scholarly journals Geosmartness for Personalized and Sustainable Future Urban Mobility

2021 ◽  
pp. 59-83
Author(s):  
Martin Raubal ◽  
Dominik Bucher ◽  
Henry Martin
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Information Technologies ◽  
Large Scale ◽  
Smart Cities ◽  
Mobile Technologies ◽  
Urban Mobility ◽  
Mobility Patterns ◽  
Movement Data ◽  
Spatio Temporal

AbstractUrban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.

mMarketing Opportunities for User Collaborative Environments in Smart Cities

2017 ◽  
pp. 1213-1241
Author(s):  
Artemis D. Avgerou ◽  
Despina A. Karayanni ◽  
Yannis C. Stamatiou
Keyword(s):  
Wireless Communications ◽  
Mobile Devices ◽  
Large Scale ◽  
Collective Intelligence ◽  
Personal Information ◽  
Smart Cities ◽  
Mobility Patterns ◽  
Communications Networks ◽  
Pollution Levels ◽  
Commercial Activities

Smart City infrastructures connect people with their devices through wireless communications networks while they offer sensor-based information about the city's status and needs. Connecting people carrying mobile devices equipped with sensors through such an infrastructure leads to the “collective intelligence” or “crowdsourcing” paradigm. This paradigm has been deployed in numerous contexts such as performing large-scale experiments (e.g., monitoring the pollution levels or analyzing mobility patterns of people to derive useful information about rush hours in cities) or gathering and sharing user collected experiences in efforts to increase privacy awareness and personal information protection levels. In this chapter, we will focus on employing this paradigm in the mMarketing/mCommerce domain and discuss how crowdsourcing can create new opportunities for commercial activities as well as expansion of existing ones.

scholarly journals Arctic avian predators synchronise their spring migration with the northern progression of snowmelt

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Teja Curk ◽  
Ivan Pokrovsky ◽  
Nicolas Lecomte ◽  
Tomas Aarvak ◽  
David F. Brinker ◽  
...  
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
The Arctic ◽  
Migratory Species ◽  
Migration Patterns ◽  
Predator Species ◽  
Movement Data ◽  
Breeding Grounds ◽  
Spatio Temporal ◽  
Spring Snowmelt

Abstract Migratory species display a range of migration patterns between irruptive (facultative) to regular (obligate), as a response to different predictability of resources. In the Arctic, snow directly influences resource availability. The causes and consequences of different migration patterns of migratory species as a response to the snow conditions remains however unexplored. Birds migrating to the Arctic are expected to follow the spring snowmelt to optimise their arrival time and select for snow-free areas to maximise prey encounter en-route. Based on large-scale movement data, we compared the migration patterns of three top predator species of the tundra in relation to the spatio-temporal dynamics of snow cover. The snowy owl, an irruptive migrant, the rough-legged buzzard, with an intermediary migration pattern, and the peregrine falcon as a regular migrant, all followed, as expected, the spring snowmelt during their migrations. However, the owl stayed ahead, the buzzard stayed on, and the falcon stayed behind the spatio-temporal peak in snowmelt. Although none of the species avoided snow-covered areas, they presumably used snow presence as a cue to time their arrival at their breeding grounds. We show the importance of environmental cues for species with different migration patterns.

scholarly journals Identifying multiscale spatio-temporal patterns in human mobility using manifold learning

2020 ◽  
Vol 6 ◽  
pp. e276 ◽  
Author(s):  
James R. Watson ◽  
Zach Gelbaum ◽  
Mathew Titus ◽  
Grant Zoch ◽  
David Wrathall
Keyword(s):  
Manifold Learning ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Network ◽  
Human Migration ◽  
Mobility Patterns ◽  
Great Opportunity ◽  
Spectral Graph ◽  
Spectral Graph Wavelets ◽  
Spatio Temporal

When, where and how people move is a fundamental part of how human societies organize around every-day needs as well as how people adapt to risks, such as economic scarcity or instability, and natural disasters. Our ability to characterize and predict the diversity of human mobility patterns has been greatly expanded by the availability of Call Detail Records (CDR) from mobile phone cellular networks. The size and richness of these datasets is at the same time a blessing and a curse: while there is great opportunity to extract useful information from these datasets, it remains a challenge to do so in a meaningful way. In particular, human mobility is multiscale, meaning a diversity of patterns of mobility occur simultaneously, which vary according to timing, magnitude and spatial extent. To identify and characterize the main spatio-temporal scales and patterns of human mobility we examined CDR data from the Orange mobile network in Senegal using a new form of spectral graph wavelets, an approach from manifold learning. This unsupervised analysis reduces the dimensionality of the data to reveal seasonal changes in human mobility, as well as mobility patterns associated with large-scale but short-term religious events. The novel insight into human mobility patterns afforded by manifold learning methods like spectral graph wavelets have clear applications for urban planning, infrastructure design as well as hazard risk management, especially as climate change alters the biophysical landscape on which people work and live, leading to new patterns of human migration around the world.

mMarketing Opportunities for User Collaborative Environments in Smart Cities

2017 ◽  
pp. 219-247
Author(s):  
Artemis D. Avgerou ◽  
Despina A. Karayanni ◽  
Yannis C. Stamatiou
Keyword(s):  
Wireless Communications ◽  
Mobile Devices ◽  
Large Scale ◽  
Collective Intelligence ◽  
Personal Information ◽  
Smart Cities ◽  
Mobility Patterns ◽  
Communications Networks ◽  
Pollution Levels ◽  
Commercial Activities

