Journal Articles
2017(2 publications)
[publication]Rodrigues, F. and Borysov, S. and Ribeiro, B. and Pereira, F.C. , "A Bayesian additive model for understanding public transport usage in special events", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017

[publication]Rodrigues, F. and , M.L. and Ribeiro, B. and Pereira, F.C. , "Learning Supervised Topic Models for Classification and Regression from Crowds", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017

2015(2 publications)
[publication]Jiang, S. and Alves, A. and Rodrigues, F. and Ferreira, J. and Pereira, F.C. , "Mining point-of-interest data from social networks for urban land use classification and disaggregation", Computers, Environment and Urban Systems, 2015
[citation][year=2020]Hu, S., He, Z., Wu, L., Yin, L., Xu, Y., & Cui, H. (2020). A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data. Computers, Environment and Urban Systems, 80, 101442
[citation][year=2020]Zhou, W., Ming, D., Lv, X., Zhou, K., Bao, H., & Hong, Z. (2020). SO–CNN based urban functional zone fine division with VHR remote sensing image. Remote Sensing of Environment, 236, 111458
[citation][year=2019]Yue, W., Chen, Y., Zhang, Q., & Liu, Y. (2019). Spatial Explicit Assessment of Urban Vitality Using Multi-Source Data: A Case of Shanghai, China. Sustainability, 11(3), 638.
[citation][year=2019]Sparks, K., Thakur, G., Pasarkar, A., & Urban, M. (2019). A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation. International Journal of Geographical Information Science, 1-18.
[citation][year=2019]De Kok, R., Mauri, A., & Bozzon, A. (2019). Automatic processing of user-generated content for the description of energy-consuming activities at individual and group level. Energies, 12(1), 15.
[citation][year=2019]Cao, K., Guo, H., & Zhang, Y. (2019). Comparison of approaches for urban functional zones classification based on multi-source geospatial data: A case study in Yuzhong district, Chongqing, China. Sustainability, 11(3), 660.
[citation][year=2019]Niu, H., & Silva, E. (2019, September). Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods. ASCE.
[citation][year=2019]Ge, P., He, J., Zhang, S., Zhang, L., & She, J. (2019). An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification. ISPRS International Journal of Geo-Information, 8(2), 90.
[citation][year=2019]Yi, D., Yang, J., Liu, J., Liu, Y., & Zhang, J. (2019). Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing. ISPRS International Journal of Geo-Information, 8(12), 555.
[citation][year=2019]Hu, Y., & Han, Y. (2019). Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone. Sustainability, 11(5), 1385.
[citation][year=2019]Lee, D., & Lee, S. (2019, September). Inferring the character of urban commercial areas from age-biased online search results: how place recommendation data can reveal dynamic seoul neighborhoods. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (pp. 991-995).
[citation][year=2019]Wu, J., Li, J., & Ma, Y. (2019). Exploring the Relationship between Potential and Actual of Urban Waterfront Spaces in Wuhan Based on Social Networks. Sustainability, 11(12), 3298.
[citation][year=2019]Liu, X., Huang, Q., & Gao, S. (2019). Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN. International Journal of Geographical Information Science, 33(6), 1196-1223.
[citation][year=2019]Han, Z., Long, Y., Wang, X., & Hou, J. (2019). Urban redevelopment at the block level: Methodology and its application to all Chinese cities. Environment and Planning B: Urban Analytics and City Science, 2399808319843928.
[citation][year=2019]Min, M., Lin, C., Duan, X., Jin, Z., & Zhang, L. (2019). Spatial distribution and driving force analysis of urban heat island effect based on raster data: a case study of the Nanjing metropolitan area, China. Sustainable Cities and Society, 101637.
[citation][year=2019]Yang, J., Zhu, J., Sun, Y., & Zhao, J. (2019). Delimitating urban commercial central districts by combining kernel density estimation and road intersections: a case study in nanjing city, china. ISPRS International Journal of Geo-Information, 8(2), 93.
[citation][year=2019]Zhao, Y., Li, Q., Zhang, Y., & Du, X. (2019). Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs. Remote Sensing, 11(21), 2502.
[citation][year=2019]Sideris, N., Bardis, G., Voulodimos, A., Miaoulis, G., & Ghazanfarpour, D. (2019). Using Random Forests on Real-World City Data for Urban Planning in a Visual Semantic Decision Support System. Sensors, 19(10), 2266.
[citation][year=2019]Hong, Y., & Yao, Y. (2019). Hierarchical community detection and functional area identification with OSM roads and complex graph theory. International Journal of Geographical Information Science, 33(8), 1569-1587.
[citation][year=2019]Gao, J., Zhang, Y. C., & Zhou, T. (2019). Computational socioeconomics. Physics Reports.
[citation][year=2019]Zhu, Y., Deng, X., & Newsam, S. (2019). Fine-grained land use classification at the city scale using ground-level images. IEEE Transactions on Multimedia.
[citation][year=2019]Zhai, W., Bai, X., Shi, Y., Han, Y., Peng, Z. R., & Gu, C. (2019). Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs. Computers, Environment and Urban Systems, 74, 1-12.
[citation][year=2019]Pan, Y., Chen, S., Li, T., Niu, S., & Tang, K. (2019). Exploring spatial variation of the bus stop influence zone with multi-source data: A case study in Zhenjiang, China. Journal of Transport Geography, 76, 166-177.
[citation][year=2019]Ye, T., Zhao, N., Yang, X., Ouyang, Z., Liu, X., Chen, Q., ... & Jia, P. (2019). Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model. Science of the total environment, 658, 936-946.
[citation][year=2019]Yang, X., Ye, T., Zhao, N., Chen, Q., Yue, W., Qi, J., ... & Jia, P. (2019). Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data. Remote sensing, 11(5), 574.
[citation][year=2019]Li, Q., Zhou, S., & Wen, P. (2019). The relationship between centrality and land use patterns: Empirical evidence from five Chinese metropolises. Computers, Environment and Urban Systems, 78, 101356.
[citation][year=2019]Martí, P., Serrano-Estrada, L., & Nolasco-Cirugeda, A. (2019). Social media data: Challenges, opportunities and limitations in urban studies. Computers, Environment and Urban Systems, 74, 161-174.
[citation][year=2019]Chen, M., Arribas-Bel, D., & Singleton, A. (2019). Understanding the dynamics of urban areas of interest through volunteered geographic information. Journal of Geographical Systems, 21(1), 89-109.
[citation][year=2019]Lee, D., & Lee, S. (2019). Inferring the Character of Urban Commercial Areas from Age-biased Online Search Results.
[citation][year=2019]Martí Ciriquián, P., Serrano-Estrada, L., & Nolasco-Cirugeda, A. (2019). Social Media data: Challenges, opportunities and limitations in urban studies.
[citation][year=2019]Yang, C. (2019). A new perspective on urban form with the integration of Space Syntax and MCDA–An exploratory analysis of the city of Xi’an, China (Master's thesis, University of Waterloo).
[citation][year=2019]Lin, Y., & Geertman, S. (2019, July). Can Social Media Play a Role in Urban Planning? A Literature Review. In International Conference on Computers in Urban Planning and Urban Management (pp. 69-84). Springer, Cham.
