Abstract. Managing flood risk in Europe is a critical issue because climate change is expected to increase flood hazard in many european countries. Beside climate change, land use evolution is also a key factor influencing future flood risk. The core contribution of this paper is a new methodology to model residential land use evolution. Based on two climate scenarios ("dry" and "wet"), the method is applied to study the evolution of flood damage by 2100 along the river Meuse. Nine urbanization scenarios were developed: three of them assume a "current trend" land use evolution, leading to a significant urban sprawl, while six others assume a dense urban development, characterized by a higher density and a higher diversity of urban functions in the urbanized areas. Using damage curves, the damage estimation was performed by combining inundation maps for the present and future 100 yr flood with present and future land use maps and specific prices. According to the dry scenario, the flood discharge is expected not to increase. In this case, land use changes increase flood damages by 1-40 %, to C 334-462 million in 2100. In the wet scenario, the relative increase in flood damage is 540-630 %, corresponding to total damages of C 2.1-2.4 billion. In this extreme scenario, the influence of climate on the overall damage is 3-8 times higher than the effect of land use change. However, for seven municipalities along the river Meuse, these two factors have a comparable influence. Consequently, in the "wet" scenario and at the level of the whole Meuse valley in the Walloon region, careful spatial planning would reduce the increase in flood damage by no more than 11-23 %; but, at the level of several municipalities, more sustainable spatial planning would reduce future flood damage to a much greater degree.

No abstract

No abstract

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tA large amount of research in the past has focused on the relationships between the energy consumption for home-to-work travel and land-use patterns. However, little is known about children's mobility. This paper analyses the energy consumption, travel distances and mode choices for school commuting based on two decennial surveys in Belgium. The results highlight the following: (1) mobility behaviours have evolved drastically over the past decades for school commuting, an evolution that cannot be entirely related to land-use variables, (2) the energy consumption for school commuting is strongly dependent upon the school level, and (3) the links between land-use patterns and energy consumption for school commuting are different than those highlighted within the literature between urban forms and hometo-work commutes. The concentration of secondary schools and tertiary institutions in urban centres induces higher energy consumption rates, whereas the decentralisation of nursery and primary schools across the entire territory leads to very low local energy consumption and increased walking and cycling. These results provide a better understanding of school commuting within the European context and could guide future policies focused on transport energy consumption at the local scale.

Malaria burden is increasing in sub-Saharan cities because of rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between urban environments and malaria. Additionally, no standardized urban land-use/land-cover has been defined for urban malaria studies. Here, we demonstrate the potential of local climate zones (LCZs) for modeling malaria prevalence rate ( Pf PR 2−10 ) and studying malaria prevalence in urban settings across nine sub-Saharan African cities. Using a random forest classification algorithm over a set of 365 malaria surveys we: (i) identify a suitable set of covariates derived from open-source earth observations; and (ii) depict the best buffer size at which to aggregate them for modeling Pf PR 2−10 . Our results demonstrate that geographical models can learn from LCZ over a set of cities and be transferred over a city of choice that has few or no malaria surveys. In particular, we find that urban areas systematically have lower Pf PR 2−10 (5%–30%) than rural areas (15%–40%). The Pf PR 2−10 urban-to-rural gradient is dependent on the climatic environment in which the city is located. Further, LCZs show that more open urban environments located close to wetlands have higher Pf PR 2−10 . Informal settlements—represented by the LCZ 7 (lightweight lowrise)—have higher malaria prevalence than other densely built-up residential areas with a mean prevalence of 11.11%. Overall, we suggest the applicability of LCZs for more exploratory modeling in urban malaria studies.

No abstract

Background The rapid and often uncontrolled rural–urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa’s population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam. Methods Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2–10 years (PfPR2–10) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR2–10 across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population. Results The results suggest that the spatial distribution of PfPR2–10 in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR2–10 and hotspots of malaria prevalence were found near suitable vector breeding sites such as wetlands, marshes and riparian vegetation. In both cities, there is a clear separation of higher risk in informal settlements and lower risk in the more affluent neighborhoods. Additionally, areas associated with urban agriculture exhibit higher malaria prevalence values. Conclusions The outcome of this research highlights that populations living in informal settlements show higher malaria prevalence compared to those in planned residential neighborhoods. This is due to (i) increased human exposure to vectors, (ii) increased vector density and (iii) a reduced capacity to cope with malaria burden. Since informal settlements are rapidly expanding every year and often house large parts of the urban population, this emphasizes the need for systematic and consistent malaria surveys in such areas. Finally, this study demonstrates the importance of remote sensing as an epidemiological tool for mapping urban malaria variations at large spatial extents, and for promoting evidence-based policy making and control efforts.

A systematic and precise understanding of urban socio-economic spatial inequalities in developing regions is needed to address global sustainability goals. At the intra-urban scale, access to detailed databases (i.e., a census) is often a difficult exercise. Geolocated surveys such as the Demographic and Health Surveys (DHS) are a rich alternative source of such information but can be challenging to interpolate at such a fine scale due to their spatial displacement, survey design and the lack of very high-resolution (VHR) predictor variables in these regions. In this paper, we employ satellite-derived VHR land-use/land-cover (LULC) datasets and couple them with the DHS Wealth Index (WI), a robust household wealth indicator, in order to provide city-scale wealth maps. We undertake several modelling approaches using a random forest regressor as the underlying algorithm and predict in several geographic administrative scales. We validate against an exhaustive census database available for the city of Dakar, Senegal. Our results show that the WI was modelled to a satisfactory degree when compared against census data even at very fine resolutions. These findings might assist local authorities and stakeholders in rigorous evidence-based decision making and facilitate the allocation of resources towards the most disadvantaged populations. Good practices for further developments are discussed with the aim of upscaling these findings at the global scale.