Urban areas are evolving in terms of their growing population and their developing systems and infrastructure. This evolution brings about concerns regarding social equity, which refers to the fairness of distribution of certain attributes to groups or individuals. Transport equity is one of the dimensions of this issue, and accessibility distribution is a commonly used indicator for its measurement. Such measurements are applied to urban transport systems to analyze the ease of accessing public services and areas of economic activity. With the development of complex network metrics and increased availability of historical data, temporal analysis of transport systems is gaining more attention. Complex network models based on graph-theoretical approaches provide a repeatable means for quantitative analysis of transport systems in terms of accessibility to opportunities. This research investigates the development of transport equity from the lens of spatial accessibility distribution over time and seeks to analyze its evolution using complex network theory. A random walk based accessibility methodology is developed for this purpose. This methodology is tested in a quantitative case-study, where the City of Helsinki is analyzed in terms of accessibility to education opportunities via walking as a travel mode. The results show that the developed methodology is useful for the identification of accessibility distribution, as well as equity evaluation. With the aid of topological network indicators, and spatial analysis of school locations, the effect of transport systems and land use can be distinguished. This method requires a complete data availability for each historical timestep, as well as adaptation of its parameters based on real-world travel behavior and infrastructural changes. Future research should investigate the multi-layer approach with the inclusion of other travel modes in the model. Furthermore, accessibility to other opportunities such as job locations can be tested with this methodology.

Cash and Voucher Assistance (CVA), a type of humanitarian aid consisting of giving money instead of products, is being used more frequently because of its effectiveness and efficiency in helping people in need (Cash Learning Partnership, 2020b). The debate on using CVA is currently focusing on improving the quality by better incorporating ’voices’ (needs and preferences) of recipients and by enhancing targeting. In targeting it is a major challenge to quickly identify the individuals and families with the biggest needs, given the lack of data (Aiken et al., 2021). Research on ways of measuring impact on and satisfaction of recipients combined with research on demographic and behavioural characteristics of recipients could lead to deeper insights in recipients of trackable CVA modalities (evouchers and ecash). This research uses the marketing literature on customer segmentation combined with machine learning algorithms to come up with an innovative new approach of categorizing recipients of evouchers, using the case of a Red Cross project on Sint Maarten. The main research question is: How can recipients of cash and voucher assistance be categorized using the field of consumer segmentation by using machine learning methods? The objective of this research is to come up with new methods to better understand recipients of CVA. Theories on customer segmentation pointed to the use of data-driven clustering methods to categorize consumers. Combined with a framework of recency, frequency and monetary aspects, recipients of evouchers could be categorized effectively. A required addition to this clustering method is to use a dimension reduction technique to avoid the negative consequence of the curse of dimensionality. Therefore, a two-step approach of dimension reduction and clustering has been applied in this research. It has been found in this research that a factor-cluster approach can lead to insightful clusters using geo-demographic data and behaviour data. Factor analysis has been used to reduce the dimensions while the k-prototype algorithm has been used to cluster into five distinct groups of recipients. The geo-demographic variables that were the most determining in characterizing distinct clusters consisted of: the age of the main beneficiary, the different household compositions and of a constructed factor ’big families and big receivers’. The most distinguishable variables on behaviour were: the number of supermarket visits (frequency), the time between the first voucher was received and the first transaction (recency) and the variables on the amount of money that was spent with the vouchers (monetary). To be able include the ’voice’ of recipients (needs and preferences), a connection between the registra tion data, behavioural data and survey data is needed. In this research only an exploratory connection could be established, due to the lack of a common identifier between the survey data and the other datasets. However, one crucial finding of this research is that it seems like the combination of these data sources can give meaningful insights in the needs, preferences and behaviour of households of Sint Maarten. With these insights specific clusters can be targeted for additional assistance, based on their needs. Recommendations for future studies include studying the validity of the found cluster results with different validation indices on cohesion and compactness, and by using simulations to determine the cluster stability. Before this factor-cluster approach can be deployed in CVA projects, more research on the treatment of limitations of this approach needs to be conducted. This is critical in communi cating the conditions and constraints of this model to humanitarian aid workers in the field. Another recommendation is to improve the design of surveys to measure the needs of recipients. For insightful factor-cluster results on the needs of recipients, survey data should be linked to geo-demographic and behaviour data. More research on including clusters in retargeting methods using feedback loops have a large potential in minimizing targeting errors and more effectively meeting the needs of recipients. With this research, the humanitarian sector can benefit from new ways to understand the needs of the most vulnerable in need. Decision-makers should build upon the feedback of recipients and move towards a new era of humanitarian assistance.