![]() Chapter 4 builds on the initial analysis to account for the 'spatial process' or pattern of home prices. Chapter 3 introduces linear regression, goodness of fit metrics, and cross-validation, with the goal of assessing model accuracy and generalizability. ![]() Non-developed land cover in the study areaĬhapters 3 & 4: Intro to Geospatial Machine LearningĬhapters 3 and 4 provide a first look at geospatial predictive modeling, forecasting home prices in Boston, MA. Towns inside of the Lancaster County study areaįootprints for a sample of 60% of buildings in the study area Philadelphia Census Tracts with data on the total population, number of white residents, educational attainment, median household income, median rent, and poverty for the year 2000Ĭhapter 2: Expanding the Urban Growth BoundaryĬhapter 2 explores the discontinuous nature of boundaries to understand how an Urban Growth Area in Lancaster County, PA affects suburban sprawl. Stations on the Market Frankford (El) line Introducing the tidyverse, tidycensus, and sf packages, this chapter analyzes whether Philadelphia renters are willing to pay a premium for transit amenities. Chapter 1: Indicators for Transit Oriented Developmentįollowing the Introduction, Chapter 1 introduces indicators as an important tool for simplifying and communicating complex processes to non-technical decision makers. ![]() The sections below provide a description of each dataset and the original source, when applicable. However, for posterity, the DATA folder on this repo has all the data, organized by chapter. Each chapter includes API calls that read data directly into R. Readers can expect an introduction to R, geospatial data science, and machine learning, conveyed through real world use cases of data science in government.Īll of the book's data is free and open source, compiled from across the web. ![]() Designed for students studying City Planning and related disciplines, the book teaches both code and context toward improved public-sector decision making. The book is available online and eventually, in print. Public Policy Analytics is a new book by Ken Steif, Ph.D that teaches at the intersection of data science and public policy. ![]()
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