[citation][year=2019]Wu Wanyu, & Niu Xinyi. (2019). Research on the Impact of the Diversity of the Built Environment on the Vitality of Streets: A Case Study of Nanjing West Road in Shanghai. Southern Architecture , (2), 14.
[citation][year=2019]Soundararaj, B. (2019). Estimating Footfall From Passive Wi-Fi Signals: Case Study with Smart Street Sensor Project (Doctoral dissertation, UCL (University College London)).
[citation][year=2019]Bahadorizadeh, H., & Malek, M. R. (2019). User Generate Spatial Content in Land Administration and Cadastre: Types and Usage. Geospatial Engineering Journal, 10(2), 51-62.
[citation][year=2019]Vedernikov, O. (2019). Optimal route planning for hitchhiking (Doctoral dissertation). University of Melbourne, Australia. http://hdl.handle.net/11343/227596
[citation][year=2019]Sideris, N. (2019). Spatial decision support in urban environments using machine learning, 3D geo-visualization and semantic integration of multi-source data (Doctoral dissertation). Université de Limoges, France. https://tel.archives-ouvertes.fr/tel-02449667/file/2019LIMO0083.pdf
[citation][year=2019]Chen, E., Ye, Z., Wang, C., & Zhang, W. (2019). Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data. Cities, 95, 102359.
[citation][year=2019]Firzatullah, R. M. (2019). A Development of Spatial Skyline Query Based on Surrounding Environment for Data Streaming Using Apache-Spark (Master thesis dissertation). Institut Pertanian Bogor University, Indonesia. http://repository.ipb.ac.id/handle/123456789/98738
[citation][year=2018]Akerkar, R., & Hong, M. (2018, May). Unlocking Value from Ubiquitous Data. In International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications (pp. 3-17). Springer, Cham.
[citation][year=2018]Rosina, K., Batista e Silva, F., Vizcaino, P., Marín Herrera, M., Freire, S., & Schiavina, M. (2018). Increasing the detail of European land use/cover data by combining heterogeneous data sets. International Journal of Digital Earth, 1-25.
[citation][year=2018]Fan, D., Qin, K., & Kang, C. (2018, June). Understanding Urban Functionality from POI Space. In 2018 26th International Conference on Geoinformatics (pp. 1-6). IEEE.
[citation][year=2018]Mou, F., He, Y., Peng, J., Ma, Y., Zheng, Z. Z., Wang, S. L., & Li, J. (2018, December). A New Urban Functional Regions Minig Method with MPETM. In 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 73-76). IEEE.
[citation][year=2018]Liu, X. (2018). Detection and Exploration of Individual Semantic Trajectories Using Social Media Data (Doctoral dissertation).
[citation][year=2018]Yu, Y., Li, J., Zhu, C., & Plaza, A. (2018). Urban Impervious Surface Estimation from Remote Sensing and Social Data. Photogrammetric Engineering & Remote Sensing, 84(12), 771-780.
[citation][year=2018]Wang, Y., de Almeida Correia, G. H., van Arem, B., & Timmermans, H. H. (2018). Understanding travellers’ preferences for different types of trip destination based on mobile internet usage data. Transportation Research Part C: Emerging Technologies, 90, 247-259.
[citation][year=2018]Lin, J., & Cromley, R. G. (2018). Inferring the home locations of Twitter users based on the spatiotemporal clustering of Twitter data. Transactions in GIS, 22(1), 82-97.
[citation][year=2018]Chen, W., Huang, H., Dong, J., Zhang, Y., Tian, Y., & Yang, Z. (2018). Social functional mapping of urban green space using remote sensing and social sensing data. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 436-452.
[citation][year=2018]Aubrecht, C., Ungar, J., Aubrecht, D. O., Freire, S., & Steinnocher, K. (2018). Mapping Land Use Dynamics Using the Collective Power of the Crowd. In Earth Observation Open Science and Innovation (pp. 247-253). Springer, Cham.
[citation][year=2018]Novack, T., Peters, R., & Zipf, A. (2018). Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets. ISPRS International Journal of Geo-Information, 7(3), 117.
[citation][year=2018]Zhou, H., & Hirasawa, K. (2018). Spatiotemporal traffic network analysis: technology and applications. Knowledge and Information Systems, 1-37.
[citation][year=2018]Huang, L., Wu, Y., Zheng, Q., Zheng, Q., Zheng, X., Gan, M., ... & Zhang, J. (2018). Quantifying the Spatiotemporal Dynamics of Industrial Land Uses through Mining Free Access Social Datasets in the Mega Hangzhou Bay Region, China. Sustainability, 10(10), 3463.
[citation][year=2018]Wang, S., Xu, G., & Guo, Q. (2018). Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China. ISPRS International Journal of Geo-Information, 7(11), 425.
[citation][year=2018]Yang, S., Shen, J., Kone?ný, M., Wang, Y., & Štampach, R.( 2018). STUDY ON THE SPATIAL HETEROGENEITY OF THE POI QUALITY IN OPENSTREETMAP.
[citation][year=2018]Liu, X., Niu, N., Liu, X., Jin, H., Ou, J., Jiao, L., & Liu, Y. (2018). Characterizing mixed-use buildings based on multi-source big data. International Journal of Geographical Information Science, 32(4), 738-756.
[citation][year=2018]Song, J., Lin, T., Li, X., & Prishchepov, A. (2018). Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China. Remote Sensing, 10(11), 1737.
[citation][year=2018]Martí, P., Serrano-Estrada, L., & Nolasco-Cirugeda, A. (2018). Social Media data: Challenges, opportunities and limitations in urban studies. Computers, Environment and Urban Systems.
[citation][year=2018]Lei, P., Marfia, G., Pau, G., & Tse, R. (2018). Can we monitor the natural environment analyzing online social network posts? A literature review. Online Social Networks and Media, 5, 51-60.
[citation][year=2018]Zhu, Y., Deng, X., & Newsam, S. (2018). Fine-grained land use classification at the city scale using ground-level images. arXiv preprint arXiv:1802.02668.
[citation][year=2018]Chen, Y., Ge, Y., An, R., & Chen, Y. (2018). Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest. Remote Sensing, 10(2), 242.
[citation][year=2018]Jana, Arnab & Verma, Deepank & Ramamritham, Krithivasan. (2018). HOW DIVERSE ARE THE NEIGHBOURHOODS? A DIVERSITY INDEX TO ASSESS LAND USE MIX THROUGH OPEN SOURCE AND ONLINE DATASETS.
[citation][year=2017]Lin J, Cromley RG. Inferring the home locations of Twitter users based on the spatiotemporal clustering of Twitter data. Transactions in GIS. 2017;00:1–16. https://doi.org/10.1111/tgis.12297
[citation][year=2017]Khoshamooz, G. and Taleai, M. (2017), Multi-Domain User-Generated Content Based Model to Enrich Road Network Data for Multi-Criteria Route Planning. Geogr Anal, 49: 239–267. doi:10.1111/gean.12124
[citation][year=2017]Zhang, Y.; Li, Q.; Huang, H.; Wu, W.; Du, X.; Wang, H. The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China. Remote Sens. 2017, 9, 865.
[citation][year=2017]Jeyasree, J., & Bhuvaneshwari, K. (2017). The Segmentation of Age Related Macular Degeneration in Color Fundus Image. Asian Journal of Applied Science and Technology (AJAST), 1(3), 27-30.
[citation][year=2017]Bao, J., Xu, C., Liu, P., & Wang, W. (2017). Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests. Networks and Spatial Economics, 1-23.
