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

While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, thus being able to serve as a low-cost decision support tool for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions.

Structure of 311 service requests as a signature of urban location.

This paper explores the determinants of green building policy adoption and the spatial and temporal diffusion of such policies. This research builds substantially on previous work by employing an o...

Greening the Regulatory Landscape: The Spatial and Temporal Diffusion of Green Building Policies in U.S. Cities

In this article, we apply signal processing and data science methodologies to study the environmental impact of burning different types of heating oil in New York City, where currently the burning of heavy fuel oil in buildings produces more annual black carbon, a key component of PM2.5, emissions, than all cars and trucks combined. The data utilized in this article are collected through New York City's Local Law 84 (LL84) energy disclosure mandate. The mandate requires annual energy consumption reporting for large buildings (i.e., approximately greater than 50,000 gross feet) of all use types. This analysis utilizes actual heating oil consumption data for calendar year 2012. The LL84 data set was merged with land use and geographic data at the tax lot level from the Primary Land Use Tax Lot Output (PLUTO) data set from the New York City Department of City Planning. The PLUTO data set provides building and tax lot characteristics, as well as their geographic location.

Big Data + Big Cities: Graph Signals of Urban Air Pollution [Exploratory SP]

Cities are increasingly adopting energy policies that reduce information asymmetries and knowledge gaps through data transparency, including energy disclosure and mandatory audit requirements for existing buildings. Although such audits impose non-trivial costs on building owners, their energy use impacts have not been empirically evaluated. Here we examine the effect of a large-scale mandatory audit policy—New York City’s Local Law 87—on building energy use, using detailed audit and energy data between 2011 and 2016 for approximately 4,000 buildings. This specific policy context, in which the compliance year is randomly assigned, provides a unique opportunity to explore the audit effect without the self-selection bias found in studies of voluntary audit policies. We find energy use reductions of approximately –2.5% for multifamily residential buildings and –4.9% for office buildings. The results suggest that mandatory audits, by themselves, create an insufficient incentive to invest in energy efficiency at the scale needed to meet citywide carbon-reduction goals. Although more cities are considering mandatory building energy audits, their effect on energy use in buildings is not clear. Using data from New York, Kontokosta et al. estimate the extent to which Local Law 87 mandating building energy audits has contributed to a decrease in energy use.

The impact of mandatory energy audits on building energy use

This paper presents the conceptual framework and justification for a “Quantified Community” (QC) and a networked experimental environment of neighborhood labs. The QC is a fully instrumented urban neighborhood that uses an integrated, expandable, and participatory sensor network to support the measurement, integration, and analysis of neighborhood conditions, social interactions and behavior, and sustainability metrics to support public decision-making. Through a diverse range of sensor and automation technologies -- combined with existing data generated through administrative records, surveys, social media, and mobile sensors -- information on human, physical, and environmental elements can be processed in real-time to better understand the interaction and effects of the built environment on human well-being and outcomes. The goal is to create an “informatics overlay” that can be incorporated into future urban development and planning that supports the benchmarking and evaluation of neighborhood conditions, provides a test-bed for measuring the impact of new technologies and policies, and responds to the changing needs and preferences of the local community.

Constantine E. Kontokosta

Papers

Structure of 311 service requests as a signature of urban location.

Greening the Regulatory Landscape: The Spatial and Temporal Diffusion of Green Building Policies in U.S. Cities

Big Data + Big Cities: Graph Signals of Urban Air Pollution [Exploratory SP]

The impact of mandatory energy audits on building energy use

The Quantified Community and Neighborhood Labs: A Framework for Computational Urban Planning and Civic Technology Innovation