TY - JOUR AU - Juhász, Sándor AU - Pintér, Gergő AU - Kovács, Ádám J. AU - Borza, Endre Márk AU - Mónus, Gergely AU - Lőrincz, László AU - Lengyel, Balázs TI - Amenity complexity and urban locations of socio-economic mixing JF - EPJ DATA SCIENCE J2 - EPJ DATA SCI VL - 12 PY - 2023 IS - 1 PG - 18 SN - 2193-1127 DO - 10.1140/epjds/s13688-023-00413-6 UR - https://m2.mtmt.hu/api/publication/34129889 ID - 34129889 AB - Cities host diverse people and their mixing is the engine of prosperity. In turn, segregation and inequalities are common features of most cities and locations that enable the meeting of people with different socio-economic status are key for urban inclusion. In this study, we adopt the concept of economic complexity to quantify the sophistication of amenity supply at urban locations. We propose that neighborhood complexity and amenity complexity are connected to the ability of locations to attract diverse visitors from various socio-economic backgrounds across the city. We construct the measures of amenity complexity based on the local portfolio of diverse and non-ubiquitous amenities in Budapest, Hungary. Socio-economic mixing at visited third places is investigated by tracing the daily mobility of individuals and by characterizing their status by the real-estate price of their home locations. Results suggest that measures of ubiquity and diversity of amenities do not, but neighborhood complexity and amenity complexity are correlated with the urban centrality of locations. Urban centrality is a strong predictor of socio-economic mixing, but both neighborhood complexity and amenity complexity add further explanatory power to our models. Our work combines urban mobility data with economic complexity thinking to show that the diversity of non-ubiquitous amenities, central locations, and the potentials for socio-economic mixing are interrelated. LA - English DB - MTMT ER - TY - JOUR AU - Pintér, Gergő AU - Felde, Imre TI - Commuting Analysis of the Budapest Metropolitan Area Using Mobile Network Data JF - ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION J2 - ISPRS INT J GEO-INFORMATION VL - 11 PY - 2022 IS - 9 PG - 20 SN - 2220-9964 DO - 10.3390/ijgi11090466 UR - https://m2.mtmt.hu/api/publication/33075302 ID - 33075302 AB - The analysis of human movement patterns based on mobile network data makes it possible to examine a very large population cost-effectively and has led to several discoveries about human dynamics. However, the application of this data source is still not common practice. The goal of this study was to analyze the commuting tendencies of the Budapest Metropolitan Area using mobile network data as a case study and propose an automatized alternative approach to the current, questionnaire-based method, as commuting is predominantly analyzed by the census, which is performed only once in a decade in Hungary. To analyze commuting, the home and work locations of cell phone subscribers were determined based on their appearances during and outside working hours. The detected home locations of the subscribers were compared to census data at a settlement level. Then, the settlement and district level commuting tendencies were identified and compared to the findings of census-based sociological studies. It was found that the commuting analysis based on mobile network data strongly correlated with the census-based findings, even though home and work locations were estimated by statistical methods. All the examined aspects, including commuting from sectors of the agglomeration to the districts of Budapest and the age-group-based distribution of the commuters, showed that mobile network data could be an automatized, fast, cost-effective, and relatively accurate way of analyzing commuting, that could provide a powerful tool for sociologists interested in commuting. LA - English DB - MTMT ER - TY - THES AU - Pintér, Gergő TI - Analyzing the Mobility Customs of the Urban Population Using Mobile Network Data PB - Óbudai Egyetem PY - 2022 SP - 110 UR - https://m2.mtmt.hu/api/publication/32901873 