NDLI: Real-time urban population monitoring using pervasive sensor network

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Finding non-dominated paths in uncertain road networks

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Automatic geographic metadata correction for sensor-rich video sequences

Scalable estimation of precision maps in a MapReduce framework

A concise summary of spatial anomalies and its application in efficient real-time driving behaviour monitoring

Differentially private publication of location entropy

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Managing massive trajectories on the cloud

Mining city-wide encounters in real-time

MoTrIS: a framework for route planning on multimodal transportation networks

GeoTrend: spatial trending queries on real-time microblogs

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Gloria: a batch friendly indexing and storage framework

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CDO: extremely high-throughput road distance computations on city road networks

A framework for evacuation hotspot detection after large scale disasters using location data from smartphones: case study of Kumamoto earthquake

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Atlas: on the expression of spatial-keyword group queries using extended relational constructs

Scalable 3D spatial queries for analytical pathology imaging with MapReduce

Simba: spatial in-memory big data analysis

LEDS: local event discovery and summarization from tweets

Automatic detection and matching of geospatial properties in transportation data sources (demo paper)

Predicting irregular individual movement following frequent mid-level disasters using location data from smartphones

Immersive tangible geospatial modeling

A SMART approach to quality assessment of site-based spatio-temporal data

Assisted journey recollections from photo streams: (demo paper)

A visual and computational analysis approach for exploring significant locations and time periods along a bus route

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Real-time urban population monitoring using pervasive sensor network

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ADCN: an anisotropic density-based clustering algorithm

DNN-based prediction model for spatio-temporal data

Unsupervised annotated city traffic map generation

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Location corroborations by mobile devices without traces

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A simple baseline for travel time estimation using large-scale trip data

A demonstration of SHAREK: an efficient matching framework for ride sharing systems

On-demand aggregation of gridded data over user-specified spatio-temporal domains

Quantitative evaluation of public spaces using crowd replication

RxSpatial: the reactive spatial library

Spatio-temporal modeling of the topology of swarm behavior with persistence landscapes

Polygon consensus: smart crowdsourcing for extracting building footprints from historical maps

Personalized location models with adaptive mixtures

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Multi-user routing to single destination with confluence

Location privacy for group meetups

A distantly supervised method for extracting spatio-temporal information from text

Scalable user assignment in power grids: a data driven approach

Spatio-temporal sentiment hotspot detection using geotagged photos

Cleansing indoor RFID data using regular expressions

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Real-time detection and classification of traffic jams from probe data

Real-time urban population monitoring using pervasive sensor network

Content Provider	ACM Digital Library
Author	Bobrek, Miljko Lu, Wei Thakur, Gautam S. Nutaro, James Liu, Cheng Kuruganti, Teja Killough, Stephen
Abstract	It is estimated that 50% of the global population lives in urban areas occupying just 0.4% of the Earth's surface. Understanding urban activity constitutes monitoring population density and its changes over time, in urban environments. Currently, there are limited mechanisms to non-intrusively monitor population density in real-time. The pervasive use of cellular phones in urban areas is one such mechanism that provides a unique opportunity to study population density by monitoring the mobility patterns in near real-time. Cellular carriers such as AT&T harvest such data through their cell towers; however, this data is proprietary and the carriers restrict access, due to privacy concerns. In this work, we propose a system that passively senses the population density and infers mobility patterns in an urban area by monitoring power spectral density in cellular frequency bands using periodic beacons from each cellphone without knowing who and where they are located. A wireless sensor network platform is being developed to perform spectral monitoring along with environmental measurements. Algorithms are developed to generate real-time fine-resolution population estimates.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450345897
DOI	10.1145/2996913.2996937
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Spectral monitoring Transportation Population dynamics Sensors
Content Type	Text
Resource Type	Article

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