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The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020–December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65  years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies.

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

<p>Water withdrawals for irrigated crop production constitute the largest global consumer of blue water resources. Monitoring the dynamics of irrigated crop cultivation allows to track changes in water consumption of irrigated cropping, which is particularly paramount in water-scarce arid and semi-arid areas. We analyzed changes in irrigated crop cultivation along with occurrence of hydrological droughts for the Amu Darya river basin of Central Asia (534,700 km<sup>2</sup>), once the largest tributary river to the Aral Sea before large-scale irrigation projects have grossly reduced the amount of water that reaches the river delta. We used annual and seasonal spectral-temporal metrics derived from Landsat time series to quantify the three predominant cropping practices in the region (first season, second season, double cropping) for every year between 1988 and 2020. We further derived unbiased area estimates for the cropping classes at the province level based on a stratified random sample (n=2,779). Our results reveal a small yet steady decrease in irrigated second season cultivation across the basin. Regionally, we observed a gradual move away from cotton monocropping in response to the policy changes that were instigated since the mid-1990s. We compared the observed cropping dynamics to the occurrence of hydrological droughts, i.e., periods with inadequate water resources for irrigation. We find that areas with higher drought risks rely more on irrigation of the second season crops. Overall, our analysis provides the first fine-scale, annual crop type maps for the irrigated areas in the Amu Darya basin. The results shed light on how institutional changes and hydroclimatic factors that affect land-use decision-making, and thus the dynamics of crop type composition, in the vast irrigated areas of Central Asia.</p>