ID - 32901873 LA - English DB - MTMT ER - TY - CHAP AU - Kerecsen, Szabo AU - Pintér, Gergő AU - Felde, Imre ED - Szakál, Anikó TI - Evaluating the Socioeconomic Status of a Large Social Event Attendees T2 - IEEE 16th International Symposium on Applied Computational Intelligence and Informatics SACI 2022 PB - IEEE CY - Temesvár SN - 9781665481243 PY - 2022 SP - 77 EP - 80 PG - 4 UR - https://m2.mtmt.hu/api/publication/32873276 ID - 32873276 LA - English DB - MTMT ER - TY - JOUR AU - Pintér, Gergő AU - Felde, Imre TI - Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data JF - INFORMATION (BASEL) J2 - INFORMATION-BASEL VL - 13 PY - 2022 IS - 3 SN - 2078-2489 DO - 10.3390/info13030114 UR - https://m2.mtmt.hu/api/publication/32753195 ID - 32753195 LA - English DB - MTMT ER - TY - JOUR AU - Pintér, Gergő AU - Felde, Imre TI - Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study JF - INFORMATION (BASEL) J2 - INFORMATION-BASEL VL - 12 PY - 2021 IS - 11 SP - 468 SN - 2078-2489 DO - 10.3390/info12110468 UR - https://m2.mtmt.hu/api/publication/32494626 ID - 32494626 LA - English DB - MTMT ER - TY - JOUR AU - Pintér, Gergő AU - Felde, Imre TI - Evaluating the Effect of the Financial Status to the Mobility Customs JF - ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION J2 - ISPRS INT J GEO-INFORMATION VL - 10 PY - 2021 IS - 5 SP - 328 EP - 349 PG - 21 SN - 2220-9964 DO - 10.3390/ijgi10050328 UR - https://m2.mtmt.hu/api/publication/32014438 ID - 32014438 AB - In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs. LA - English DB - MTMT ER - TY - JOUR AU - Pintér, Gergő AU - Mosavi, Amirhosein AU - Felde, Imre TI - Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach JF - ENTROPY J2 - ENTROPY-SWITZ VL - 22 PY - 2020 IS - 12 SP - 1421 SN - 1099-4300 DO - 10.3390/e22121421 UR - https://m2.mtmt.hu/api/publication/31807406 ID - 31807406 LA - English DB - MTMT ER - TY - CHAP AU - Mojrian, Sanaz AU - Pintér, Gergő AU - Joloudari, Javad Hassannataj AU - Felde, Imre AU - Szabo-Gali, Akos AU - Nádai, László AU - Mosavi, Amirhosein ED - Dinh, M ED - Felipe, A L ED - Dang-Pham, D TI - Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System T2 - The 2020 RIVF International Conference on Computing & Communication Technologies (RIVF) PB - Institute of Electrical and Electronics Engineers (IEEE) CY - New York, New York SN - 9781728153773 PY - 2020 SP - 1 EP - 7 PG - 7 DO - 10.1109/RIVF48685.2020.9140744 UR - https://m2.mtmt.hu/api/publication/31387078 ID - 31387078 N1 - Uni. of Science and Technology, Department of Information Technology Mazandaran, Babol, Iran Obuda University, John von Neumann Faculty of Informatics, Budapest, Hungary University of Birjand, Electrical and Computer Engineering Faculty, Birjand, Iran J. Selye University, Department of Mathematics and Informatics, Komarno, Slovakia Cited By :14 Export Date: 15 November 2022 LA - English DB - MTMT ER - TY - JOUR AU - Pintér, Gergő AU - Felde, Imre AU - Mosavi, Amirhosein AU - Ghamisi, Pedram AU - Gloaguen, Richard TI - COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach JF - MATHEMATICS J2 - MATHEMATICS-BASEL VL - 8 PY - 2020 IS - 6 SP - 890 PG - 20 SN - 2227-7390 DO - 10.3390/math8060890 UR - https://m2.mtmt.hu/api/publication/31333491 ID - 31333491 N1 - John von Neumann Faculty of Informatics, Obuda University, Budapest, 1034, Hungary Faculty of Civil Engineering, Technische Universität Dresden, Dresden, 01069, Germany Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, 1034, Hungary Thuringian Institute of Sustainability and Climate Protection, Jena, 07743, Germany Department of Mathematics, J. Selye University, Komarno, 94501, Slovakia Machine Learning Group, Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, Freiberg, 09599, Germany Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, Freiberg, 09599, Germany Cited By :148 Export Date: 15 November 2022 Correspondence Address: Mosavi, A.; Faculty of Civil Engineering, Germany; email: amir.mosavi@mailbox.tu-dresden.de LA - English DB - MTMT ER -