[citation][year=2017]Jia, T., & Ji, Z. (2017). Understanding the Functionality of Human Activity Hotspots from Their Scaling Pattern Using Trajectory Data. ISPRS International Journal of Geo-Information, 6(11), 341.
[citation][year=2017]Wang, H., Dong, Y., & Zhang, K. (2017, May). A spatial-temporal model to improve PM2. 5 inference. In Computer and Information Science (ICIS), 2017 IEEE/ACIS 16th International Conference on (pp. 173-177). IEEE.
[citation][year=2017]Xing, H., Meng, Y., Hou, D., Song, J., & Xu, H. (2017). Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model. Remote Sensing, 9(6), 602.
[citation][year=2017]Xing, H., Meng, Y., Hou, D., Cao, F., & Xu, H. (2017). Exploring point-of-interest data from social media for artificial surface validation with decision trees. International Journal of Remote Sensing, 38(23), 6945-6969.
[citation][year=2017]Mesbah, S., Bozzon, A., Lofi, C., & Houben, G. J. (2017, February). Describing data processing pipelines in scientific publications for big data injection. In Proceedings of the 1st Workshop on Scholarly Web Mining (pp. 1-8). ACM.
[citation][year=2017]Emmanouil Chaniotakis, Constantinos Antoniou, Georgia Aifadopoulou, and Loukas Dimitriou. Inferring Activities from Social Media Data. Transportation Research Record: Journal of the Transportation Research Board 2017 2666:, 29-37
[citation][year=2017]Xiao, Y.; Chen, X.; Li, Q.; Yu, X.; Chen, J.; Guo, J. Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data. ISPRS Int. J. Geo-Inf. 2017, 6, 358.
[citation][year=2017]Chaniotakis, E., Antoniou, C., Aifadopoulou, G., & Dimitriou, L. (2017). Inferring activities from social media data. Transportation research record, 2666(1), 29-37.
[citation][year=2017]e Silva, F. B., Rosina, K., Schiavina, M., Marin, M., Freire, S., Craglia, M., & Lavalle, C. (2017). Spatiotemporal mapping of population in Europe: The “ENACT” project in a nutshell. In 57th european regional science association (ERSA) congress.
[citation][year=2017]Lei, P., Marfia, G., Pau, G., & Tse, R. (2017). Online Social Networks and Media.
[citation][year=2017]Deng, X., & Newsam, S. (2017, November). Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use. In Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics (p. 4). ACM.
[citation][year=2017]Wood, S., Muthyala, R., Jin, Y., Qin, Y., Rukadikar, N., Rai, A., & Gao, H. (2017, December). Automated industry classification with deep learning. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 122-129). IEEE.
[citation][year=2017]Touya, Guillaume, et al. "Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations." ISPRS International Journal of Geo-Information 6.3 (2017): 80.
[citation][year=2017]Ermagun, Alireza, et al. "Real-time trip purpose prediction using online location-based search and discovery services." Transportation Research Part C: Emerging Technologies 77 (2017): 96-112.
[citation][year=2017]Liu, Xiaoping, et al. "Classifying urban land use by integrating remote sensing and social media data." International Journal of Geographical Information Science (2017): 1-22.
[citation][year=2017]Yue, Yang, et al. "Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy." International Journal of Geographical Information Science 31.4 (2017): 658-675.
[citation][year=2017]Gao, Song, Krzysztof Janowicz, and Helen Couclelis. "Extracting urban functional regions from points of interest and human activities on location?based social networks." Transactions in GIS 21.3 (2017): 446-467.
[citation][year=2017]Yao, Yao, et al. "Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model." International Journal of Geographical Information Science 31.4 (2017): 825-848.
[citation][year=2017]Ricciato, Fabio, et al. "Beyond the “single-operator, CDR-only” paradigm: An interoperable framework for mobile phone network data analyses and population density estimation." Pervasive and Mobile Computing 35 (2017): 65-82.
[citation][year=2017]Yao, Yao, et al. "Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata." International Journal of Geographical Information Science (2017): 1-28.
[citation][year=2017]Niu, Ning, et al. "Integrating multi-source big data to infer building functions." International Journal of Geographical Information Science (2017): 1-20.
[citation][year=2017]Chen, Y., Liu, X., Li, X., Liu, X., Yao, Y., Hu, G., Xu, X., Pei, F. Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method. Landscape and Urban Planning
volume 160, issue , year 2017, pp. 48 - 60
[citation][year=2016]Muhammad Adnan, Francisco C. Pereira, Carlos Lima Azevedo, Kakali Basak, Milan Lovric, Sebastián Raveau, Yi Zhu, Joseph Ferreira, Christopher Zegras, Moshe Ben-Akiva, SimMobility: A Multi-scale Integrated Agent-Based Simulation Platform (2016)
[citation][year=2016]Yao, Yao, et al. "Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model." International Journal of Geographical Information Science (2016): 1-24.
[citation][year=2016]Vedernikov, Oleksii, Lars Kulik, and Kotagiri Ramamohanarao. "The Hitchhiker’s guide to the pick-up locations." Open Geospatial Data, Software and Standards 1.1 (2016): 12.
[citation][year=2016]Gong, X. "Exploring Human Activity Patterns Across Cities through Social Media Data." MSc Thesis. TU Delft. Netherlands (2016).
[citation][year=2016]Umwelt, Ingenieurfakultät Bau Geo. "Visual Analysis of Large Floating Car Data-A Bridge-Maker between Thematic Mapping and Scientific Visualization." Master Thesis. 2016 TECHNISCHE UNIVERSITÄT MÜNCHEN
[citation][year=2016]Psyllidis, Achilleas. "Revisiting Urban Dynamics through Social Urban Data." A+ BE| Architecture and the Built Environment 6.18 (2016): 1-334.
[citation][year=2016]Ricciato, Fabio, et al. "Beyond the “single-operator, CDR-only” paradigm: An interoperable framework for mobile phone network data analyses and population density estimation." Pervasive and Mobile Computing (2016).
[citation][year=2016]Milad Mirbabaie, Stefan Stieglitz, and Stephan Volkeri. 2016. Volunteered Geographic Information and Its Implications for Disaster Management. In Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS) (HICSS '16). IEEE Computer Society, Washington, DC, USA, 207-216. DOI=http://dx.doi.org/10.1109/HICSS.2016.33
[citation][year=2016]Guy Lansley, Paul A. Longley, The geography of Twitter topics in London, Computers, Environment and Urban Systems, Volume 58, July 2016, Pages 85-96, ISSN 0198-9715, http://dx.doi.org/10.1016/j.compenvurbsys.2016.04.002.
[citation][year=2016]Jonietz, D.; Zipf, A. Defining Fitness-for-Use for Crowdsourced Points of Interest (POI). ISPRS Int. J. Geo-Inf. 2016, 5, 149. doi:10.3390/ijgi5090149
[citation][year=2016]Yimin Chen, Xiaoping Liu, Xia Li, Xingjian Liu, Yao Yao, Guohua Hu, Xiaocong Xu, Fengsong Pei, Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method, Landscape and Urban Planning, Volume 160, April 2017, Pages 48-60, ISSN 0169-2046, http://dx.doi.org/10.1016/j.landurbplan.2016.12.001.
[citation][year=2015]E. Chaniotakis, C. Antoniou and E. Mitsakis.Data for Leisure Travel Demand from Social Networking Services. hEART 2015. 4th symposium of European Association for Research in Transportation. September 2015. http://www.heart2015.transport.dtu.dk/-/media/Sites/hEART2015/abstracts hEART/hEART_2015_submission_60.ashx?la=da

