Irene Casas

Protection of Geoprivacy and Accuracy of Spatial Information: How Effective Are Geographical Masks?

Spatial analysis and mapping of georeferenced, individual-level data can help identify important geographical patterns or lead to knowledge significant for dealing with specific social issues in a particular area. However, given the need to protect personal privacy when using geospatial data, the possibility for undertaking geographical analysis on certain types of individual-level data is becoming increasingly circumscribed. This article addresses the need to protect geoprivacy while making georeferenced, individual-level data available in such a way that analytical results are not significantly affected. The effectiveness of three geographical masks with different perturbation radii (r) is examined using a data set for lung-cancer deaths in Franklin County, Ohio, in 1999. The findings reveal a rather consistent trade-off between data confidentiality and accuracy of analytical results. There seems to be a threshold r-value at which the results of analyses on masked data become substantially different fro...

The Impact of Energy, Transport, and Trade on Air Pollution in China

A team of U.S.- and China-based geographers examines the relationship between Chinas economic development and its environment by modeling the effects of energy, transport, and trade on local air pollution emissions (sulfur dioxide and soot particulates) using the Environmental Kuznets model. Specifically, the latter model is investigated using spatial econometrics that take into account potential regional spillover effects from high-polluting neighbors. The analysis finds an inverted-U relationship for sulfur dioxide but a U-shaped curve for soot particulates. This suggests that soot particulates such as black carbon may pose a more serious environmental problem in China than sulfur dioxide. Journal of Economic Literature, Classification Numbers: C50, F10, Q43, R40. 4 figures, 3 tables, 47 references.

Intra- and Interseasonal Autoregressive Prediction of Dengue Outbreaks Using Local Weather and Regional Climate for a Tropical Environment in Colombia

Dengue fever transmission results from complex interactions between the virus, human hosts, and mosquito vectors—all of which are influenced by environmental factors. Predictive models of dengue incidence rate, based on local weather and regional climate parameters, could benefit disease mitigation efforts. Time series of epidemiological and meteorological data for the urban environment of Cali, Colombia are analyzed from January of 2000 to December of 2011. Significant dengue outbreaks generally occur during warm-dry periods with extreme daily temperatures confined between 18°C and 32°C—the optimal range for mosquito survival and viral transmission. Two environment-based, multivariate, autoregressive forecast models are developed that allow dengue outbreaks to be anticipated from 2 weeks to 6 months in advance. These models have the potential to enhance existing dengue early warning systems, ultimately supporting public health decisions on the timing and scale of vector control efforts.

Visualizing the impact of space-time uncertainties on dengue fever patterns

In this article, we evaluate the impact of positional and temporal inaccuracies on the mapping and detection of potential outbreaks of dengue fever in Cali, an urban environment of Colombia. Positional uncertainties in input data are determined by comparison between coordinates following an automated geocoding process and those extracted from on-field GPS measurements. Temporal uncertainties are modeled around the incubation period for dengue fever. To test the robustness of disease intensities in space and time when accounting for the potential space-time error, each dengue case is perturbed using Monte Carlo simulations. A space-time kernel density estimation (STKDE) is conducted on both perturbed and observed sets of dengue cases. To reduce the computational effort, we use a parallel spatial computing solution. The results are visualized in a 3D framework, which facilitates the discovery of new, significant space-time patterns and shapes of dengue outbreaks while enhancing our understanding of complex and uncertain dynamics of vector-borne diseases.

A Comparison of Three Methods for Identifying Transport-Based Exclusion: A Case Study of Children's Access to Urban Opportunities in Erie and Niagara Counties, New York

ABSTRACT Achieving transport sustainability is contingent on many factors, including transportation services being provided equitably regardless of race, income, gender, disability, and/or any other differentiating characteristics. A major risk of inequitable service provision is that without sufficient accessibility via transport, populations are put at a disadvantage, which may result in conditions of exclusion. At the present time, however, the dimensions of transport-based social exclusion are not fully understood, and the elusive nature of the concept renders it difficult to quantify. In this paper, three methods for identifying transport-excluded populations are examined and compared. The first follows a traditional approach to identifying disadvantaged groups by means of an inequality index based on deprivation. The other two techniques are accessibility-based, and work with a detailed travel diary data set. The study is conducted in the counties of Erie and Niagara, New York, and the population examined is composed of children between the ages of 5 and 18 years old. The results reveal how the models differentially identify excluded populations and should inform planners and practitioners of the implications for choosing between these different approaches.

