Enhancing the Jaquez k Nearest Neighbor Test for Space-Time Interaction

The Jacquez k nearest neighbor test, originally developed to improve upon shortcomings of existing tests for space-time interaction, has been shown to be a robust and powerful method of detecting interaction. Despite its flexibility and power however, the test has three main shortcomings: (1) it discards important information regarding the spatial and temporal scale at which detected interac- tion takes place; (2) the results of the test have not been visualized; (3) recent research demonstrates the test to be susceptible to population shift bias. This study presents enhancements to the Jacquez k nearest neighbors test with the goal of addressing each of these three shortcomings and improving the utility of the test. Data on Burkitt’s lymphoma cases in Uganda between 1961-1975 are employed to illustrate the modifications and enhance the visual output of the test. Output from the enhanced test is compared to that provided by alternative tests of space-time interaction. Results show the enhancements presented in this study transform the Jacquez test into a complete, descriptive, and informative metric that can be used as a stand alone measure of global space-time interaction.space-time interaction, Jacquez k nearest neighbor, visualization, space-time cube, population shift bias

Visual exploration of eye movement data using the Space-Time-Cube

Eye movement recordings produce large quantities of spatio- temporal data, and are more and more frequently used as an aid to gain further insight into human thinking in usability studies in GIScience domain among others. After reviewing some common visualization methods for eye movement data, the limitations of these methods are discussed. This paper proposes an approach that enables the use of the Space-Time-Cube (STC) for representation of eye movement recordings. Via interactive functions in the STC, spatiotemporal patterns in eye movement data could be analyzed. A case study is presented according to proposed solutions for eye movement data analysis. Finally, the advantages and limitations of using the STC to visually analyze eye movement recordings are summarized and discussed

Visualized exploratory spatiotemporal analysis of hand-foot-mouth disease in southern China

Objectives: In epidemiological research, major studies have focused on theoretical models; however, few methods of visual analysis have been used to display the patterns of disease distribution.Design: For this study, a method combining the space-time cube (STC) with space-time scan statistics (STSS) was used to analyze the pattern of incidence of hand-foot-mouth disease (HFMD) in Guangdong Province from May 2008 to March 2009. In this research, STC was used to display the spatiotemporal pattern of incidence of HFMD, and STSS were used to detect the local aggregations of the disease.Setting: The hand-foot-mouth disease data were obtained from Guangdong Province from May 2008 to March 2009, with a total of 68,130 cases.Results: The STC analysis revealed a differential pattern of HFMD incidence among different months and cities and also showed that the population density and average precipitation are correlated with the incidence of HFMD. The STSS analysis revealed that the most likely aggregation includes the Shenzhen, Foshan and Dongguan populations, which are the most developed regions in Guangdong Province.Conclusion: Both STC and STSS are efficient tools for the exploratory data analysis of disease transmission. STC clearly displays the spatiotemporal patterns of disease. Using the maximum likelihood ratio, the STSS model precisely locates the most likely aggregation

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

The popularity of tracking devices continues to contribute to increasing volumes of spatio-temporal data about moving objects. Current approaches in analysing these data are unable to capture collective behaviour and correlations among moving objects. An example of these types of patterns is moving flocks. This article develops an improved algorithm for mining such patterns following a frequent pattern discovery approach, a well-known task in traditional data mining. It uses transaction-based data representation of trajectories to generate a database that facilitates the application of scalable and efficient frequent pattern mining algorithms. Results were compared with an existing method (Basic Flock Evaluation or BFE) and are demonstrated for both synthetic and real data sets with a large number of trajectories. The results illustrate a significant performance increase. Furthermore, the improved algorithm has been embedded into a visual environment that allows manipulation of input parameters and interactive recomputation of the resulting flocks. To illustrate the visual environment a data set containing 30 years of tropical cyclone tracks with 6 hourly observations is used. The example illustrates how the visual environment facilitates exploration and verification of flocks by changing the input parameters and instantly showing the spatio-temporal distribution of the resulting flocks in the Space-Time Cube and interactively selecting

Tracking and visualization of space-time activities for a micro-scale flu transmission study