[publication]Pereira, F.C. and Rodrigues, F. and Polisciuc, E. and Ben-Akiva, M. , "Why so many people? Explaining Nonhabitual Transport Overcrowding With Internet Data ", Transactions on Intelligent Transportation Systems, 2015

2013(5 publications)
[publication]Rodrigues, F. and Alves, A. and Polisciuc, E. and Jiang, S. and Ferreira, J. and Pereira, F.C. , "Estimating disaggregated employment size from Points-of-Interest and census data: From mining the web to model implementation and visualization", International Journal on Advanced Intelligent Systems,  vol. 6,  pp. 41-52, 2013
[citation][year=2018]Folch, D. C., Spielman, S. E., & Manduca, R. (2018). Fast food data: Where user?generated content works and where it does not. Geographical Analysis, 50(2), 125-140.
[citation][year=2018]Novack, T., Peters, R., & Zipf, A. (2018). Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets. ISPRS International Journal of Geo-Information, 7(3), 117.
[citation][year=2018]Gervasoni, L., Fenet, S., Perrier, R., & Sturm, P. (2018, October). Convolutional neural networks for disaggregated population mapping using open data. In IEEE International Conference on Data Science and Advanced Analytics (DSAA).
[citation][year=2018]Gervasoni, L., Fenet, S., & Sturm, P. (2018, January). Une méthode pour l’estimation désagrégée de données de population à l’aide de données ouvertes. In 18ème Conférence Internationale sur l'Extraction et la Gestion des Connaissances.
[citation][year=2017]Touya, G., Antoniou, V., Olteanu-Raimond, A. M., & Van Damme, M. D. (2017). Assessing crowdsourced POI quality: Combining methods based on reference data, history, and spatial relations. ISPRS International Journal of Geo-Information, 6(3), 80.
[citation][year=2016]Jonietz, D.; Zipf, A. Defining Fitness-for-Use for Crowdsourced Points of Interest (POI). ISPRS Int. J. Geo-Inf. 2016, 5, 149. doi:10.3390/ijgi5090149
[citation][year=2015]DRAFT, S. 2015, Why so many people? Explaining non-habitual transport overcrowding with internet data.Montini, L., Rieser-Schüssler, N., Horni, A., & Axhausen, K. (2014). Trip purpose identification from GPS tracks. Transportation Research Record: Journal of the Transportation Research Board, (2405), 16-23.
[citation][year=2014]Montini, L., Rieser-Schüssler, N., Horni, A., & Axhausen, K. (2014). Trip purpose identification from GPS tracks. Transportation Research Record: Journal of the Transportation Research Board, (2405), 16-23.
[citation][year=2014]Montini, L., & Rieser, N. (2014). Implementation and pretest of the trip purpose detection.
[citation][year=2014]Fine-resolution population mapping using OpenStreetMap points-of-interest
Mohamed Bakillah , Steve Liang , Amin Mobasheri , Jamal Jokar Arsanjani , Alexander Zipf 
International Journal of Geographical Information Science 
Vol. 28, Iss. 9, 2014
[citation][year=2014]Limits of Predictability in Commuting Flows in the Absence of Data for Calibration (Yingxiang Yang, C. Herrera-Yagüe, N. Eagle, Marta C González),Nature Collections, Scientific Reports 4, Article number: 5662 doi:10.1038/srep05662 (2014) http://www.nature.com/srep/2014/140711/srep05662/full/srep05662.html
[citation][year=2013]S Jiang, GA Fiore, Y Yang, J Ferreira Jrâ?¦, A review of urban computing for mobile phone traces: current methods, challenges and opportunities, Proceedings of the 2nd  …, 2013