Smart City infrastructures connect people with their devices through wireless communications networks while they offer sensor-based information about the city's status and needs. Connecting people carrying mobile devices equipped with sensors through such an infrastructure leads to the “collective intelligence” or “crowdsourcing” paradigm. This paradigm has been deployed in numerous contexts such as performing large-scale experiments (e.g., monitoring the pollution levels or analyzing mobility patterns of people to derive useful information about rush hours in cities) or gathering and sharing user collected experiences in efforts to increase privacy awareness and personal information protection levels. In this chapter, we will focus on employing this paradigm in the mMarketing/mCommerce domain and discuss how crowdsourcing can create new opportunities for commercial activities as well as expansion of existing ones.

scholarly journals TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping

2019 ◽  
Vol 8 (11) ◽  
pp. 513 ◽  
Author(s):  
Luiz Fernando F. G. Assis ◽  
Karine Reis Ferreira ◽  
Lubia Vinhas ◽  
Luis Maurano ◽  
Claudio Almeida ◽  
...  
Keyword(s):  
Spatial Data ◽  
Web Application ◽  
Data Analytics ◽  
Large Scale ◽  
Computer Network ◽  
Service Architecture ◽  
Data Infrastructure ◽  
Spatio Temporal ◽  
Spatial Interfaces ◽  
The Government

The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific community to estimate the parameter heterogeneity of the Earth’s surface. Along with descriptions of features and statistics for aggregating spatio-temporal information, the government programs have disseminated thematic maps to further the implementation of effective public policies and foster sustainable development. In Brazil, PRODES and DETER have shown that they are committed to monitoring the mapping areas of large-scale deforestation systematically and by means of data quality assurance. However, these programs are so complex that they require the designing, implementation and deployment of a spatial data infrastructure based on extensive data analytics features so that users who lack a necessary understanding of standard spatial interfaces can still carry out research on them. With this in mind, the Brazilian National Institute for Space Research (INPE) has designed TerraBrasilis, a spatial data analytics infrastructure that provides interfaces that are not only found within traditional geographic information systems but also in data analytics environments with complex algorithms. To ensure it achieved its best performance, we leveraged a micro-service architecture with virtualized computer resources to enable high availability, lower size, simplicity to produce an increment, reliable to change and fault tolerance in unstable computer network scenarios. In addition, we tuned and optimized our databases both to adjust to the input format of complex algorithms and speed up the loading of the web application so that it was faster than other systems.

scholarly journals THE ROLE OF GEOMATICS ENGINEER IN SMART CITIES: A VIEW WITHIN THE FRAMEWORK OF TURKISH 2020–2023 NATIONAL SMART CITIES STRATEGY AND ACTION PLAN

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 333-341
Author(s):  
K. Konur ◽  
R. M. Alkan
Keyword(s):  
Spatial Data ◽  
Information Technologies ◽  
Smart Cities ◽  
Action Plan ◽  
Geographic Information ◽  
Geographic Information Technologies ◽  
Geographic Data ◽  
Technological Developments ◽  
Made In

Abstract. The development of technology resulted major revolutions in the cities. With the integration of technological developments into cities, the concept of smart cities began to emerge. Today, applications are made on smart cities in many countries. It is not possible to build a smart city without geographic data. It is one of the main duties of Geomatics Engineers to produce, use, process and finalize the geographic data and present it to the user. In this study, referring to the role of Geomatics Engineer in smart cities across Turkey 2020-2023 National Smart Cities Strategy and Action Plan framework is made in the investigations. When this plan is examined, it is seen that the importance of geographical/geo-spatial data and geo-information technologies for the realization of smart cities is an undeniable fact. In the 2020–2023 National Smart Cities Strategy and Action Plan, it has been clearly demonstrated that Geographic Information Systems and Geographic Information Technologies have a great role in creating smart cities.

scholarly journals Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences

2021 ◽  
Vol 13 (24) ◽  
pp. 13921
Author(s):  
Laiyun Wu ◽  
Samiul Hasan ◽  
Younshik Chung ◽  
Jee Eun Kang
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Mobility Models ◽  
Mobility Patterns ◽  
User Characteristics ◽  
Transit System ◽  
Demographic Groups ◽  
Urban Dynamics ◽  
User Groups ◽  
Spatio Temporal

Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics and modal preferences. We identified users’ commuting locations and estimated the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets.

On the Support of Mobility in ORDBMS

2014 ◽  
Vol 4 (1) ◽  
pp. 38-64
Author(s):  
Nikos Pelekis ◽  
Elias Frentzos ◽  
Nikos Giatrakos ◽  
Yannis Theodoridis
Keyword(s):  
Spatial Data ◽  
Data Types ◽  
Temporal Semantics ◽  
Movement Data ◽  
Data Framework ◽  
Object Relational ◽  
Data Definition ◽  
Object Databases ◽  
Spatio Temporal ◽  
Manipulation Language

Composition of space and mobility in a unified data framework results into Moving Object Databases (MOD). MOD management systems support storage and query processing of non-static spatial objects and provide essential operations for higher level analysis of movement data. The goal of this paper is to present Hermes MOD engine that supports the aforementioned functionality through appropriate data types and methods in Object-Relational DBMS (ORDBMS) environments. In particular, Hermes exploits on the extensibility interface of ORDBMS that already have extensions for static spatial data types and methods that follow the Open Geospatial Consortium (OGC) standard, and extends the ORDBMS by supporting time-varying geometries that change their position and/or extent in space and time dimensions, either discretely or continuously. It further extends the data definition and manipulation language of the ORDBMS with spatio-temporal semantics and functionality.

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