[Show abstract][Hide abstract] ABSTRACT: The purpose of this exploratory case study is to use social network analysis techniques to visualise and analyse potential real estate property flipping transactions which may be a type of investment in Mansfield, OH. While real estate property flipping is typically associated with hot real estate markets, Mansfield's real estate market, interestingly, has been a cold one. Social network analysis is a method for analysing the structure of relationships among social entities through networks and graphs. We look at how homebuyers and grantees of mortgages relate to each other, utilising Gephi and UCINET software for visualisation purposes. We find that almost 50% of the mortgage grantees are from Ohio, which runs counter to our expectations based on the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994. We also find that the topological structure is highly fragmented. In some cases, the components represent only a single transaction between one homebuyer and one grantee. In other cases, the clusters are more complex, indicating potential real estate property flipping.
Preview · Article · Jul 2015 · European Journal of Housing Policy
[Show abstract][Hide abstract] ABSTRACT: This paper investigates sensitivity of location-sharing services (LSS) data with a focus on understanding American daily travel pattern using three LSS datasets: Brightkite, Gowalla and Foursquare. Through a systematic data refining process, person miles of travel and daily person trip are created and compared both among themselves and with the US National Household Travel Survey (NHTS) of 2009. The results suggest that LSS data provides a better estimation of person miles of travel than daily person trip on average. In addition, the comparison with the NHTS reveals that LSS data tends to have a better reflection of daily travel behavior among metro areas with high population density.
[Show abstract][Hide abstract] ABSTRACT: Big data holds tremendous potential for public policy analysis. At the same time, its use prompts a number of issues related to statistical bias, privacy, equity, and governance, among others. Accordingly, there is a need to formulate, evaluate, and implement policies that not only mitigate the risks, but also maximize the benefits of using big data for policy analysis. This poses a number of challenges, which are highlighted in this essay.
No preview · Article · Jul 2014 · Review of Policy Research
[Show abstract][Hide abstract] ABSTRACT: The notion of the ‘productivity puzzle,’ referring particularly to gender disparities in science and technology publication rates, raises a variety of critical issues for understanding related workforce development and capacity. However, such issues typically are framed relative to an increasingly outdated cultural and technological landscape in which scientific productivity is viewed principally as an outcome. We argue instead that characterizing scientific productivity as a multifaceted dynamic, highly networked, and interactive process, rather than just an outcome, might provide greater insight into the gendered nature of science and lead to a re-framing of the gender-differentiated productivity puzzle. By rethinking how we engage related questions, we might gain ground on explaining and unraveling the productivity puzzle in ways that will benefit the scientific enterprise and society in general.
Preview · Article · Jan 2012 · SSRN Electronic Journal
[Show abstract][Hide abstract] ABSTRACT: Context: Online social networks and the Web 2.0 technologies embedded in these sites are creating an environment in which individuals can communicate and share information in ways that were previously not possible. Such websites are providing an unprecedented and growing collection of data on individual behavior that is very rich in detail. This includes information on with whom, when and where people interact, and more generally, what their activity patterns look like in time and space, and even what their sentiment or preferences are at specific moments and locations. Knowledge Gaps: There is a burgeoning body of literature that draws upon social media data and more broadly information collected via mobile communications devices (e.g., cell phone trajectories) to model and understand particular aspects of human behavior, including mobility patterns and social and spatio-temporal interaction. Yet, very little of this research has examined how and to what extent the spatio-temporal activity patterns revealed by these new forms of data vary across metropolitan areas, especially after controlling for relevant city-specific characteristics such as the size, density, composition or demographic profile of a city. While we recognize that the study of space-time activity patterns itself is not new, there are some gaps in the literature that should be noted. First, most analyses have been confined to a select set of cities – i.e., those that have conducted travel diaries or activity-based surveys. Due to inconsistencies in the format and type of information collected from such surveys, comparative analyses are problematic. Second, few