Our linear way of producing has to come to an end. More and more companies and government institutions see the value of circularity of raw materials. The municipal- ity of Amsterdam published several reports to clarify the directions towards circu- larity. A report on waste-chains highlights the importance of organic residual flows for circular economy transition. This thesis focuses on organic residual streams, the lack of understanding of these streams has been mentioned as an obstacle to plan- ning by many of regional stakeholders. The topic of this thesis is formulated within the context of the REPAiR project by the AMS Institute. The starting point of this thesis is the waste collection location in public space and follows the waste back to the retailers of food. The households are identified, after which the used trashcan and the retailers of food are identified.
To create the geographical connection between the three nodes in this thesis, a related works research is conducted. The main source of food waste is food retail and the main disposal is collection of residual waste. Since the municipality of Amsterdam is not collection organic waste separately, residual waste is considered. Multiple researches added spatial dimensions to their material flows, trying to cre- ate a better insight in sinks and flows of the chains. Household level is not reached. This leads to the research question: How can household waste flows be mapped in reverse based on their environment?
To create the mentioned connections, two main methods are developed. First, the method household-to-trashcan and second, the method household-to-retail. The first method is simulating the pedestrian path from a household towards their used trashcan. A least cost raster is created from the BGT, the direct environment is translated into walkability values representing the willingness to walk on a certain object. By comparing the paths to different trashcans, the trashcan reachable with the smallest least cost value is picked. Another factor is inserted, considering that walking to the trashcan is mandatory trip, executed in as less time as possible, city hotspots are inserted. The hypothesis suggests that people are willing to walk further, if the residents can combine their trip to the trashcan with their trip to, for example, the supermarket. By changing the values in the least cost raster, this direction is made preferable. After the validation of this method is concluded, that a stronger influence of the city hotspot (among the tested values), results in a higher positive correlation of the number of households per trashcan and the mass of waste in the trashcan in kg.
The second method household-to-retail, is a demonstration, due to the uncer- tainly and the many involved personal factors. Food retailers in different isochrone areas are given importance values based on their distance towards the considered household and the floor size of the retailer.
After establishing the connections from trashcan to household and from house- hold to food retail, reverse distribution of the waste measure in the trashcan can be executed. In this way is known which household contributed how many kg per year to the trash composition of the used trashcan. When adding the other step, the contribution by the food retailers can be given.
This thesis generates the first step in mapping residual waste flows touching household level. Promising positive correlations between the number of households using a trashcan and the kg measured inside this trashcan are shown. The method household-to-retail cannot be validated and is therefore only a demonstration. The outcome of this thesis is a step in the right direction to be able to follow waste through the waste chain, by analyzing results in trash composition and knowing which households and retailer contribute to this composition.

Recently, the application of machine learning and data fusion techniques on hyperspectral imagery have demonstrated potential for ground cover classification at material level. Hereby, specific locations of resources enclosed in cities (e.g. roof materials) can be identified, which is critically relevant within the field of urban mining. A limitation of this approach is the so-called 'pepper and salt effect', the oversensitivity of the classifiers to spectral variations within a pixel (e.g. chimneys, roof windows). Identifying and correcting affected pixels can be done statistically (e.g. using a majority filter), but not in cases where spectral variations affect a majority of pixels characterizing a surface. A solution to this limitation would be the usage of 3D city models containing the objects inducing the spectral variations. However, such highly detailed 3D city models are often unavailable as they cannot be produced automatically yet. An alternative covered by this research is to use a less detailed 3D city model and semantically enrich it with the required data. As 3D city models are usually produced using a point cloud, such a point cloud is used to perform the enrichment. The main research question addressed is therefore: How can a CityGML LOD2 model be semantically enriched in order to improve material classification performed on roof surfaces?.      To address this, an existing LOD2 model was compared to a point cloud acquired by Ligth Detecation and Ranging and 'deviation' points were identified. This identification uses a distance check for seed selection and performs a region growing with an orientation check. In a subsequent step, 'deviation' point regions were translated into a geometric shape by usage of their Voronoi diagram and fused with the pixels of hyperspectral imagery. Part of this research is also a nominal validation analyzing a total of 41 buildings and 831 pixels located in the south of Rotterdam (Netherlands). Overall kappa values of up to 0.7 and commission errors as low as 10% (for the class 'clean' pixels) were obtained, showing potential of the chosen method. Additionally, a rational validation was performed to assess the impact of potential tolerance of classifiers for 'spectral deviations'. This one only included 10 buildings, but took into account 328 pixels located up to 30% outside the roof surface A main outcome is the recommendation on settings to use depending on the specific user needs. To accurately quantify materials, relatively 'loose' settings are recommended. In contrast, to identify presence of materials, stricter settings are recommended. Beyond this, recommendations to data suppliers and potential applications of the method to other fields are formulated.

Amsterdam circular economy ambitions raise the question of what would be the impact of an organic waste collection system on inhabitants. Impact is an effect of a source on a receptor. Existing impact modelling studies describe impact either from source or from receptor perspective. In spatial planning, both source and receptor are variant over space. However, incorporation of the spatial dimension in existing impact modelling studies is limited, especially in presentation of outcomes.

The objective of this research is to answer: what spatio-temporal method is suitable to assess the impacts of organic waste collection system scenarios in Amsterdam? Tools used are QGIS and GRASS GIS, combined with PyQGIS scripting.

Five impact indicators were defined: three nuisance indicators (noise, odour and congestion), all quantified in three factors (intensity, temporality and affected population), and two sustainable planning indicators (CO2 emission and waste collection), quantified in intensity factor only. Calculations are performed using simple theoretical principles, implemented from both source and receptor point of view.

It was found that basic spatial tools and calculation principles suffice to quantify impact spatio-temporally. For congestion and waste collection modelling network calculations are needed. All indicator outcomes except for CO2 emission are relevant to present spatially. Both source and receptor point of view have their advantages and challenges, and therefore are complementing each other. Aggregation of factor values into one results in loss of information and transparency, which is contradicting to information and transparency gained by the high granularity spatio-temporal model. However, overview and simplicity in presentation of outcomes could be maintained by describing all impact factors per indicator in one clock-like marker. Impact assessment outcomes should be presented for individual sources and receptors. When presenting them on small scale, spatial aggregation into total values per neighbourhood is needed as well. Compared to presentation in a non-spatial graph, the spatial presentation of outcomes is of added value.