[publication]Rodrigues, F. and Pereira, F.C. and Ribeiro, B. , "Learning from Multiple Annotators: Distinguishing Good from Random Labelers", Pattern Recognition Letters, Elsevier, 2013
[citation][year=2016]C Long, G Hua, A Kapoor, A joint gaussian process model for active visual recognition with expertise estimation in crowdsourcing, International Journal of Computer Vision, 2016
[citation][year=2015]ED Simpson, M Venanzi, S Reece, P Kohliâ?¦, Language Understanding in the Wild: Combining Crowdsourcing and Machine Learning, Proceedings of the 24th …, 2015
[citation][year=2015]YE Kara, G Genc, O Aran, L Akarun, Modeling annotator behaviors for crowd labeling, Neurocomputing, 2015
[citation][year=2015]M Venanzi, O Parson, A Rogers, N Jennings, The ActiveCrowdToolkit: An Open-Source Tool for Benchmarking Active Learning Algorithms for Crowdsourcing Research, Third AAAI Conference on  …, 2015
[citation][year=2015]A Fuddoly, J Jaafar, N Zamin, News Classification with Human Annotators: A Case Study, Jurnal Teknologi, 2015
[citation][year=2014]A Tarasov, SJ Delany, B Mac Namee, Dynamic estimation of worker reliability in crowdsourcing for regression tasks: Making it work, Expert Systems with Applications, 2014
[citation][year=2014]A Tarasov, Dynamic Estimation of Rater Reliability using Multi-Armed Bandits, Publication/NA, 2014
[citation][year=2013]L Kinley, Towards the use of Citizen Sensor Information as an Ancillary Tool for the Thematic Classification of Ecological Phenomena, Proceedings of the 2nd AGILE (Association of  …, 2013

[publication]Rodrigues, F. and Pereira, F.C. and Ribeiro, B. , "Sequence labeling with multiple annotators", Machine Learning, Springer, 2013
[citation][year=2016]C Long, G Hua, A Kapoor, A joint gaussian process model for active visual recognition with expertise estimation in crowdsourcing, International Journal of Computer Vision, 2016
[citation][year=2015]RG Brace, Physician Participation in Crowdsourcing: Effect of Intrinsic and Extrinsic Motivation, Publication/NA, 2015
[citation][year=2014]D Hovy, B Plank, A Søgaard, Experiments with crowdsourced re-annotation of a POS tagging data set., ACL (2), 2014
[citation][year=2014]H Fromreide, A Søgaard, NER in Tweets Using Bagging and a Small Crowdsourced Dataset, Advances in Natural Language Processing, 2014