The Internet Highway and Religious Communities: Mapping and Contesting Spaces in Religion-Online

We examine “religion-online,” an underrepresented area of research in new media, communication, and geography, with a multilevel study of the online representation and (re)presentation of Protestant Christian organizations in Singapore, which has one of the highest Internet penetration rates in the world and also believers affiliated with all the major world religions. We first critically discuss and empirically examine how online technologies are employed for religious community building in novel and diverse ways. Then we investigate the role religious leaders play through their mental representations of the spatial practices and scales through which their religious communities are imagined and practiced online. We show how churches use the multimodality of the Internet to assemble multiple forms of visible data and maps to extend geographic sensibilities of sacred space and create new social practices of communication.

A spatial model of socioeconomic and environmental determinants of dengue fever in Cali, Colombia

Dengue fever has gradually re-emerged across the global South, particularly affecting urban areas of the tropics and sub-tropics. The dynamics of dengue fever transmission are sensitive to changes in environmental conditions, as well as local demographic and socioeconomic factors. In 2010, the municipality of Cali, Colombia, experienced one of its worst outbreaks, however the outbreak was not spatially homogeneous across the city. In this paper, we evaluate the role of socioeconomic and environmental factors associated with this outbreak at the neighborhood level, using a Geographically Weighted Regression model. Key socioeconomic factors include population density and socioeconomic stratum, whereas environmental factors are proximity to both tire shops and plant nurseries and the presence of a sewage system (R2=0.64). The strength of the association between these factors and the incidence of dengue fever is spatially heterogeneous at the neighborhood level. The findings provide evidence to support public health strategies in allocating resources locally, which will enable a better detection of high risk areas, a reduction of the risk of infection and to strengthen the resilience of the population.

Spatio-Temporal Patterns of Dengue Fever in Cali, Colombia

Dengue fever is an arboviral disease typical of the tropics that can be life-threatening and if not controlled properly may result in an epidemic. The absence of an effective vaccine makes strategies to prevent the virus transmission the most effective means of control. The planning of such strategies, however, is difficult due to the constant movement of individuals and mosquito host (Aedes aegypti). In this paper, the spatial and temporal relations that might exist between infected individuals during a dengue-epidemic year are explored. This research is motivated in that a deep understanding of potential transmission patterns between individuals might lead to a better design and planning of control strategies. A GIS-based Health Exploratory AnaLysis Tool (HELP) is used to compute space-time relationships by means of spatial K-function, kernel density, space-time K-function and linking pairs of cases within significant time and space intervals. Significant clustering was observed at a scale of 50 meters and 750 meters, respectively while temporal significance was determined at two days and five to eight days. While an increase of cases occurs in the months following severe droughts due to an El NiA±o phenomenon, the location of clusters remains relatively stable. These are observed near areas where potential habitats for the mosquito exist such as storm drains, hard surfaces where water accumulates (e.g., vases, containers), but also in poorer neighborhoods. The results from the spatial analysis provide valuable information for health care managers to take preventive actions at the municipality level.

GABRIEL: Gis Activity-Based tRavel sImuLator. Activity Scheduling in the Presence of Real-Time Information

A series of travel simulators have been developed in the past two decades under the Intelligent Transportation Systems (ITS) umbrella. They have addressed issues such as reactions to advisory radio and variable message signs, use of navigation systems, route diversion, and mode choice. The objective of this paper is to present the design and implementation of a different kind of travel simulator. GABRIEL (Gis Activity-Based tRavel sImuLator) has as a foundation the activity-based approach and makes use of geographic information systems (GIS) as a development environment. The simulation scenario consists of a commute trip where two activities take place. En-route to the first destination, congestion occurs and subjects are requested to take action based on a set of alternatives. The simulator provides re-routing, destination substitution, dynamic geographic information and real-time information to aid users in their decision-making process. As a result it helps subjects in developing their ability to adapt given a particular scenario and allow researchers in understanding trip making, activity rescheduling, and the decision-making process from a comprehensive perspective.

Accelerating the discovery of space-time patterns of infectious diseases using parallel computing

Infectious diseases have complex transmission cycles, and effective public health responses require the ability to monitor outbreaks in a timely manner. Space-time statistics facilitate the discovery of disease dynamics including rate of spread and seasonal cyclic patterns, but are computationally demanding, especially for datasets of increasing size, diversity and availability. High-performance computing reduces the effort required to identify these patterns, however heterogeneity in the data must be accounted for. We develop an adaptive space-time domain decomposition approach for parallel computation of the space-time kernel density. We apply our methodology to individual reported dengue cases from 2010 to 2011 in the city of Cali, Colombia. The parallel implementation reaches significant speedup compared to sequential counterparts. Density values are visualized in an interactive 3D environment, which facilitates the identification and communication of uneven space-time distribution of disease events. Our framework has the potential to enhance the timely monitoring of infectious diseases.