Several large US cities, including Chicago, Los Angeles, New York, and Philadelphia, have developed information systems to distribute property-level housing data to community organizations and municipal agencies. These early warning systems are also intended to predict which properties are at greatest risk of abandonment, but they have rarely used statistical modeling to support such forecasts. This study used logistic regression to analyze data from the Philadelphia Neighborhood Information System in order to determine which properties were most likely to become imminently dangerous. Several different characteristics of the property, including whether it was vacant, had outstanding housing code violations, and tax arrearages as well as characteristics of nearby properties were identified as significant predictors. Challenges common to the development of early warning systems -including integrating administrative data, defining abandonment, and modeling temporal and spatial data -are discussed along with policy implications for cities like Philadelphia that have thousands of vacant and abandoned properties. ABSTRACT: Several large US cities, including Chicago, Los Angeles, New York, and Philadelphia, have developed information systems to distribute property-level housing data to community organizations and municipal agencies. These early warning systems are also intended to predict which properties are at greatest risk of abandonment, but they have rarely used statistical modeling to support such forecasts. This study used logistic regression to analyze data from the Philadelphia Neighborhood Information System in order to determine which properties were most likely to become imminently dangerous. Several different characteristics of the property, including whether it was vacant, had outstanding housing code violations, and tax arrearages as well as characteristics of nearby properties were identified as significant predictors. Challenges common to the development of early warning systemsÐincluding integrating administrative data, defining abandonment, and modeling temporal and spatial dataÐare discussed along with policy implications for cities like Philadelphia that have thousands of vacant and abandoned properties.Over the past 30 years, community groups, municipal governments, and researchers

ABSTRACT:We propose an extension of map algebra to three dimensions for spatio-temporal data handling. This approach yields a new class of map algebra functions that we call "cube functions." Whereas conventional map algebra functions operate on data layers representing two-dimensional space, cube functions operate on data cubes representing two-dimensional space over a third-dimensional period of time. We describe the prototype implementation of a spatio-temporal data structure and selected cube function versions of conventional local, focal, and zonal map algebra functions. The utility of cube functions is demonstrated through a case study analyzing the spatio-temporal variability of remotely sensed, southeastern U.S. vegetation character over various land covers and during different El Niño/Southern Oscillation (ENSO) phases. Like conventional map algebra, the application of cube functions may demand significant data preprocessing when integrating diverse data sets, and are subject to limitations related to data storage and algorithm performance. Solutions to these issues include extending data compression and computing strategies for calculations on very large data volumes to spatio-temporal data handling.

Objective The nine pictorial health warning labels (PWLs) proposed by the US Food and Drug Administration vary in format and feature of visual and textual information. Congruency is the degree to which visual and textual features reflect a common theme. This characteristic can affect attention and recall of label content. This study investigates the effect of congruency in PWLs on smoker’s attention and recall of label content. Methods 120 daily smokers were randomly assigned to view either congruent or incongruent PWLs, while having their eye movements recorded. Participants were asked to recall label content immediately after exposure and 5 days later. Results Overall, the image was viewed more and recalled better than the text. Smokers in the incongruent condition spent more time focusing on the text than smokers in the congruent condition (p=0.03), but dwell time of the image did not differ. Despite lower dwell time on the text, smokers in the congruent condition were more likely to correctly recall it on day 1 (p=0.02) and the risk message of the PWLs on both day 1 (p=0.01) and day 5 (p=0.006) than smokers in the incongruent condition. Conclusions This study identifies an important design feature of PWLs and demonstrates objective differences in how smokers process PWLs. Our results suggest that message congruency between visual and textual information is beneficial to recall of label content. Moreover, images captured and held smokers’ attention better than the text.

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