generalizability by neighborhood context<br/>5.5.4 Does this model allocate better than traditional crime hotspots? <br/>5.6 Conclusion - Bias but useful? <br/>5.7 Assignment - Predict risk <br/><br/>People-based ML models<br/>6.1 Bounce to work<br/>6.2 Exploratory analysis <br/>6.3 Logistic regression<br/>6.3.1 Training/Testing sets <br/>6.3.2 Estimate a churn model <br/>6.4 Goodness of Fit <br/>6.4.1 Roc Curves <br/>6.5 Cross-validation <br/>6.6 Generating costs and benefits <br/>6.6.1 Optimizing the cost/benefit relationship <br/>6.7 Conclusion - churn <br/>6.8 Assignment - Target a subsidy <br/><br/>People-Based ML Models: Algorithmic Fairness<br/>7.1 Introduction <br/>7.1.1 The spectre of disparate impact <br/>7.1.2 Modeling judicial outcomes <br/>7.1.3 Accuracy and generalizability in recidivism algorithms <br/>7.2 Data and exploratory analysis <br/>7.3 Estimate two recidivism models <br/>7.3.1 Accuracy & Generalizability <br/>7.4 What about the threshold?<br/><br/>7.5 Optimizing ‘equitable’ thresholds <br/>7.6 Assignment - Memo to the Mayor <br/><br/><br/>Predicting rideshare demand<br/>8.1 Introduction - ride share <br/>8.2 Data Wrangling - ride share <br/>8.2.1 Lubridate<br/>8.2.2 Weather data <br/>8.2.3 Subset a study area using neighborhoods <br/>8.2.4 Create the final space/time panel <br/>8.2.5 Split training and test<br/>8.2.6 What about distance features? <br/>8.3 Exploratory Analysis - ride share <br/>8.3.1 Trip_Count serial autocorrelation <br/>8.3.2 Trip_Count spatial autocorrelation <br/>8.3.3 Space/time correlation? <br/>8.3.4 Weather<br/>8.4 Modeling and validation using purrr::map<br/>8.4.1 A short primer on nested tibbles <br/>8.4.2 Estimate a ride share forecast <br/>8.4.3 Validate test set by time <br/>8.4.4 Validate test set by space <br/>8.5 Conclusion - Dispatch<br/>8.6 Assignment - Predict bike share trips<br/>Conclusion - Algorithmic Governance <br/>Index
520 ## - SUMMARY, ETC.
Summary, etc. Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.<br/><br/>(https://www.routledge.com/Public-Policy-Analytics-Code-and-Context-for-Data-Science-in-Government/Steif/p/book/9780367507619#)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Politics and government--Data processing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Political planning--Data processing
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Bill No Bill Date Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Accession Number Checked out Date last seen Date checked out Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Public Policy & General Management IB/IN/502 14-09-2023 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 09/20/2023 International Book Centre 3206.57 1 352.380285 STE 005204 04/03/2024 01/04/2024 01/04/2024 1 4876.92 09/22/2023 Book

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