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Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis

Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
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Description

This study assessed the spatial distribution of vulnerability to extreme heat in 1990 and 2000 within metropolitan Phoenix based on an index of seven equally weighted measures of physical exposure and adaptive capacity. These measures were derived from spatially interpolated climate, normalized differential vegetation index, and U.S. Census data. From

This study assessed the spatial distribution of vulnerability to extreme heat in 1990 and 2000 within metropolitan Phoenix based on an index of seven equally weighted measures of physical exposure and adaptive capacity. These measures were derived from spatially interpolated climate, normalized differential vegetation index, and U.S. Census data. From resulting vulnerability maps, we also analyzed population groups living in areas of high heat vulnerability. Results revealed that landscapes of heat vulnerability changed substantially in response to variations in physical and socioeconomic factors, with significant alterations to spatial distribution of vulnerability especially between eastern and western sectors of Phoenix. These changes worked to the detriment of Phoenix's Hispanic population and the elderly concentrated in urban-fringe retirement communities.

ContributorsChow, Winston, 1951- (Author) / Chuang, Wen-Ching (Author) / Gober, Patricia (Author)
Created2011-08-18
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Description

We examined the horizontal and vertical nocturnal cooling influence of a small park with irrigated lawn and xeric surfaces (∼3 ha) within a university campus of a hot arid city. Temperature data from 0.01- to 3-m heights observed during a bicycle traverse of the campus were combined with modeled spatial

We examined the horizontal and vertical nocturnal cooling influence of a small park with irrigated lawn and xeric surfaces (∼3 ha) within a university campus of a hot arid city. Temperature data from 0.01- to 3-m heights observed during a bicycle traverse of the campus were combined with modeled spatial temperature data simulated from a three-dimensional microclimate model (ENVI-met 3.1). A distinct park cool island, with mean observed magnitudes of 0.7–3.6°C, was documented for both traverse and model data with larger cooling intensities measured closer to surface level. Modeled results possessed varying but generally reasonable accuracy in simulating both spatial and temporal temperature data, although some systematic errors exist. A combination of several factors, such as variations in surface thermal properties, urban geometry, building orientation, and soil moisture, was likely responsible for influencing differential urban and non-urban near-surface temperatures. A strong inversion layer up to 1 m over non-urban surfaces was detected, contrasting with near-neutral lapse rates over urban surfaces. A key factor in the spatial expansion of the park cool island was the advection of cooler park air to adjacent urban surfaces, although this effect was mostly concentrated from 0- to 1-m heights over urban surfaces that were more exposed to the atmosphere.

ContributorsChow, Winston, 1951- (Author) / Pope, Ronald L. (Author) / Martin, Chris A. (Author) / Brazel, Anthony J. (Author)
Created2010-05-21