studies have looked explicitly at the simultaneous integration of space, time and social (inclusive of cyber socialization) interaction, and the complex mobility patterns that arise from this behavior. Lastly, unlike location sharing services data, the information provided by travel behavior surveys tend to capture only mobility patterns arising from the primary residents of a city and not the behavior of transient visitors to that location. Study Objectives: The primary objectives of this study are to 1). understand how and to what extent location sharing services data approximate regional spatio-temporal activity patterns 2). develop a set of network-based metrics for characterizing the centrality and disorder of such activities in a region, and, 3). conduct a cross-city comparison using these metrics and related indicators of mobility.Data: To carry out the proposed research, we intend to use location services data collected over a five month period in 2010-11 (Cheng et al., 2011). This data provides information on user check-ins, or more specifically, where individuals indicate they are at different times of the day and week. Additional details on each individual’s status within the social networks that they belong are also included in the dataset. Methodology: The study methodology draws heavily on techniques from social network analysis, although concepts form landscape ecology, physics and geography are also utilized to capture different aspects of regional activity patterns. To gain an understanding of the types of activities that location sharing services data capture, we first conduct a correlation analysis using sector-based establishment data from the U.S. Census County Business Patterns. Correlations are examined at the zip code level. Second, using a space-time bipartite network topology, we derive a set of measures that characterize the centrality and disorder (entropy) of activities in a region, and that further can be decomposed to examine the spatial distribution of these characteristics. With individual location data aggregated to grid cells and summarized according to regular time intervals, we apply the technique to two U.S. metropolitan areas: Atlanta and Chicago.Significance: Implications for travel demand forecasting, epidemiological and information diffusion modelling and abnormal crowd detection (e.g., through “burstiness” analysis) will be drawn from the study.
Full-text · Article · Jan 2012 · SSRN Electronic Journal
[Show abstract][Hide abstract] ABSTRACT: Acknowledging that the digital divide is becoming more of a knowledge divide, we invoke an image of the 'Tower of Babel,' evincing a vertical hierarchy of information and relations in which access to the top tiers is highly limited. Depicting the knowledge society itself, which encompasses a highly complex interconnected system of digital networks within which interaction among social actors occur and from which knowledge is created and diffused, we emphasize networks as a central factor determining access and posit the broader notion of then Digital Knowledge Network Divide (DKND) to better understand related structures and dynamics. In the face of concerns over democratizing trends and, more, general calls for expanding the science and technology workforce and increasing scientific literacy, access to knowledge is critical. Accordingly, an important challenge for the years to come will be to characterize the evolving and unique landscape of the knowledge society in order to inform and design effective policies and programs. Related research will require the development of measures and tools that capture the hierarchical relations and dynamics of the DKND and that, ultimately, will allow for the assessment of related spatio-temporal disparities and the determination of indicators of network connectivity to measure changes in overall access and participation in the knowledge society.
[Show abstract][Hide abstract] ABSTRACT: We conduct an exploratory analysis of gender differences in collaboration and productivity in the United States in two biomedical and behavioral science fields: biology and psychology. While the particular trajectories look somewhat different for the two fields, the trend in both has been toward a growing presence of women with advanced degrees and as faculty in related programs. We explore whether or not the productivity of women relative to men is consistent with these trends, how patterns of collaboration differ by gender, and if either is mediated by field. To examine these issues, we develop a set of gendered metrics on collaboration and productivity based on Thomson Reuters Web of Science citations for 2008. In both biology and psychology, women tended to collaborate more than men, but still were found to be disadvantaged overall in productivity outcomes. In general, the analysis provided useful insights and directions for future research. Of particular note is the need to move beyond static analyses in order to capture the dynamic complexity of gender as an interactive and dynamic factor in processes affecting biomedical workforce development and productivity.
