This research investigates how a Spatial Decision Support System (SDSS) that is dedicated to communicate flood risk information can be analytically and communicatively supportive for laymen in flood prone areas that have varying preferences for communication methods. To divide residents into groups, the cultural theory of risk is used, by which residents’ perspectives can be classified into fatalist, hierarchist, individualist and egalitarian. Floodlabel.net, a prototype SDSS that aims to inform residents about their personal flood risk, is used as a case. This research concluded that improvements for floodlabel.net regarding the analytical and communicative support could be beneficial for bringing residents in general and residents from a specific group of cultural theory to action. Yet, a platform such as floodlabel.net should always be assisted by other communication methods for an optimal flood risk communication.
The broken window theory is one of the most influential, well-documented, and controversial perspectives in criminology. This study aimed to address a lack of a large-scale spatial data analysis. Police complaints and service requests were used to predict serious street crime through perceived disorder, to find the best trade-off between model performance and granularity for different spatio-temporal scales, and best performing machine learning model types. It provided a 3D Kriging random forest model for monthly tabular area crime numbers recording an R2 of 93,0% and MAPE accuracy of 81,0% for out-of-bag data. The best predictors were instances of social disorder.
The aim of my thesis was therefore to identify which spatial, infrastructural, consumer and sociocultural factors explain spatial variation in electricity, natural gas and water consumption by households within the municipality of Amsterdam. The results illustrate that twelve different factors underlie electricity, natural gas and water consumption. The factors building size, building type, income level and household size predominantly explain spatial variation in electricity consumption by households, whereas the factors building age, presence of district heating and income level are most important for explaining differences in natural gas consumption. Finally, the factors building type, household size and migration history were discovered to be most important for explaining spatial variation in household water consumption.