“…e employment space and commuting scope of the urban population in the suburbs of New York were analyzed by using CDR data in different periods [34]. Urban activities have also been analyzed dynamically in Monza and Brianza province, Italy, using the amount of mobile phone conversations, messages, and the number of mobile switching center users in different time intervals [11]. However, some experts have mentioned the greater influence of density than volume for CDR data applications [35].…”
Section: Literature Reviewmentioning
confidence: 99%
“…In recent years, many studies have made full use of big data for urban land use classification or UFA detection [9,10]. For example, the number of regional mobile phone calls has been used to represent the characteristics of urban functions [11], and points of interest (POIs) data have been collected to demonstrate the land use of an area [12,13].…”
With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.
“…e employment space and commuting scope of the urban population in the suburbs of New York were analyzed by using CDR data in different periods [34]. Urban activities have also been analyzed dynamically in Monza and Brianza province, Italy, using the amount of mobile phone conversations, messages, and the number of mobile switching center users in different time intervals [11]. However, some experts have mentioned the greater influence of density than volume for CDR data applications [35].…”
Section: Literature Reviewmentioning
confidence: 99%
“…In recent years, many studies have made full use of big data for urban land use classification or UFA detection [9,10]. For example, the number of regional mobile phone calls has been used to represent the characteristics of urban functions [11], and points of interest (POIs) data have been collected to demonstrate the land use of an area [12,13].…”
With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.
“…Az utóbbi évtizedben a mobilcella adatokon alapuló elemzések azon gazdasági és társadalmi folyamatok feltárása során kerültek előtérbe, ahol a hagyományos statisztikával nehezen modellezhető, speciális térbeli vagy időbeli mozgásokat, folyamatokat vizsgáltak a kutatók. Ezek az adatok kiválóan alkalmazhatóak települési szint alatt, például egy adott város működésének megértését vagy a várostervezést segítő kutatásokhoz (MANFREDINI et al 2014, STEENBRUGGEN et al 2015, egyének vagy különböző társadalmi csoportok mozgási szokásainak feltárásához, vagy akár a mozgási útvonalak előrejelzéséhez (CALABRESE et al 2013, DOYLE et al 2014, TRASARTI et al 2017. A mobilcella adatok egyes városrészek, illetve nagyobb térségek közötti mobilitás kutatásakor is számos új információval szolgálnak, mint például az ingázás (WAN et al 2018) és a helyi/helyközi közlekedési módok (HUANG et al 2018, SHIN et al 2015.…”
A turizmus természetében az ezredfordulót követően kikristályosodó változások szükségszerűvé tették értelmezésének és mérésének újragondolását. A politikai szféra a turisztikai szervezetekkel, statisztikai intézetekkel, akadémiai kutatóhelyekkel együttműködve törekszik az utazással összefüggő új jelenségek gazdasági, társadalmi és környezeti hatásmechanizmusainak korszerű interpretálására. A turizmus hagyományos, a szálláshelyek igénybevételén alapuló értelmezése mára már a múlté. A szakpolitikának a stratégiaalkotás, a jogszabály-előkészítés, a fejlesztési irányok meghatározása és a támogatási rendszerek működtetése során a nemkonvencionális turisztikai mobilitásban rejlő lehetőségek kiaknázását is célszerű szem előtt tartania. Jelen tanulmány az elmélet és a gyakorlat határmezsgyéjén haladva (a hazai és a nemzetközi szakirodalom feldolgozására, valamint az UNWTO és a KSH adatbázisainak másodelemzésére építve) arra törekszik, hogy rávilágítson a statisztikai számbavételezésen kívül eső, úgynevezett láthatatlan turizmus hordozta potenciálra, amelynek felismerése helyi (térségi) és nemzetgazdasági szinten egyaránt jelentősen hozzájárulhat a turizmussal összefüggő bevételek növekedéséhez.
“…A common type of data is the data collected by cell phone base stations. Sometimes, cell phone providers interpolate the data collected by the base stations as is discussed in Manfredini et al [33]. Some researchers interpolate the data to obtain fine grained distributions as in Ratti et al [29].…”
Reconstructing fine-grained spatial densities from coarse-grained measurements, namely the aggregate observations recorded for each subregion in the spatial field of interest, is a critical problem in many real world applications. In this paper, we propose a novel Constrained Spatial Smoothing (CSS) approach for the problem of spatial data reconstruction. We observe that local continuity exists in many types of spatial data. Based on this observation, our approach performs sparse recovery via a finite element method, while in the meantime enforcing the aggregated observation constraints through an innovative use of the Alternating Direction Method of Multipliers (ADMM) algorithm framework. Furthermore, our approach is able to incorporate external information as a regression add-on to further enhance recovery performance. To evaluate our approach, we study the problem of reconstructing the spatial distribution of cellphone traffic volumes based on aggregate volumes recorded at sparsely scattered base stations. We perform extensive experiments based on a large dataset of Call Detail Records and a geographical and demographical attribute dataset from the city of Milan, and compare our approach with other methods such as Spatial Spline Regression. The evaluation results show that our approach significantly outperforms various baseline approaches. This proves that jointly modeling the underlying spatial continuity and the local features that characterize the heterogeneity of different locations can help improve the performance of spatial recovery.
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