Location Imprecision Research Articles

Decision Tree-Based Contextual Location Prediction from Mobile Device Logs

Contextual location prediction is an important topic in the field of personalized location recommendation in LBS (location-based services). With the advancement of mobile positioning techniques and various sensors embedded in smartphones, it is convenient to obtain massive human mobile trajectories and to derive a large amount of valuable information from geospatial big data. Extracting and recognizing personally interesting places and predicting next semantic location become a research hot spot in LBS. In this paper, we proposed an approach to predict next personally semantic place with historical visiting patterns derived from mobile device logs. To address the problems of location imprecision and lack of semantic information, a modified trip-identify method is employed to extract key visit points from GPS trajectories to a more accurate extent while semantic information are added through stay point detection and semantic places recognition. At last, a decision tree model is adopted to explore the spatial, temporal, and sequential features in contextual location prediction. To validate the effectiveness of our approach, experiments were conducted based on a trajectory collection in Guangzhou downtown area. The results verified the feasibility of our approach on contextual location prediction from continuous mobile devices logs.

Integrated spatiotemporal surveillance system: Data, Analysis and Visualization

ObjectiveTo build an open source spatiotemporal system that integratesanalysis and visualization for disease surveillanceIntroductionMost surveillance methods in the literature focus on temporalaberration detections with data aggregated to certain geographicalboundaries. SaTScan has been widely used for spatiotemporalaberration detection due to its user friendly software interface.However, the software is limited to spatial scan statistics and suffersfrom location imprecision and heterogeneity of population. RSurveillance has a collection of spatiotemporal methods that focusmore on research instead of surveillanceMethodsBased in Ontario, Canada, we used postal codes for determiningthe location of cases of reportable infectious diseases. The variationin geographic sizes and shapes of the case and census geographiescreated challenges for developing a uniform temporal spatialsurveillance system, including:Linking case and population data due to misclassification errors,Distance based correlations due to irregularly shaped areas(e.g. FSA’s), andVisualization bias due to variation in population density, e.g. largearea with little population.To overcome these challenges, we developed the Ontario HybridInformation Map (OHIM) boundary, which is a combination ofPublic Health Unit boundaries (rural areas), census subdivisions(rural urban mixed) and regular grid cells (urban). The goal is tocapture population details in urban areas without losing informationin rural areas. OHIM has around 4600 geographies with more thanhalf located in urban centers. Population distribution by gender andage group was calculated for each OHIM geography. A lookup filewas also created to link all Ontario postal codes to OHIM geography.To create baselines, historical data for influenza A were used tomodel the seasonality and calculate expected case count for eachOHIM geography for each week. Standardized incident ratios (SIR)were calculated as exploratory statistics, and a spatiotemporal Besag-York-Mollie (BYM) model was used to calculate the probability thatthe risk is higher than a pre-specified threshold. Integrated NestedLaplace Approximation (R-INLA) was used in R to explore differenttypes of spatiotemporal interactions and for fast Bayesian inference.The ability to apply the models was verified by examining previousoutbreaks and seeking the opinion of staff that routinely performsurveillance on influenza.To ensure the visualization integrates with the analysis, R packageShiny was used to build an interactive spatiotemporal visualizationon OHIM boundary utilizing Open Street Map and html5. Theapplication not only allows users to pan and zoom in space and timeto explore the results and locate high risk areas, it also gives users theflexibility to change algorithm parameters for instant feedback. Figure1 demonstrates a zoomed-in OHIM boundary with pointers signalfor “high risk” area at user specified statistics exceeds a threshold(e.g., SIR > 2). Using the algorithms and visualization tools,surveillance experts pick the optimal time and place to be notifiedbased on historical data and therefore the optimal threshold, whichwill be verified by prospectively running the algorithms.ResultsThe OHIM boundaries build the foundation for efficient spatialmodelling and visualization for public health surveillance in Ontario.Together with the integrated modelling and visualization system,staff are able to interactively optimize the aberration thresholds andidentify potential outbreaks in real time. Staff reported preference ofSIR due to its faster computations and easier interpretation.One major challenge was scalability: the ability to handle highresolutions of spatiotemporal data. When the system was applied on4600 polygons by 200 weeks, significant delays were encountered inboth analysis and visualization. Difficulties in computational time,memory requirement and visualization interactivity created delaysand freezing, thereby limited user experience. This problem waspartially addressed by optimizing parameters for fast computationsConclusionsThis work shows the “proof of concept” for an open source,customizable spatiotemporal surveillance system that overcomesexisting data challenges in Ontario. However, more work is requiredto make this fully operational and efficient in production.

