Projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018.

As of May 27, 2018, 6 suspected, 13 probable and 35 confirmed cases of Ebola virus disease (EVD) had been reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the total outbreak size and duration with and without vaccine use. We modeled Ebola virus transmission using a stochastic branching process model that included reproduction numbers from past Ebola outbreaks and a particle filtering method to generate a probabilistic projection of the outbreak size and duration conditioned on its reported trajectory to date; modeled using high (62%), low (44%), and zero (0%) estimates of vaccination coverage (after deployment). Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize the Thiel-Sen regression model predicting the outbreak size from the number of observed cases from April 4 to May 27. We used these techniques on probable and confirmed case counts with and without inclusion of suspected cases. Probabilistic projections were scored against the actual outbreak size of 54 EVD cases, using a log-likelihood score. With the stochastic model, using high, low, and zero estimates of vaccination coverage, the median outbreak sizes for probable and confirmed cases were 82 cases (95% prediction interval [PI]: 55, 156), 104 cases (95% PI: 58, 271), and 213 cases (95% PI: 64, 1450), respectively. With the Thiel-Sen regression model, the median outbreak size was estimated to be 65.0 probable and confirmed cases (95% PI: 48.8, 119.7). Among our three mathematical models, the stochastic model with suspected cases and high vaccine coverage predicted total outbreak sizes closest to the true outcome. Relatively simple mathematical models updated in real time may inform outbreak response teams with projections of total outbreak size and duration

Forest and Peatland Fire Severity Assessment at Siak Regency, Riau Province using Sentinel-2 Imagery

Siak Regency, Riau Province is one of the most forest and land fire-prone regencies in Indonesia. Most of the fires occur in peatland areas which contributes to the transboundary haze pollution in the region. Despite limited studies, fire severity assessment is an essential step in post-fire activities to estimate ecological impacts and economic impacts and law enforcement.  This study aims to estimate fire severity using Sentinel-2 imagery at Siak Regency, Riau Province. The methods applied Normalized Burn Ratio on Sentinel-2 Imagery as an identification model based on reflectance value for 2019 imagery. The study revealed that burned areas in Siak Regency could be classified into four fire severity classes: low fire severity, moderate-low fire severity, moderate-high fire severity, and high fire severity. High fire severity was found mainly at Sungai Apit and Mempura Districts

Hierarchical accompanying and inhibiting patterns on the spatial arrangement of taxis' local hotspots

Due to the large volume of recording, the complete spontaneity, and the flexible pick-up and drop-off locations, taxi data portrays a realistic and detailed picture of urban space use to a certain extent. The spatial arrangement of pick-up and drop-off hotspots reflects the organizational space, which has received attention in urban structure studies. Previous studies mainly explore the hotspots at a large scale by visual analysis or some simple indexes, where the hotspots usually cover the entire central business district, train stations, or dense residential areas, reaching a radius of hundreds or even thousands of meters. However, the spatial arrangement patterns of small-scale hotspots, reflecting the specific popular pick-up and drop-off locations, have not received much attention. Using two taxi trajectory datasets in Wuhan and Beijing, China, this study quantitatively explores the spatial arrangement of fine-grained pick-up and drop-off local hotspots with different levels of popularity, where the sizes are adaptively set as 90m*90m in Wuhan and 105m*105m in Beijing according to the local hotspot identification method. Results show that popular hotspots tend to be surrounded by less popular hotspots, but the existence of less popular hotspots is inhibited in regions with a large number of popular hotspots. We use the terms hierarchical accompany and inhibiting patterns for these two spatial configurations. Finally, to uncover the underlying mechanism, a KNN-based model is proposed to reproduce the spatial distribution of other less popular hotspots according to the most popular ones. These findings help decision-makers construct reasonable urban minimum units for precise traffic and disease control, as well as plan a more humane spatial arrangement of points of interest

Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning

Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-demand gap, and then providing efficient incentives to encourage participant movements to maintain the desired balance. In this paper, we propose Deep-Gap, a deep learning approach based on residual learning to predict the gap between mobile crowdsourced service supply and demand at a given time and space. The prediction can drive the incentive model to achieve a geographically balanced service coverage in order to avoid the case where some areas are over-supplied while other areas are under-supplied. This allows anticipating the supply-demand gap and redirecting crowdsourced service providers towards target areas. Deep-Gap relies on historical supply-demand time series data as well as available external data such as weather conditions and day type (e.g., weekday, weekend, holiday). First, we roll and encode the time series of supply-demand as images using the Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF) and the Recurrence Plot (REC). These images are then used to train deep Convolutional Neural Networks (CNN) to extract the low and high-level features and forecast the crowdsourced services gap. We conduct comprehensive comparative study by establishing two supply-demand gap forecasting scenarios: with and without external data. Compared to state-of-art approaches, Deep-Gap achieves the lowest forecasting errors in both scenarios.Comment: Accepted at CloudCom 2019 Conferenc

Model for Spatiotemporal Crime Prediction with Improved Deep Learning

Crime is hard to anticipate since it occurs at random and can occur anywhere at any moment, making it a difficult issue for any society to address. By analyzing and comparing eight known prediction models: Naive Bayes, Stacking, Random Forest, Lazy:IBK, Bagging, Support Vector Machine, Convolutional Neural Network, and Locally Weighted Learning – this study proposed an improved deep learning crime prediction model using convolutional neural networks and the xgboost algorithm to predict crime. The major goal of this research is to provide an improved crime prediction model based on previous criminal records. Using the Boston crime dataset, where our larceny crime dataset was extracted, exploratory data analysis (EDA) is used to uncover patterns and explain trends in crimes. The performance of the proposed model on the basis of accuracy, recall, and f-measure was 100% outperforming the other models used in this study. The analysis of the proposed model and prediction can aid security services in making better use of their resources, anticipating crime at a certain time, and serving the society better

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Ranking places in attributed temporal urban mobility networks

Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of “hotspots” of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of “hotspots” and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.This work is supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P. It is also funded by the EU H2020 programme under Grant Agreement No. 780754, “Track & Know”

HotSketch: Drawing Police Patrol Routes among Spatiotemporal Crime Hotspots

During the course of a day, a police unit is expected to move throughout the city to provide a visible presence and respond quickly to emergencies. Planning this movement at the beginning of the shift can provide a helpful first step in ensuring that officers are present in areas of high crime, but these plans can quickly break down as they are pulled away to 911 calls. Once such an initial plan is deferred, police units need to be able to rapidly and fluidly decide where to go next depending on their immediate location and time. In this paper, we present our research to couple spatiotemporal analysis of historical crime data with sketch-based interaction methods. This research is presented through an initial prototype, HotSketch, which we describe through a set of use cases within the domain of police patrol route planning