The research possibilities of commuting to work by the methods of complex network analysis

Pálóczi, Gábor

doi:10.15196/ts560202

Cited by 8 publications

(15 citation statements)

References 17 publications

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“…A hálózatelméleti elemzések a társadalomtudományokban legtöbbször szociális (Kincses-Bálint 2016, Pálóczi 2016, Broekel-Boschma 2016, Varga et al 2016, Álvarez-Díaz et al 2017) vagy vállalati hálók összefüggéseire irányulnak. A gazdasági alkalmazásuk sem példa nélküli, ezen belül a pénzügyi, banki vizsgálatok általában rendszerkockázat-elemzésre, illetve az ügyfélszerzéssel, ügyfélmegtartással kapcsolatos kérdésekre irányulnak.…”

Section: Az Adatstruktúra éS a Területi-hálózati Elemzések Kombinálási Lehetőségeiunclassified

A hazai fizetési forgalom területi aspektusa

Kuttor¹,

Pál²

2019

Ter. Stat.

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Section: Az Adatstruktúra éS a Területi-hálózati Elemzések Kombinálási Lehetőségeiunclassified

A hazai fizetési forgalom területi aspektusa

Kuttor¹,

Pál²

2019

Ter. Stat.

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“…In Hungary, the forms of 'traditional' annual traffic surveys cannot analyse the patterns of daily traffic flows on a deep spatial level, and local municipalities have also limited capacity to make their own traffic measurements. Hence, using Google Maps data we can extend our knowledge based on traditional census data regarding commuting (e. g. Pálóczi, G. 2016;Varga, L. et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Using Google Maps road traffic estimations to unfold spatial and temporal inequalities of urban road congestion: A pilot study from Budapest

Baji

2018

HunGeoBull

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In recent urban geography literature, smart cites became a fashionable subject. The smart city paradigm is strongly connected to researches based on big data. The main objective of this paper is to strengthen the idea of usefulness of big data in examining and developing the urban transportation system as part of smart cities. In this pilot research, Google Maps traffic estimation data were used to evaluate the special vehicular traffic flows in District 3, in Budapest. On 45 defined road sections, travel time estimation data were collected with the aim to calculate the extent of wasted time in traffic jams. According to our results this source of 'big data' is a feasible way of conducting 'smart' research on a city road system. The most relevant advantage of this database is that it is continually generated on a high spatial and temporal resolution. The conclusion of this pilot research is that spatial and temporal inequalities are evincible from this database, unrecognized processes can be easily analysed, which can help urban planners to rethink their strategies on urban transport system. The most important findings showed that, on workdays, there is a second wave of peak traffic on many roads, and, within our chosen district, there are congestion hot spot places. It is important to note that Google Maps data have limits, but by understanding them this method is a useful way for geographers to examine urban traffic congestion patterns with a high spatial resolution.

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“…Let be the origin-destination commuting matrix with th entry equal to the commuting flow from area to area . Following De Montis et al 2013, Pálóczi (2016), and Adam et al 2018, we measure the strength of the commuting flows between areas via the symmetric matrix…”

Section: Delineating Functional Lmasmentioning

confidence: 99%

“…The Louvain algorithm is one of many algorithms that partition the nodes in a network into "communities" that "share common properties and/or play similar roles" (Fortunato, 2010). It is applied in several studies of local labour markets (De Montis, Caschili, & Chessa, 2013;Pálóczi, 2016;Thomas, Adam, & Verhetsel, 2017;Adam, Delvenne, & Thomas, 2018;Dannemann, Sotomayor-Gómez, & Samaniego, 2018). Other algorithms have different objectives, such as maximising random walk containment (Pons & Latapy, 2006) or minimising the random walk description lengths (Rosvall & Bergstrom, 2008).…”

Section: The Louvain Algorithmmentioning

confidence: 99%