[publication]Pereira, F.C. and Rodrigues, F. and Ben-Akiva, M. , "Text analysis in incident duration prediction", Transportation Research Part C, Elsevier, 2013
[citation][year=2016]AJP Tixier, MR Hallowell, B Rajagopalanâ?¦, Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports, Automation in  …, 2016
[citation][year=2016]L Jin, S Amin, Analysis of a Stochastic Switched Model of Freeway Traffic Incidents, arXiv preprint arXiv:1601.00204, 2016
[citation][year=2015]H Park, A Haghani, Real-time prediction of secondary incident occurrences using vehicle probe data, Transportation Research Part C: Emerging  …, 2015
[citation][year=2015]L Tanguy, N Tulechki, A Urieli, E Hermannâ?¦, Natural language processing for aviation safety reports: from classification to interactive analysis, Computers in Industry, 2015
[citation][year=2015]DPK Seedah, F Leite, Information Extraction for Freight-Related Natural Language Queries, Computing in Civil Engineering 2015, 2015
[citation][year=2015]Y Lu, FC Pereira, R Seshadriâ?¦, DynaMIT2. 0: Architecture Design and Preliminary Results on Real-Time Data Fusion for Traffic Prediction and Crisis Management, …  (ITSC), 2015 IEEE  …, 2015
[citation][year=2015]J Weng, Y Zheng, X Qu, X Yan, Development of a maximum likelihood regression tree-based model for predicting subway incident delay, Transportation Research Part C: Emerging  …, 2015
[citation][year=2015]CP Khatri, REAL-TIME ROAD TRAFFIC INFORMATION DETECTION THROUGH SOCIAL MEDIA, Publication/NA, 2015
[citation][year=2015]A Kurkcu, EF Morgul, K Ozbay, Extended Implementation Method for Virtual Sensors: Web-Based Real-Time Transportation Data Collection and Analysis for Incident Management, …  Research Record: Journal  …, 2015
[citation][year=2015]A Kurkcu, K Ozbay, Extended Implementation Method for Virtual Sensors, Publication/NA, 2015
[citation][year=2014]R Li, Traffic incident duration analysis and prediction models based on the survival analysis approach, Intelligent Transport Systems, IET, 2014
[citation][year=2014]DPK Seedah, Retrieving information from heterogeneous freight data sources to answer natural language queries, Publication/NA, 2014
[citation][year=2014]Y He, S Blandin, L Wynterâ?¦, Analysis and real-time prediction of local incident impact on transportation networks, Data Mining Workshop ( …, 2014

[publication]Pereira, F.C. and Rodrigues, F. and Ben-Akiva, M. , "Using data from the web to predict public transport arrivals under special events scenarios", Journal of Intelligent Transportation Systems: Technology, Planning, and Operations (JITS), 2013
[citation][year=2015]WY Szeto, J YEUNG, RCP WONGâ?¦, Trip Attraction, Trip Distribution, and Modal Split for Columbarium Trips, Journal of the Eastern Asia  …, 2015
[citation][year=2015]K Gkiotsalitis, A Stathopoulos, Optimizing Leisure Travel: Is BigData Ready to Improve the Joint Leisure Activities Efficiency?, Engineering and Applied Sciences  …, 2015
[citation][year=2015]E Chaniotakis, C Antoniou, Use of Geotagged Social Media in Urban Settings: Empirical Evidence on Its Potential from Twitter, Intelligent Transportation Systems  …, 2015
[citation][year=2015]WY Szeto, RCP Wong, J Yeungâ?¦, Mixed logit approach to modeling arrival time choice behavior of cemetery and columbarium visitors during grave-sweeping festivals, …  A: Transport Science, 2015
[citation][year=2014]S Gowrishankar, ER Stern, DB Work, Including the social component in smart transportation systems, National Workshop on  …, 2014

Conference Articles
2016(1 publication)
[publication]Ferrugento, A. and Gonçalo Oliveira, H,  and Alves, A. and Rodrigues, F. , "Can Topic Modelling benefit from Word Sense Information?", in 10th International Conference on Language Resources and Evaluation, 2016
[citation][year=2018]Pham, P., Do, P., and Ta, C. D. C. (2018). GOW-LDA: Applying term co-occurrence graph representation in lda topic models improvement. In Proceedings of International Conference on Computational Science and Technology, ICCST 2017, pages 420–431. Springer.

2015(2 publications)
[publication]Ferrugento, A. and Alves, A. and Gonçalo Oliveira, H,  and Rodrigues, F. , "Towards the Improvement of a Topic Model with Semantic Knowledge", in 14th Portuguese Conference on Artificial Intelligence (EPIA 2015), 2015
[citation][year=2017]Mireles, V., & Revenko, A. (2017). Evolution of Semantically Identified Topics. In HybridSemStats@ ISWC.

[publication]Rodrigues, F. and , M.L. and Ribeiro, B. and Pereira, F.C. , "Learning Supervised Topic Models from Crowds", in The Third AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2015

2014(3 publications)
[publication]Rodrigues, F. and Pereira, F.C. and Ribeiro, B. , "Gaussian Process Classification and Active Learning with Multiple Annotators", in International Conference on Machine Learning (ICML 2014), 2014
[citation][year=2016]C Long, G Hua, A Kapoor, A joint gaussian process model for active visual recognition with expertise estimation in crowdsourcing, International Journal of Computer Vision, 2016
[citation][year=2016]D Padmanabhan, D Garg, S Shevadeâ?¦, A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds, arXiv preprint arXiv: …, 2016
[citation][year=2016]E Weigl, W Heidl, E Lughofer, T Radauerâ?¦, On improving performance of surface inspection systems by online active learning and flexible classifier updates, Machine Vision and  …, 2016
[citation][year=2015]C Long, G Hua, Multi-class Multi-annotator Active Learning with Robust Gaussian Process for Visual Recognition, Proceedings of the IEEE International Conference …, 2015
[citation][year=2015]N Rohani, P Ruiz, E Besler, R Molinaâ?¦, Variational Gaussian process for sensor fusion, … 2015 23rd European, 2015
[citation][year=2015]M Venanzi, J Guiver, P Kohli, N Jennings, Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems, arXiv preprint arXiv: …, 2015