A citizen data-based approach to predictive mapping of spatial variation of natural phenomena

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (Bcont(0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the field-observed distribution of R. bieti. Bcont(0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (−0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.

Mapping thunder sources by inverting acoustic and electromagnetic observations

AbstractWe present a new method of locating current flow in lightning strikes by inversion of thunder recordings constrained by Lightning Mapping Array observations. First, radio frequency (RF) pulses are connected to reconstruct conductive channels created by leaders. Then, acoustic signals that would be produced by current flow through each channel are forward modeled. The recorded thunder is considered to consist of a weighted superposition of these acoustic signals. We calculate the posterior distribution of acoustic source energy for each channel with a Markov Chain Monte Carlo inversion that fits power envelopes of modeled and recorded thunder; these results show which parts of the flash carry current and produce thunder. We examine the effects of RF pulse location imprecision and atmospheric winds on quality of results and apply this method to several lightning flashes over the Magdalena Mountains in New Mexico, USA. This method will enable more detailed study of lightning phenomena by allowing researchers to map current flow in addition to leader propagation.

Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data

Global positioning system (GPS) technologies collect unprecedented volumes of animal location data, providing ever greater insight into animal behaviour. Despite a certain degree of inherent imprecision and bias in GPS locations, little synthesis regarding the predominant causes of these errors, their implications for ecological analysis or solutions exists. Terrestrial deployments report 37 per cent or less non-random data loss and location precision 30 m or less on average, with canopy closure having the predominant effect, and animal behaviour interacting with local habitat conditions to affect errors in unpredictable ways. Home-range estimates appear generally robust to contemporary levels of location imprecision and bias, whereas movement paths and inferences of habitat selection may readily become misleading. There is a critical need for greater understanding of the additive or compounding effects of location imprecision, fix-rate bias, and, in the case of resource selection, map error on ecological insights. Technological advances will help, but at present analysts have a suite of ad hoc statistical corrections and modelling approaches available-tools that vary greatly in analytical complexity and utility. The success of these solutions depends critically on understanding the error-inducing mechanisms, and the biggest gap in our current understanding involves species-specific behavioural effects on GPS performance.

