Jonatan Abraham Jonatan Abraham is a PhD student at the Department of Urban and Regional Studies at KTH and a member of the Urban & Community Safety Research Group. Currently, he is also participating in projects within the collaboration Senseable Stockholm Lab. His research area is primarily individuals’ perceived safety, especially in relation to the physical and social environment in both urban and rural contexts. He often focuses on the spatial component of safety and as such primarily utilizes GIS and spatial statistical methods in his research.
Vania Ceccato Vania Ceccato is a Professor at the Department of Urban Planning and Environment, KTH Royal Institute of Technology, Stockholm, Sweden. She is the head of the Urban & Community Safety Research Group and the coordinator of the Safeplaces network. Ceccato is interested in the relationship between the environment and safety. GIS and spatial statistical methods underlie her research that is on the geography of crime and fear in urban and rural environments; transit safety; the intersectionality of safety, the impact of crime on housing markets; and safety governance.
Jacob Hassler Jacob Hassler is a PhD candidate in Planning and Decision analysis at KTH, Stockholm. His research has a spatial focus. It is primarily concerned with studying how accessibility to, and demand for, emergency services vary across space and time. Disparities in accessibility between urban and rural areas and between population groups, and how these can be measured and decreased through planning, are central to mu my research, as is the use of quantitative methods, primarily GIS.
Ioannis Ioannidis Ioannis Ioannidis is a PhD candidate at the Department of Urban Planning and Environment, KTH Royal Institute of Technology, Stockholm, Sweden. He is also a member of the Urban & Community Safety Research Group. Ioannidis is interested in the relationship between the urban environment and crime. Spatial statistical methods underlie his research that is on the geography of crime in urban environment; the implementation of remote sensing methods for data collection; ML in pattern recognition.