Use and validation of location-based services in urban research: An example with Dutch restaurants

Urban Studies ◽  
2018 ◽  
Vol 56 (5) ◽  
pp. 868-884 ◽  
Author(s):  
Daniel Arribas-Bel ◽  
Jessie Bakens
Keyword(s):  
Big Data ◽  
The Netherlands ◽  
Detailed Analysis ◽  
Urban Geography ◽  
Location Based Services ◽  
Urban Landscapes ◽  
Location Based Service ◽  
Economic Data ◽  
Urban Research ◽  
Urban Amenities

This article focuses on the use of big data for urban geography research. We collect data from the location-based service Foursquare in The Netherlands and employ it to obtain a rich catalogue of restaurant locations and other urban amenities, as well as a measure of their popularity among users. Because the Foursquare data can be combined with traditional sources of socio-economic data obtained from Statistics Netherlands, we can quantify, document and characterise some of the biases inherent in these new sources of data in the context of urban applications. A detailed analysis is given as to when this type of big data is useful and when it is misleading. Although the users of Foursquare are not representative of the whole population, we argue that this inherent bias can be exploited for research about the attractiveness of urban landscapes and consumer amenities in addition to the more traditional data on urban amenities.

A Stream Data Processing Framework for Location-Based Service Using NoSQL Technology

2015 ◽  
Vol 763 ◽  
pp. 159-163 ◽  
Author(s):  
Nan Ju Kim ◽  
Eui In Choi
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Streaming Data ◽  
Location Based Services ◽  
Location Based Service ◽  
Stream Data ◽  
Data Processing Method ◽  
Specific Device ◽  
Internet Development ◽  
Processing Framework

One of the most exciting changes in Location-Based Services is the incredible growth of internet, development of wearable devices, and advanced positioning technologies. In addition, the big data from those sources helps performing seamless LBS as a technology. The existing processing methods used to detect the location of a particular tag, or specific device are not enough for complex processing while collecting all of the streaming data at the same time using a variety of wireless communication system [10,11,12,13]. We can use big data processing method for processing all the streaming data in real time. In this paper, we propose a framework for improving performance of Seamless LBS using NoSQL technology.

scholarly journals Mobile Tour Planning Using Landmark Photo Matching and Intelligent Character Recognition

2012 ◽  
Vol 182-183 ◽  
pp. 854-859 ◽  
Author(s):  
Cheng Ming Huang ◽  
Wen Hung Liao ◽  
Sheng Chih Chen
Keyword(s):  
Mobile Application ◽  
Character Recognition ◽  
Location Based Services ◽  
Smart Phones ◽  
Location Based Service ◽  
Voice Communication ◽  
Tourist Destinations ◽  
Travel Experience ◽  
Intelligent Character Recognition ◽  
Tourist Information

The functionalities of smart phones have extended from basic voice communication to gaming, multimedia entertainment, information retrieval and location-based services. In this paper, we attempt to design a mobile application to assist visitors to have better understandings of popular tourist destinations and related routing information while on tour. The users can obtain descriptions of a specific attraction by simply taking the picture of a landmark photo often shown in the travel booklet using their mobile devices. This is achieved by matching the landmark picture with an image database containing popular tourist spots to locate the interested destination. The location information is further confirmed using techniques in intelligent character recognition. Upon successful identification of the interested location, tourist information regarding this destination, along with the routing details will be delivered using location-based service. We anticipate the proposed mobile application to effectively assist foreign visitors by bringing comprehensive, up-to-date tourist information and promoting better travel experience.

scholarly journals Challenges and Countermeasures Faced by Enterprise Economic Operation Analysis in the Big Data Era

2021 ◽  
Vol 251 ◽  
pp. 01049
Author(s):  
Yang Xin ◽  
Yinning He
Keyword(s):  
Big Data ◽  
Economic Analysis ◽  
Economic Data ◽  
Multiple Impacts ◽  
Economic Activities ◽  
Economic Management ◽  
Management Mechanism ◽  
Operation Analysis ◽  
Economic Operation ◽  
Data Environment

The analysis of enterprise economic operation is mainly to collect, integrate, analyze and store information on the economic activities of the enterprise, so as to realize the standardization and management of the overall economic activities of the enterprise. In the era of big data, the analysis of corporate economic operations has also changed. In short, the big data environment has changed the corporate economic analysis and management mechanism, and has changed the collection of corporate operating economic data. It also has multiple impacts on data analysis and changed the overall structure of corporate economic analysis. This article will briefly discuss the challenges and countermeasures facing enterprise economic operation analysis in the era of big data, hoping to provide a reference for enterprise economic management.