[publication]Alves, A. and Ferrugento, A. and , M.L. and Rodrigues, F. , "ASAP:  Automatic Semantic Alignment for Phrases", in SemEval Workshop, COLING 2014, Ireland, 2014
[citation][year=2017]Silva, A. D. B. (2017). O uso de recursos linguísticos para mensurar a semelhança semântica entre frases curtas através de uma abordagem híbrida.
[citation][year=2017]Kadupitiya, J. C. S., Ranathunga, S., & Dias, G. (2017, July). Assessment and Error Identification of Answers to Mathematical Word Problems. In 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT) (pp. 55-59). IEEE.
[citation][year=2016]Kadupitiya, J. C. S., Ranathunga, S., & Dias, G. (2016). Short Sentence Similarity Calculation using Corpus-Based and Knowledge-Based Similarity Measures. WSSANLP 2016, 44.
[citation][year=2016]Luisa Bentivogli, Raffaella Bernardi , Marco Marelli, Stefano Menini, Marco Baroni, Roberto Zamparelli. SICK through the SemEval glasses. Lesson learned from the evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. Journal of Language Resources and Evaluation. 10.1007/s10579-015-9332-5
[citation][year=2016]Lu, Wei, et al. "Joint semantic similarity assessment with raw corpus and structured ontology for semantic-oriented service discovery." Personal and Ubiquitous Computing 20.3 (2016): 311-323.
[citation][year=2016]Kadupitiya, J. C. S., Surangika Ranathunga, and Gihan Dias. "Sinhala Short Sentence Similarity Measures using Corpus-Based Simi-larity for Short Answer Grading." WSSANLP 2016 (2016): 44.
[citation][year=2015]Bentivogli, L., Bernardi, R., Marelli, M., Menini, S., Baroni, M., & Zamparelli, R.  2015. SICK Through the SemEval Glasses.
[citation][year=2015]Yuanyuan Cai, Wei Lu, Xiaoping Che, Kailun Shi. Differential Evolutionary Algorithm Based on Multiple Vector Metrics for
Semantic Similarity Assessment in Continuous Vector Space. 21st International Conference on Distributed Multimedia Systems (DMS'2015).
http://ksiresearchorg.ipage.com/seke/dms15paper/dms15paper_1.pdf
[citation][year=2015]Wei Lu, Yuanyuan Cai, Xiaoping Che, and Kailun Shi. 2015. Semantic Similarity Assessment Using Differential Evolution Algorithm in Continuous Vector Space. J. Vis. Lang. Comput. 31, PB (December 2015), 246-251. DOI=http://dx.doi.org/10.1016/j.jvlc.2015.10.015
[citation][year=2015]Cai, Yuanyuan, et al. "Knowledge-Enhanced Multi-semantic Fusion for Concept Similarity Measurement in Continuous Vector Space." Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015 IEEE 12th Intl Conf on. IEEE, 2015.
[citation][year=2014]Marelli, Marco, et al. "Semeval-2014 task 1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment." SemEval-2014 (2014).

[publication]Francisco Antunes and O’Sullivan, A. and Rodrigues, F. and Pereira, F.C. , "Accounting for Heteroscedasticity in Big Data", in Workshop on Big Data and Urban Informatics, 2014

2012(1 publication)
[publication]Rodrigues, F. and Pereira, F.C. and Alves, A. and Jiang, S. and Ferreira, J. , "Automatic Classification of Points-of-Interest for Land-use Analysis", in GEOProcessing 2012 : The Fourth International Conference on Advanced Geographic Information Systems, Applications, and Services, 2012
[citation][year=2019]Yao, Y., Liu, P., Hong, Y., Liang, Z., Wang, R., Guan, Q., & Chen, J. (2019). Fine-scale intra- and inter-city commercial store site recommendations using knowledge transfer. Transactions in GIS, 23(5), 1029-1047.
[citation][year=2018]Wong, J., Husain, A. M., & Panjwani, S. D. (2018). U.S. Patent No. 9,945,676. Washington, DC: U.S. Patent and Trademark Office.
[citation][year=2018]Yu, Y., Li, J., Zhu, C., & Plaza, A. (2018). Urban Impervious Surface Estimation from Remote Sensing and Social Data. Photogrammetric Engineering & Remote Sensing, 84(12), 771-780.
[citation][year=2018]Fang, F., Yuan, X., Wang, L., Liu, Y., & Luo, Z. (2018). Urban Land-Use Classification From Photographs. IEEE Geoscience and Remote Sensing Letters, (99), 1-5.
[citation][year=2018]Zhang, X., Li, W., Zhang, F., Liu, R., & Du, Z. (2018). Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data. ISPRS International Journal of Geo-Information, 7(12), 459.
[citation][year=2017]Liu, Xiaoping, et al. "Classifying urban land use by integrating remote sensing and social media data." International Journal of Geographical Information Science (2017): 1-22.
[citation][year=2017]Yao, Yao, et al. "Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model." International Journal of Geographical Information Science 31.4 (2017): 825-848.
[citation][year=2017]Wood, S., Muthyala, R., Jin, Y., Qin, Y., Rukadikar, N., Rai, A., & Gao, H. (2017, December). Automated industry classification with deep learning. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 122-129). IEEE.
[citation][year=2016]Lin, Chow-Sing, and Shang-Hsuan Hsu. "Effective self-adjustment places of interest discovery in public places." International Journal of Ad Hoc and Ubiquitous Computing 22.4 (2016): 226-235.
[citation][year=2016]Yao, Yao, et al. "Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model." International Journal of Geographical Information Science (2016): 1-24.
[citation][year=2016]Chen, Chenru, et al. "Land use classification in construction areas based on volunteered geographic information." Agro-Geoinformatics (Agro-Geoinformatics), 2016 Fifth International Conference on. IEEE, 2016.
[citation][year=2016]Montini, Lara. Extraction of transportation information from combined position and accelerometer tracks. PhD Thesis Diss. Eidgenössische Technische Hochschule Zürich. 2016.
[citation][year=2014]Montini, L., Rieser-Schüssler, N., Horni, A., Axhausen, K., Trip Purpose Identification from GPS Tracks. Transportation Research Board, pp 16–23, 2014.
[citation][year=2014]Montini, L., & Rieser, N. Implementation and pretest of the trip purpose detection. 2014
[citation][year=2014]Bakillah, M., Liang, S., Mobasheri, A., Jokar Arsanjani, J., & Zipf, A.. Fine-resolution population mapping using OpenStreetMap points-of-interest. International Journal of Geographical Information Science, (ahead-of-print), 1-24. 2014.
[citation][year=2013]Küster, T., Lützenberger, M., Freund, D., & Albayrak, S. Distributed evolutionary optimisation for electricity price responsive manufacturing using multi-agent system technology. International Journal On Advances in Intelligent Systems, 6(1 and 2), 27-40, 2013.

