@article{MTMT:34129889,
	title = {Amenity complexity and urban locations of socio-economic mixing},
	url = {https://m2.mtmt.hu/api/publication/34129889},
	author = {Juhász, Sándor and Pintér, Gergő and Kovács, Ádám J. and Borza, Endre Márk and Mónus, Gergely and Lőrincz, László and Lengyel, Balázs},
	doi = {10.1140/epjds/s13688-023-00413-6},
	journal-iso = {EPJ DATA SCI},
	journal = {EPJ DATA SCIENCE},
	volume = {12},
	unique-id = {34129889},
	issn = {2193-1127},
	abstract = {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.},
	year = {2023},
	eissn = {2193-1127},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816; Kovács, Ádám J./0000-0001-6503-3047; Borza, Endre Márk/0000-0002-8804-4520}
}

@article{MTMT:33075302,
	title = {Commuting Analysis of the Budapest Metropolitan Area Using Mobile Network Data},
	url = {https://m2.mtmt.hu/api/publication/33075302},
	author = {Pintér, Gergő and Felde, Imre},
	doi = {10.3390/ijgi11090466},
	journal-iso = {ISPRS INT J GEO-INFORMATION},
	journal = {ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION},
	volume = {11},
	unique-id = {33075302},
	abstract = {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.},
	year = {2022},
	eissn = {2220-9964},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816; Felde, Imre/0000-0003-4126-2480}
}

@mastersthesis{MTMT:32901873,
	title = {Analyzing the Mobility Customs of the Urban Population Using Mobile Network Data},
	url = {https://m2.mtmt.hu/api/publication/32901873},
	author = {Pintér, Gergő},
	publisher = {Obuda University},
	unique-id = {32901873},
	year = {2022},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816}
}

@inproceedings{MTMT:32873276,
	title = {Evaluating the Socioeconomic Status of a Large Social Event Attendees},
	url = {https://m2.mtmt.hu/api/publication/32873276},
	author = {Kerecsen, Szabo and Pintér, Gergő and Felde, Imre},
	booktitle = {IEEE 16th International Symposium on Applied Computational Intelligence and Informatics SACI 2022},
	unique-id = {32873276},
	year = {2022},
	pages = {77-80},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816}
}

@article{MTMT:32753195,
	title = {Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data},
	url = {https://m2.mtmt.hu/api/publication/32753195},
	author = {Pintér, Gergő and Felde, Imre},
	doi = {10.3390/info13030114},
	journal-iso = {INFORMATION-BASEL},
	journal = {INFORMATION (BASEL)},
	volume = {13},
	unique-id = {32753195},
	year = {2022},
	eissn = {2078-2489},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816}
}

@article{MTMT:32494626,
	title = {Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study},
	url = {https://m2.mtmt.hu/api/publication/32494626},
	author = {Pintér, Gergő and Felde, Imre},
	doi = {10.3390/info12110468},
	journal-iso = {INFORMATION-BASEL},
	journal = {INFORMATION (BASEL)},
	volume = {12},
	unique-id = {32494626},
	year = {2021},
	eissn = {2078-2489},
	pages = {468},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816; Felde, Imre/0000-0003-4126-2480}
}

@article{MTMT:32014438,
	title = {Evaluating the Effect of the Financial Status to the Mobility Customs},
	url = {https://m2.mtmt.hu/api/publication/32014438},
	author = {Pintér, Gergő and Felde, Imre},
	doi = {10.3390/ijgi10050328},
	journal-iso = {ISPRS INT J GEO-INFORMATION},
	journal = {ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION},
	volume = {10},
	unique-id = {32014438},
	abstract = {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.},
	year = {2021},
	eissn = {2220-9964},
	pages = {328-349},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816; Felde, Imre/0000-0003-4126-2480}
}

@article{MTMT:31807406,
	title = {Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach},
	url = {https://m2.mtmt.hu/api/publication/31807406},
	author = {Pintér, Gergő and Mosavi, Amirhosein and Felde, Imre},
	doi = {10.3390/e22121421},
	journal-iso = {ENTROPY-SWITZ},
	journal = {ENTROPY},
	volume = {22},
	unique-id = {31807406},
	year = {2020},
	eissn = {1099-4300},
	pages = {1421},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816; Mosavi, Amirhosein/0000-0003-4842-0613}
}

@inproceedings{MTMT:31387078,
	title = {Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System},
	url = {https://m2.mtmt.hu/api/publication/31387078},
	author = {Mojrian, Sanaz and Pintér, Gergő and Joloudari, Javad Hassannataj and Felde, Imre and Szabo-Gali, Akos and Nádai, László and Mosavi, Amirhosein},
	booktitle = {The 2020 RIVF International Conference on Computing & Communication Technologies (RIVF)},
	doi = {10.1109/RIVF48685.2020.9140744},
	unique-id = {31387078},
	year = {2020},
	pages = {1-7},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816; Mosavi, Amirhosein/0000-0003-4842-0613}
}

@article{MTMT:31333491,
	title = {COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach},
	url = {https://m2.mtmt.hu/api/publication/31333491},
	author = {Pintér, Gergő and Felde, Imre and Mosavi, Amirhosein and Ghamisi, Pedram and Gloaguen, Richard},
	doi = {10.3390/math8060890},
	journal-iso = {MATHEMATICS-BASEL},
	journal = {MATHEMATICS},
	volume = {8},
	unique-id = {31333491},
	year = {2020},
	eissn = {2227-7390},
	pages = {890-910},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816; Mosavi, Amirhosein/0000-0003-4842-0613}
}