scholarly journals Exploring the regulatory challenges of a possible rollout of smart water meters in the Netherlands

2018 ◽  
Vol 19 (3-4) ◽  
pp. 159-179
Author(s):  
Brenda Espinosa Apráez ◽  
Saskia Lavrijssen
Keyword(s):  
Drinking Water ◽  
Big Data ◽  
The Netherlands ◽  
Goods And Services ◽  
Smart Water ◽  
Water Meters ◽  
Regulatory Challenges

Big data have become a driver of innovation in multiple sectors, including the management of infrastructures employed for the provision of essential goods and services, such as drinking water. As technology enables new possibilities of action for infrastructure managers, it could be questioned whether the regulations in place still deal adequately with such possibilities or if certain adjustments are necessary, especially considering that infrastructure managers usually operate in highly regulated environments. This study explores the regulatory challenges of introducing smart water meters (SWM) in the Netherlands. In particular, it discusses whether the introduction of SWM will require adjusting the regulations of the sector, to deal with the new possibilities of action enabled by this technology.

Role of Urban Big Data in Travel Behavior Research

2020 ◽  
pp. 036119812097502
Author(s):  
Chihuangji Wang ◽  
Daniel Baldwin Hess
Keyword(s):  
Big Data ◽  
Survey Data ◽  
Travel Behavior ◽  
Data Science ◽  
Location Based Service ◽  
Labor Costs ◽  
Public And Private ◽  
Public And Private Sector ◽  
Data Analytic

Understanding urban travel behavior (TB) is critical for advancing urban transportation planning practice and scholarship; however, traditional survey data is expensive (because of labor costs) and error-prone. With advances in data collection techniques and data analytic approaches, urban big data (UBD) is currently generated at an unprecedented scale in relation to volume, variety, and speed, producing new possibilities for applying UBD for TB research. A review of more than 50 scholarly articles confirms the remarkable and expanding role of UBD in TB research and its advantages over traditional survey data. Using this body of published work, a typology is developed of four key types of UBD—social media, GPS log, mobile phone/location-based service, and smart card—focusing on the features and applications of each type in the context of TB research. This paper discusses in significant detail the opportunities and challenges in the use of UBD from three perspectives: conceptual, methodological, and political. The paper concludes with recommendations for researchers to develop data science knowledge and programming skills for analysis of UBD, for public and private sector agencies to cooperate on the collection and sharing of UBD, and for legislators to enforce data security and confidentiality. UBD offers both researchers and practitioners opportunities to capture urban phenomena and deepen knowledge about the TB of individuals.

scholarly journals Deep Intelligent Prediction Network: A Novel Deep Learning Based Prediction Model on Spatiotemporal Characteristics and Location Based Services for Big Data Driven Intelligent Transportation System

2019 ◽  
Vol 8 (11) ◽  
pp. 845-849
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Recommendation System ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Location Based Services ◽  
Traffic Data ◽  
Spatiotemporal Characteristics

The concept of big Data for intelligent transportation system has been employed for traffic management on dealing with dynamic traffic environments. Big data analytics helps to cope with large amount of storage and computing resources required to use mass traffic data effectively. However these traditional solutions brings us unprecedented opportunities to manage transportation data but it is inefficient for building the next-generation intelligent transportation systems as Traffic data exploring in velocity and volume on various characteristics. In this article, a new deep intelligent prediction network has been introduced that is hierarchical and operates with spatiotemporal characteristics and location based service on utilizing the Sensor and GPS data of the vehicle in the real time. The proposed model employs deep learning architecture to predict potential road clusters for passengers. It is injected as recommendation system to passenger in terms of mobile apps and hardware equipment employment on the vehicle incorporating location based services models to seek available parking slots, traffic free roads and shortest path for reach destination and other services in the specified path etc. The underlying the traffic data is classified into clusters with extracting set of features on it. The deep behavioural network processes the traffic data in terms of spatiotemporal characteristics to generate the traffic forecasting information, vehicle detection, autonomous driving and driving behaviours. In addition, markov model is embedded to discover the hidden features .The experimental results demonstrates that proposed approaches achieves better results against state of art approaches on the performance measures named as precision, execution time, feasibility and efficiency.

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