3,153 research outputs found
TPM: A GPS-based Trajectory Pattern Mining System
With the development of big data and artificial intelligence, the technology
of urban computing becomes more mature and widely used. In urban computing,
using GPS-based trajectory data to discover urban dense areas, extract similar
urban trajectories, predict urban traffic, and solve traffic congestion
problems are all important issues. This paper presents a GPS-based trajectory
pattern mining system called TPM. Firstly, the TPM can mine urban dense areas
via clustering the spatial-temporal data, and automatically generate
trajectories after the timing trajectory identification. Mainly, we propose a
method for trajectory similarity matching, and similar trajectories can be
extracted via the trajectory similarity matching in this system. The TPM can be
applied to the trajectory system equipped with the GPS device, such as the
vehicle trajectory, the bicycle trajectory, the electronic bracelet trajectory,
etc., to provide services for traffic navigation and journey recommendation.
Meantime, the system can provide support in the decision for urban resource
allocation, urban functional region identification, traffic congestion and so
on
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Stacking-based visualization of trajectory attribute data
Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well
Does economic geography matter for Pakistan? a spatial exploratory analysis of income and education inequalities
Generally, econometric studies on socio-economic inequalities consider regions as independent entities, ignoring the likely possibility of spatial interaction between them. This interaction may cause spatial dependency or clustering, which is referred to as spatial autocorrelation. This paper analyzes for the first time, the spatial clustering of income, income inequality, education, human development, and growth by employing spatial exploratory data analysis (ESDA) techniques to data on 98 Pakistani districts. By detecting outliers and clusters, ESDA allows policy makers to focus on the geography of socio-economic regional characteristics. Global and local measures of spatial autocorrelation have been computed using the Moran’s I and the Geary’s C index to obtain estimates of the spatial autocorrelation of spatial disparities across districts. The overall finding is that the distribution of district wise income inequality, income, education attainment, growth, and development levels, exhibits a significant tendency for socio-economic inequalities and human development levels to cluster in Pakistan (i.e. the presence of spatial autocorrelation is confirmed).Spatial effects; spatial exploratory analysis; spatial disparities; income inequality; education inequality; spatial autocorrelation
A statistical approach for studying urban human dynamics
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThis doctoral dissertation proposed several statistical approaches to analyse urban dynamics with
aiming to provide tools for decision making processes and urban studies. It assumed that human
activity and human mobility compose urban dynamics. Initially, it studied geolocated social media
data and considered them as a proxy for where and when people carry out what it is defined as the
human activity. It employed techniques associated with generalised linear models, functional data
analysis, hierarchical clustering, and epidemic data, to explain the spatio-temporal distribution
of the places where people interact with their social networks. Afterwards, to understand the
mobility in urban environments, data coming from an underground railway system were used.
The information was considered repeated daily measurements to capture the regularity of
human behaviour. By implementing methods from functional principal components data analysis
and hierarchical clustering, it was possible to describe the system and identify human mobility
patterns
Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements
Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe identification and monitoring of informal settlements in urban areas is an important
step in developing and implementing pro-poor urban policies. Understanding when,
where and who lives inside informal settlements is critical to efforts to improve their
resilience. This study aims at integrating OSM data and sentinel-2 imagery for
classifying and monitoring the growth of informal settlements methods to map informal
areas in Kampala (Uganda) and Dar es Salaam (Tanzania) and to monitor their growth
in Kampala. Three building feature characteristics of size, shape and Distance to nearest
Neighbour were derived and used to cluster and classify informal areas using Hotspot
Cluster analysis and ML approach on OSM buildings data. The resultant informal
regions in Kampala were used with Sentinel-2 image tiles to investigate the spatiotemporal
changes in informal areas using Convolutional Neural Networks (CNNs).
Results from Optimized Hot Spot Analysis and Random Forest Classification show that
Informal regions can be mapped based on building outline characteristics. An accuracy
of 90.3% was achieved when an optimally trained CNN was executed on a test set of
2019 satellite image tiles. Predictions of informality from new datasets for the years
2016 and 2017 provided promising results on combining different open source
geospatial datasets to identify, classify and monitor informal settlements
Data from mobile phone operators: A tool for smarter cities?
Abstract The use of mobile phone data provides new spatio-temporal tools for improving urban planning, and for reducing inefficiencies in present-day urban systems. Data from mobile phones, originally intended as a communication tool, are increasingly used as innovative tools in geography and social sciences research. Empirical studies on complex city systems from human-centred and urban dynamics perspectives provide new insights to develop promising applications for supporting smart city initiatives. This paper provides a comprehensive review and a typology of spatial studies on mobile phone data, and highlights the applicability of such digital data to develop innovative applications for enhanced urban management
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