Anonymization of moving objects databases by clustering and perturbation

Preserving individual privacy when publishing data is a problem that is receiving increasing attention. Thanks to its simplicity the concept of k-anonymity, introduced by Samarati and Sweeney [1], established itself as one fundamental principle for privacy preserving data publishing. According to the k-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k−1 other individuals. In this article we tackle the problem of anonymization of moving objects databases. We propose a novel concept of k-anonymity based on co-localization, that exploits the inherent uncertainty of the moving object's whereabouts. Due to sampling and imprecision of the positioning systems (e.g., GPS), the trajectory of a moving object is no longer a polyline in a three-dimensional space, instead it is a cylindrical volume, where its radius δ represents the possible location imprecision: we know that the trajectory of the moving object is within this cylinder, but we do not know exactly where. If another object moves within the same cylinder they are indistinguishable from each other. This leads to the definition of ( k , δ ) -anonymity for moving objects databases. We first characterize the ( k , δ ) -anonymity problem, then we recall NWA ( N ever W alk A lone ), a method that we introduced in [2] based on clustering and spatial perturbation. Starting from a discussion on the limits of NWA we develop a novel clustering method that, being based on EDR distance [3], has the important feature of being time-tolerant. As a consequence it perturbs trajectories both in space and time. The novel method, named W 4 M ( W ait for M e ), is empirically shown to produce higher quality anonymization than NWA , at the price of higher computational requirements. Therefore, in order to make W 4 M scalable to large datasets, we introduce two variants based on a novel (and computationally cheaper) time-tolerant distance function, and on chunking. All the variants of W 4 M 1 1 Software freely available at: www-kdd.isti.cnr.it/W4M/. are empirically evaluated in terms of data quality and efficiency, and thoroughly compared to their predecessor NWA . 2 2 Software freely available at: www-kdd.isti.cnr.it/NWA/. Data quality is assessed both by means of objective measures of information distortion, and by more usability oriented measure, i.e., by comparing the results of (i) spatio-temporal range queries and (ii) frequent pattern mining, executed on the original database and on the ( k , δ ) -anonymized one. Experimental results over both real-world and synthetic mobility data confirm that, for a wide range of values of δ and k, the relative distortion introduced by our anonymization methods is kept low. Moreover, the techniques introduced to make W 4 M scalable to large datasets, achieve their goal without giving up data quality in the anonymization process.

Comprehensive high-throughput arrays for relative methylation (CHARM)

This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation, an example of antibody-mediated methyl-specific fractionation; (2) HELP, or HpaII tiny fragment enrichment by ligation-mediated PCR, an example of differential amplification of methylated DNA; and (3) fractionation by McrBC, an enzyme that cuts most methylated DNA. These results were validated using 1466 Illumina methylation probes on the GoldenGate methylation assay and further resolved discrepancies among the methods through quantitative methylation pyrosequencing analysis. While all three methods provide useful information, there were significant limitations to each, specifically bias toward CpG islands in MeDIP, relatively incomplete coverage in HELP, and location imprecision in McrBC. However, we found that with an original array design strategy using tiling arrays and statistical procedures that average information from neighboring genomic locations, much improved specificity and sensitivity could be achieved, e.g., approximately 100% sensitivity at 90% specificity with McrBC. We term this approach "comprehensive high-throughput arrays for relative methylation" (CHARM). While this approach was applied to McrBC analysis, the array design and computational algorithms are fractionation method-independent and make this a simple, general, relatively inexpensive tool suitable for genome-wide analysis, and in which individual samples can be assayed reliably at very high density, allowing locus-level genome-wide epigenetic discrimination of individuals, not just groups of samples. Furthermore, unlike the other approaches, CHARM is highly quantitative, a substantial advantage in application to the study of human disease.

Assessing a single classification accuracy measure to deal with the imprecision of location and class: Fuzzy weighted kappa versus kappa

Standard accuracy assessment in raster modelling assumes that desirable classes are exhaustively exclusive, location error either does not exist or does not affect classification, and classification results are crisp, not fuzzy Failing to take these into account leads to propagation of the error. This paper introduces a fuzzy weighted Kappa measure as a global statistic which takes both of these sources of uncertainty into account in classification accuracy assessment. Calculation of the fuzzy weighted Kappa measure involves three processes. First, a virtual confusion matrix is constructed to incorporate the fuzziness of location. Then a similarity weight matrix is derived to represent the fuzziness of the category. Combining the virtual confusion matrix and the similarity weight matrix, the fuzzy weighted Kappa is calculated using the weighted Kappa algorithms. The fuzzy weighted Kappa is shown to be more flexible than standard methods and gives results closer to an intuitive assessment of accuracy. Although calculating the fuzzy weighted Kappa involves a degree of subjectivity, this poses no major limitations on the usefulness of the measure. It is therefore proposed as an alternative classification accuracy measure for the remote sensing and GISc community.