Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The Rise of the Creative Class

Inclusion and democracy.

Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.

Graph Signal Processing: Overview, Challenges, and Applications

Sampson, Robert J. 2012. Great American city: Chicago and the enduring neighborhood effect. Chicago: University of Chicago Press. ISBN-13: 9780226734569. pp. 552, $27.50 cloth. Robert J. Sampson’s ...

Great American city: Chicago and the enduring neighborhood effect

Cities across the United States are implementing information communication technologies in an effort to improve government services. One such innovation in e-government is the creation of 311 systems, offering a centralized platform where citizens can request services, report non-emergency concerns, and obtain information about the city via hotline, mobile, or web-based applications. The NYC 311 service request system represents one of the most significant links between citizens and city government, accounting for more than 8,000,000 requests annually. These systems are generating massive amounts of data that, when properly managed, cleaned, and mined, can yield significant insights into the real-time condition of the city. Increasingly, these data are being used to develop predictive models of citizen concerns and problem conditions within the city. However, predictive models trained on these data can suffer from biases in the propensity to make a request that can vary based on socio-economic and demographic characteristics of an area, cultural differences that can affect citizens' willingness to interact with their government, and differential access to Internet connectivity. Using more than 20,000,000 311 requests - together with building violation data from the NYC Department of Buildings and the NYC Department of Housing Preservation and Development; property data from NYC Department of City Planning; and demographic and socioeconomic data from the U.S. Census American Community Survey - we develop a two-step methodology to evaluate the propensity to complain: (1) we predict, using a gradient boosting regression model, the likelihood of heating and hot water violations for a given building, and (2) we then compare the actual complaint volume for buildings with predicted violations to quantify discrepancies across the City.

Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain.

New York City faces the challenge of an ever-increasing homeless population with almost 60,000 people currently living in city shelters. In 2015, approximately 25% of families stayed longer than ni...

Applications of Machine Learning Methods to Predict Readmission and Length-of-Stay for Homeless Families: The Case of Win Shelters in New York City

The reduction of risk and uncertainty with respect to post-retrofit performance and energy savings is a critical component to unlocking traditional capital markets for commercial building retrofits. This, in turn, is a necessary step in scaling up sustainable real estate markets and retrofit programs. While a range of factors influence actual energy savings, there remains a gap in our understanding of how building characteristics affect energy performance across a broad spectrum of variables. Using a unique database of energy benchmarking data resulting from New York City’s Local Law 84, accessed and analyzed for the first time by the author, this paper examines energy performance across a range of building characteristics, including structural, mechanical, locational, and occupancy variables. The unprecedented data used in this paper allows for the analysis of over 10,000 large (over 50,000 square feet) commercial buildings and is the first significant, robust sample of non-voluntary energy performance reporting. Using robust regression techniques, this research then presents a predictive model of building energy efficiency. This work is particularly relevant to emerging policy initiatives relating to energy benchmarking, retrofit financing programs, and commercial lease structures designed to overcome split incentive barriers.

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Predicting Building Energy Efficiency Using New York City Benchmarking Data

Topic modeling to discover the thematic structure and spatial-temporal patterns of building renovation and adaptive reuse in cities

The Olympic Games have come to be viewed as an unprecedented opportunity to leverage a short-term event into a long-term positive legacy. Although the Olympics are assumed to yield economic benefits, there has been no rigorous analysis of the impact of hosting on residential real estate markets. Utilising a substantial dataset for six host cities and comparable cities between 1984 and 2000, this paper employs an adjusted interrupted time-series approach to estimate the house price impacts of hosting the Olympic Games. The results suggest that the Olympics are not a ‘one size fits all’ economic development strategy and that potential outcomes are dependent on a number of factors, including the co-ordination of planning and Olympic-related development and the relative scale of the total Olympic investment.

Constantine E. Kontokosta

Papers

Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain.

Applications of Machine Learning Methods to Predict Readmission and Length-of-Stay for Homeless Families: The Case of Win Shelters in New York City

Predicting Building Energy Efficiency Using New York City Benchmarking Data

Topic modeling to discover the thematic structure and spatial-temporal patterns of building renovation and adaptive reuse in cities

The Price of Victory: The Impact of the Olympic Games on Residential Real Estate Markets