No preview · Article · Jun 2011 · World Medical and Health Policy
[Show abstract][Hide abstract] ABSTRACT: This article presents an analysis of five available house price indices that are used to track house prices at the metropolitan area level. These five indices are (1) the Federal Housing Finance Agency (FHFA) House Price Index (HPI), (2) the Standard & Poor’s/Case-Shiller® Home Price Indices, (3) an adjusted version of the FHFA House Price Index, (4) the Zillow Home Value Index, and (5) the NATIONAL ASSOCIATION OF REALTORS® Median Home Price. This study first discusses the strengths and weaknesses of these indices for use in a spatio-temporal analysis. Then, it provides a comparative analysis of their change rate for 10 metropolitan statistical areas (MSAs) for two time periods: the third quarter of 2006 through the third quarter of 2007 and the first quarter of 2007 through the first quarter of 2008. In addition, this research constructs a series of spatio-temporal indicators based on time and spatial lags of the HPI for 302 MSAs for the 2000 to 2007 period. The results of this data brief could help researchers interested in spatio-temporal analyses of the latest housing bubble and of house price indices at large.
[Show abstract][Hide abstract] ABSTRACT: Foreclosures are spatially contagious by nature (Can 1998). Abandoned or vacant properties have a negative spillover effect, by reducing the expected return on investment on surrounding properties. While there is a growing body of literature focusing on the latest wave of foreclosures in the US (Foote, Gerardi and Willen 2008b; Coulton et al. 2008; Lin et al. 2009), the spatial aspect has not been analyzed. We explore the spatial contagion of foreclosures, at the neighborhood level. Following an epidemiological concept, we define foreclosure contagion as an increase in neighborhood foreclosures that spreads over time from neighborhood to adjoining neighborhoods. In addition, we define foreclosure hotspots as areas with high values of foreclosures that are surrounded by areas with high foreclosure levels. The purpose is to identify the evolution of this spatial phenomenon by structure type (single units versus multi-units) and the socio-demographic characteristics of the neighborhoods affected by foreclosure. This research employs data from the Warren Group on nearly 15, 000 residential properties that entered the second stage of the foreclosure process during 2007 and the first quarter of 2008. The data cover four states: Connecticut, Massachusetts, New Hampshire, and Rhode Island. Our empirical evidence shows that the foreclosures in New England during this time period exhibited a spatial contagion pattern and that most troubled neighborhoods, the foreclosure hotspots, were “infecting” the adjacent neighborhoods. Any foreclosure mitigation program should include a spatial focus and target the foreclosures in the foreclosure hotspots.
[Show abstract][Hide abstract] ABSTRACT: Epistemic communities have been identified and studied through collaboration and citation analyses. One issue that has received increasing calls for research in this area is the role of gender, especially in light of significant variation in participation and productivity along gender lines. Accordingly, we argue for the active incorporation of gender as a central consideration in analyses aimed at assessing epistemic communities and their impact at individual, national, and international levels of analysis. Drawing on various theoretical perspectives and considering a range of empirical findings, directions for research are delineated for assessing gender differentiation in collaboration productivity and network participation, particularly in terms of scientific discipline, institutional referents, and professional age and status, along with socio-cultural and political characteristics. The development of gender-specific metrics at different levels of aggregation and their broader analytical incorporation are discussed relative to assessments of gender distributional inequities and network properties. We suggest that allowing for various factors dictated by individual circumstances and field and network conditions can lead to improved contextual consideration and help to fill gaps in our knowledge to provide better and more informed understandings about the productivity and career patterns of women scientists.
[Show abstract][Hide abstract] ABSTRACT: In this paper we develop an exploratory non-parametric clustering model of spatial and/or spatio-temporal phenomena based on Kolmogorov entropy. The methodology will be tested using quarterly HPI (Housing Price Index) data for 350 plus cities in the US from the Federal Financing and Housing Administration (FHFA), an agency of HUD (US Dept of Housing and Urban Development). This multivariate data will be also analyzed with Principal Component Analysis (PCA) techniques to identify key regions involved in creating the housing bubble and its spread to the rest of cities.
[Show abstract][Hide abstract] ABSTRACT: In this paper we explore the development of a parameter free region classifier based on Kolmogorov complexity. Given a set of regions described by unlimited but fixed number of attributes for each region, the region classifier will be able to build a classification tree which will help identify which regions are similar/dissimilar to each other based on a relative distance measure derived from Kolmogorov complexity. The region classifier is tested with the block level US Census demographics data as well as hitech establishment data for a subset of metropolitan regions. Preliminary results are presented for the census data as well as for the hitech sector for three different time periods.
