about
Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts.The Measles Vaccination Narrative in Twitter: A Quantitative Analysis.Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram.Harvesting ambient geospatial information from social media feedsA Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging OpportunitiesAuthoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?The study of slums as social and physical constructs: challenges and emerging research opportunitiesCrowdsourcing urban form and functionTHE EFFECT OF IN-GROUP FAVORITISM ON THE COLLECTIVE BEHAVIOR OF INDIVIDUALS' OPINIONSTriangulating Social Multimedia Content for Event Localization using Flickr and TwitterAssessing the impact of demographic characteristics on spatial error in volunteered geographic information featuresInternational RelationsMeasuring slum severity in Mumbai and Kolkata: A household-based approach#Earthquake: Twitter as a Distributed Sensor SystemComparing the spatial characteristics of corresponding cyber and physical communitiesDemarcating new boundaries: mapping virtual polycentric communities through social media contentDisease modeling within refugee camps: A multi-agent systems approachGeosocial gauge: a system prototype for knowledge discovery from social mediaSocial Simulations for Border SecurityTowards a collaborative geosocial analysis workbenchExploring the Emergence of Organized Crime in Rio de Janeiro: An Agent-Based Modeling ApproachMap mashups, Web 2.0 and the GIS revolutionMapping for the MassesNeoGeography and Web 2.0: concepts, tools and applicationsRandom planar graphs and the London street networkKey challenges in agent-based modelling for geo-spatial simulationBots in Nets: Empirical Comparative Analysis of Bot Evidence in Social NetworksExodus 2.0: crowdsourcing geographical and social trails of mass migration
P50
Q33639843-28CCB94E-7E88-41D4-8660-61296E8AC2E5Q36907123-3AAA800A-99F1-4D09-8A1C-4D7E57346D86Q47563349-D2160E43-544B-4C7B-AB32-96AE6FA9CE57Q56482520-FA9F8FF6-D027-41A5-B671-7E61DFEB437BQ56773304-E918DA0D-2DDE-4781-B448-7A26947C8B2FQ56773306-0DE1E39A-F307-40FA-9C92-FC313CE6ED3DQ56773308-A8F0A481-AF2C-4BD3-874C-6088F5F3780DQ56773311-220CDDF3-88DE-4A57-BE93-B95973A81EA3Q56773315-3E548857-C792-4737-8392-1CD69883280FQ56773320-914C6831-D633-4C70-91CE-DA6BF26130AEQ56773324-9011D53C-DF7D-45E1-8D08-31BA458296B0Q56773331-659D9370-158E-46ED-9296-0FE536099A23Q56773333-1BB224E0-D8C5-4368-97D9-DD6940115A51Q56773336-10991585-04BD-4911-94F6-9AA7355B2E0AQ56773338-EAF774FC-2719-4549-8BF0-64776FB35C66Q56773341-C1C19C01-BFE3-4E2B-B5F3-C32AE3D54F51Q56773346-B8D1FCA7-38FA-46A6-9BDB-127D1AB17AE3Q56773347-E1230625-EFEF-4312-AB3B-090CE37F621EQ56773353-E5DE17AC-8D1B-4D1F-A7A4-33475100B744Q56773360-22D6900F-FD81-4716-BA00-1FF20E6F7C3DQ56773364-4F34C8D2-B8F1-46F3-AF23-529301FC865EQ56773369-194D4824-2CE5-4522-A178-EEFF6036F4DDQ56773376-63527613-4404-4C2D-9F51-0D206036E18FQ56773380-D7613F32-80B7-4A68-ABAB-3EB0E362D18BQ56773383-263AE4F9-3B38-4DF2-8CAB-66176DB42A47Q56773386-0ACD02EB-3A91-4452-B1FD-0B268110D545Q56773389-C52F0F88-7042-4E7A-9347-CC5F107532DCQ60198401-A9F49CC2-2A95-4EE1-9169-258BE7E700D0Q60198404-64872330-EB9A-437D-990A-2C7B1B637A68
P50
description
hulumtues
@sq
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Andrew Crooks
@ast
Andrew Crooks
@en
Andrew Crooks
@es
Andrew Crooks
@nl
Andrew Crooks
@sl
type
label
Andrew Crooks
@ast
Andrew Crooks
@en
Andrew Crooks
@es
Andrew Crooks
@nl
Andrew Crooks
@sl
prefLabel
Andrew Crooks
@ast
Andrew Crooks
@en
Andrew Crooks
@es
Andrew Crooks
@nl
Andrew Crooks
@sl
P1053
L-2018-2017
P106
P1153
25521036800
P1960
RDQF0ScAAAAJ
P2002
AndyCrooks
P21
P2381
P2456
P31
P3829
P496
0000-0002-5034-6654