Public policy analytics: (Record no. 5438)
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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 | |
fixed length control field | 230922b |||||||| |||| 00| 0 eng d |
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 |
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 |
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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 |