2011(1 publication)
[publication]Alves, A. and Rodrigues, F. and Pereira, F.C. , "Tagging Space from Information Extraction and Popularity of Points of Interest", in International Joint Conference on Ambient Intelligence, 2011
[citation][year=2018]Cheng, T., & Shen, J. (2018). Grouping people in cities: From space-time to place-time based profiling. In Human Dynamics Research in Smart and Connected Communities (pp. 181-201). Springer, Cham.
[citation][year=2017]Ganhoto, R. F. (2017). Inferência das atividades na modelização de escolhas de destinos e seu impacto na mobilidade urbana (Thesis dissertation).
[citation][year=2016]Lee, J. E., Rho, G. I., Jang, H. M., & Yu, K. U. (2016). System Design and Implementation for Building a Place Information based on Crowdsourcing Utilizing the Graph Data Model. Journal of Cadastre & Land InformatiX, 46(1), 117-131.
[citation][year=2015]Xiao Han. "Mining user similarity in online social networks : analysis,modeling and applications". PhD Thesis. Institut National des Télécommunications, 2015. English
[citation][year=2015]Jitao Sang, Tao Mei, and Changsheng Xu. 2015. Activity Sensor: Check-In Usage Mining for Local Recommendation. ACM Trans. Intell. Syst. Technol. 6, 3, Article 41 (April 2015), 24 pages. DOI=10.1145/2700468 http://doi.acm.org/10.1145/2700468
[citation][year=2013]Sang, Jitao, Tao Mei, Changsheng Xu and Shipeng Li. "Contextual and Personalized Mobile Recommendation Systems." Tools for Mobile Multimedia Programming and Development. IGI Global, 2013. 82-97. Web. 7 Jan. 2015. doi:10.4018/978-1-4666-4054-2.ch005
[citation][year=2013]Chilooo Gachilio(2013). A Study on the Construction Method of POI Data Using SLI and Vector Map Fusion. Engineering Doctoral Thesis. Seoul National University Graduate School

2010(2 publications)
[publication]Jiang, S. and Rodrigues, F. and Alves, A. and Pereira, F.C. and Ferreira, J. , "Towards an activity-based approach for estimating travel destinations", in World Conference in Transport Research (open track), 2010
[citation][year=2014]R Mansour, N Refaei, V Murdock, Augmenting Business Entities with Salient Terms from Twitter., COLING, 2014

[publication]Alves, A. and Pereira, F.C. and Rodrigues, F. and Oliveirinha, J. , "Place in perspective: Extracting online  information about Points of Interest", in First International Joint Conference on Ambient Intelligence (acc. rate. 38.5%), 2010
[citation][year=2016]Martinkus, Phil, and M. S. C. S. Praveen Madiraju. "Personalizing Places of Interest Using Social Media Analysis." Poster in CATA 2016.
[citation][year=2015]Onur Ekmekci, Andres Sevtsuk. 50 ways to Singapore Rail Corridor. Project at MIT. http://cityform.mit.edu/projects/50
[citation][year=2014]Eunyoung Kim, Hwon Ihm, and Sung-Hyon Myaeng. 2014. Topic-based place semantics discovered from microblogging text messages. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (WWW Companion '14). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 561-562. DOI=10.1145/2567948.2576955
[citation][year=2014]Kim, E., Ihm, H., & Myaeng, S. H. (2014, April). Topic-based place semantics discovered from microblogging text messages. In Proceedings of the 23rd International Conference on World Wide Web (pp. 561-562). ACM.

Book Chapters
2016(1 publication)
[publication]Francisco Antunes and O’Sullivan, A. and Rodrigues, F. and Pereira, F.C. , "A Review of Heteroscedasticity Treatment with Gaussian Processes and Quantile Regression Meta-models", in Seeing Cities Through Big Data,  vol. 1,  pp. 141-160, 2016

PhD Theses
2016(2 publications)
[publication]Rodrigues, F. , "Probabilistic Models for Learning from Crowdsourced Data", 2016

[publication]Rodrigues, F. , "Probabilistic Models for Learning from Crowdsourced data", 2016

MSc Theses
2010(1 publication)
[publication]Rodrigues, F. , "POI Mining and Generation", 2010
[citation][year=2014]Mansour, R., Refaei, N., Murdock, V. Augmenting Business Entities with Salient Terms from Twitter. In proceedings of COLING 2014, the 25th International Conference on Computational Linguistics, 2014.