Abstract Background Infectious diseases pose increasing threats to public health with increasing population density and more and more sophisticated social networks. While efforts continue in studying the large scale dissemination of contagious diseases, individual-based activity and behaviour study benefits not only disease transmission modelling but also the control, containment, and prevention decision making at the local scale. The potential for using tracking technologies to capture detailed space-time trajectories and model individual behaviour is increasing rapidly, as technological advances enable the manufacture of small, lightweight, highly sensitive, and affordable receivers and the routine use of location-aware devices has become widespread (e.g., smart cellular phones). The use of low-cost tracking devices in medical research has also been proved effective by more and more studies. This study describes the use of tracking devices to collect data of space-time trajectories and the spatiotemporal processing of such data to facilitate micro-scale flu transmission study. We also reports preliminary findings on activity patterns related to chances of influenza infection in a pilot study. Methods Specifically, this study employed A-GPS tracking devices to collect data on a university campus. Spatiotemporal processing was conducted for data cleaning and segmentation. Processed data was validated with traditional activity diaries. The A-GPS data set was then used for visual explorations including density surface visualization and connection analysis to examine space-time activity patterns in relation to chances of influenza infection. Results When compared to diary data, the segmented tracking data demonstrated to be an effective alternative and showed greater accuracies in time as well as the details of routes taken by participants. A comparison of space-time activity patterns between participants who caught seasonal influenza and those who did not revealed interesting patterns. Conclusions This study proved that tracking technology an effective technique for obtaining data for micro-scale influenza transmission research. The findings revealed micro-scale transmission hotspots on a university campus and provided insights for local control and prevention strategies.</p

Spatio-temporal cluster analysis and transmission drivers for Peste des Petits Ruminants in Uganda.

Peste des Petits Ruminants (PPR) is a transboundary, highly contagious, and fatal disease of small ruminants. PPR causes global annual economic losses of between USD 1.5-2.0 billion across more than 70 affected countries. Despite the commercial availability of effective PPR vaccines, lack of financial and technical commitment to PPR control coupled with a dearth of refined PPR risk profiling data in different endemic countries has perpetuated PPR virus transmission. In Uganda, over the past five years, PPR has extended from north-eastern Uganda (Karamoja) with sporadic incursions in other districts /regions. To identify disease cluster hotspot trends that would facilitate the design and implementation of PPR risk-based control methods (including vaccination), we employed the space-time cube approach to identify trends in the clustering of outbreaks in neighbouring space-time cells using confirmed PPR outbreak report data (2007-2020). We also used negative binomial and logistic regression models and identified high small ruminant density, extended road length, low annual precipitation and high soil water index as the most important drivers of PPR in Uganda. The study identified (with 90 - 99% confidence) five PPR disease hotspot trend categories across subregions of Uganda. Diminishing hotspots were identified in the Karamoja region whereas consecutive, sporadic, new, and emerging hotspots were identified in central and southwestern districts of Uganda. Inter-district and cross-border small ruminant movement facilitated by longer road stretches and animal comingling precipitate PPR outbreaks as well as PPR virus spread from its initial Karamoja focus to the central and south-western Uganda. There is therefore urgent need to prioritize considerable vaccination coverage to obtain the required herd immunity among small ruminants in the new hotspot areas to block transmission to further emerging hotspots. Findings of this study provide a basis for more robust timing and prioritization of control measures including vaccination. This article is protected by copyright. All rights reserved

Visualization for exploratory analysis of spatio-temporal data

Analysis of spatio-temporal data has become critical with the emerge of ubiquitous location sensor technologies and applications keeping track of such data. Especially with the widespread availability of low cost GPS devices, it is possible to record data about the location of people and objects at a large scale. Data visualization plays a key role in the successful analysis of these kind of data. Due to the complex nature of this analysis process, current approaches and analytical tools fail to help spatio-temporal thinking and they are not effective when solving large range of problems. In this work, we propose an interactive visualization tool to support human analyst understand user behaviors by analyzing location patterns and anomalies in massive collections of spatio-temporal data. The tool that we developed within this work combines a geovisualization framework with 3D visualizations and histograms. Tool's effectiveness in exploratory analysis is tested by trend analysis and anomaly detection in a real mobile service dataset with almost 1.5 million rows

Geographic Visualization in Archaeology

Archaeologists are often considered frontrunners in employing spatial approaches within the social sciences and humanities, including geospatial technologies such as geographic information systems (GIS) that are now routinely used in archaeology. Since the late 1980s, GIS has mainly been used to support data collection and management as well as spatial analysis and modeling. While fruitful, these efforts have arguably neglected the potential contribution of advanced visualization methods to the generation of broader archaeological knowledge. This paper reviews the use of GIS in archaeology from a geographic visualization (geovisual) perspective and examines how these methods can broaden the scope of archaeological research in an era of more user-friendly cyber-infrastructures. Like most computational databases, GIS do not easily support temporal data. This limitation is particularly problematic in archaeology because processes and events are best understood in space and time. To deal with such shortcomings in existing tools, archaeologists often end up having to reduce the diversity and complexity of archaeological phenomena. Recent developments in geographic visualization begin to address some of these issues, and are pertinent in the globalized world as archaeologists amass vast new bodies of geo-referenced information and work towards integrating them with traditional archaeological data. Greater effort in developing geovisualization and geovisual analytics appropriate for archaeological data can create opportunities to visualize, navigate and assess different sources of information within the larger archaeological community, thus enhancing possibilities for collaborative research and new forms of critical inquiry