Full-text · Article · Nov 2009 · SSRN Electronic Journal
[Show abstract][Hide abstract] ABSTRACT: Cybersecurity is an issue of increasing concern since the events of September 11th. Many questions have been raised concerning the security of the Internet and the rest of U S's information infrastructure. This paper begins to examine the issue by analyzing the Internet's autonomous system (AS) map. Using the AS map, generic malicious infections are simulated and different defense strategies are considered in a cost effectiveness analysis framework. The results show that protecting the most connected nodes provides significant gains in security and that after the small minority of the most connected nodes are protected there are diminishing returns for further protection. Although if parts of the small minority of the most connected firm are not protected, such as non-US firms, protection levels are significantly decreased. A simple cost function is also proposed to prepare cost effectiveness analysis of different security strategies, and specific financial and US federal government policies will be simulated and compared.
[Show abstract][Hide abstract] ABSTRACT: An urban area is a complex, dynamic system of networks through which information, capital and power propagate across and within
nodes of activities. While innovations in information technology are making it easier for transactions in these networks to
occur over greater distances, the importance of spatial proximity in such networks is still very much relevant. Economic,
social and other types of benefits drive activities to co-locate, where one may view the process as one of preferential attachment.
The physical agglomeration of activities that arises out this process, at any point in time, is what we characterize in this
chapter as the “backbone” of region. We hypothesize that such a feature is not static, but rather, it shifts in space over
time in response to changing constraints and circumstances.
[Show abstract][Hide abstract] ABSTRACT: Disruptions to transportation networks can be very costly. However, managing disruptions and the costs associated with these
events, poses some challenges. Transport networks are, in many cases, large and complex. This paper develops a method, based
on complex network theory, to analyse transportation networks. It provides a way, through the use raster-based geographic
information system (GIS) techniques, to identify critical nodes or links in a network that reflect spatial interdependencies
with other networks and to assess how resilient the networks are to failures of these locations. For purposes of illustration,
the method is applied to the network of major roads and rail in the State of Florida.
No preview · Article · Oct 2007 · Networks and Spatial Economics
[Show abstract][Hide abstract] ABSTRACT: The events of 9/11 brought renewed focus to critical infrastructure, but the security of infrastructure has been and continues
to be an issue outside the scope of any one event or country. Oil pipeline attacks in Iraq, massive blackouts in Italy, the
United States, and Russia, submarine cable failures in the Atlantic, accidental and intentional failures of infrastructure
are an increasing and complex problem. The issue of infrastructure security is a global problem both is applicability and
connectivity. All nations are dependent on infrastructure and many of these infrastructures cross international borders and
some span the globe. A problem facing all nations is that they have the responsibility for securing infrastructure but critical
aspects are owned by the private sector. This though is only one of many problems facing infrastructure security: 1) infrastructures
are interdependent on each others reliability 2) infrastructures are large, dynamically unsynchronized, and complex 3) sharing
information about infrastructure vulnerabilities is severely hampered by fears of regulation and competition. Along with these
direct obstacles there are larger economic forces that complicate the issue. The markets driving infrastructure are geared
towards maximizing efficiency to increase profit and not maximizing protection, which can result in public vulnerabilities.
[Show abstract][Hide abstract] ABSTRACT: Designing resilient and reliable networks is a principle concern of planners and private firms. Traffic congestion whether recurring or as the result of some aperiodic event is extremely costly. This paper describes an alternative process and a model for analyzing the resiliency of networks that address some of the shortcomings of more traditional approaches â€“ e.g., the four-step modeling process used in transportation planning. It should be noted that the authors do not view this as a replacement to current approaches but rather as a complementary tool designed to augment analysis capabilities. The process that is described in this paper for analyzing the resiliency of a network involves at least three steps: 1. assessment or identification of important nodes and links according to different criteria 2. verification of critical nodes and links based on failure simulations and 3. consequence. Raster analysis, graph-theory principles and GIS are used to develop a model for carrying out each of these steps. The methods are demonstrated using two, large interdependent networks for a metropolitan area in the United States.