Alexander Zipf

OpenStreetMap (OSM) is a dataset in constant change and this dynamic needs to be better understood. Based on 12-year time series of seven OSM data contribution activities extracted from 20 large cities worldwide, we investigate the temporal dynamic of OSM data production, more specifically, the auto- and cross-correlation, temporal trend, and annual seasonality of these activities. Furthermore, we evaluate and compare nine different temporal regression methods for forecasting such activities in horizons of 1–4 weeks. Several insights could be obtained from our analyses, including that the contribution activities tend to grown linearly in a moderate intra-annual cycle. Also, the performance of the temporal forecasting methods shows that they yield in general more accurate estimations of future contribution activities than a baseline metric, i.e. the arithmetic average of recent previous observations. In particular, the …

We present a benchmark for assessing the capability of Large Language Models (LLMs) to discern intercardinal directions between geographic locations and apply it to three prominent LLMs: GPT-3.5, GPT-4, and Llama-2. This benchmark specifically evaluates whether LLMs exhibit a hierarchical spatial bias similar to humans, where judgments about individual locations' spatial relationships are influenced by the perceived relationships of the larger groups that contain them. To investigate this, we formulated 14 questions focusing on well-known American cities. Seven questions were designed to challenge the LLMs with scenarios potentially influenced by the orientation of larger geographical units, such as states or countries, while the remaining seven targeted locations less susceptible to such hierarchical categorization. Among the tested models, GPT-4 exhibited superior performance with 55.3% accuracy, followed by GPT-3.5 at 47.3%, and Llama-2 at 44.7%. The models showed significantly reduced accuracy on tasks with suspected hierarchical bias. For example, GPT-4's accuracy dropped to 32.9% on these tasks, compared to 85.7% on others. Despite these inaccuracies, the models identified the nearest cardinal direction in most cases, suggesting associative learning, embodying human-like misconceptions. We discuss the potential of text-based data representing geographic relationships directly to improve the spatial reasoning capabilities of LLMs.

Heat poses a significant risk to human health, particularly for vulnerable populations, such as pregnant women, older individuals, young children and people with pre-existing medical conditions. In view of this, we formulated a heat stress-avoidant routing approach in Heidelberg, Germany, to ensure mobility and support day-to-day activities in urban areas during heat events. Although the primary focus is on pedestrians, it is also applicable to cyclists. To obtain a nuanced understanding of the needs and demands of the wider population, especially vulnerable groups, and to address the challenge of reducing urban heat stress, we used an inter-and transdisciplinary approach. The needs of vulnerable groups, the public, and the city administration were identified through participatory methods and various tools, including interactive city walks. Solution approaches and adaptation measures to prevent heat stress were evaluated and integrated into the development of a heat-avoiding route service through a co-design process. The findings comprise the identification of perceived hotspots for heat (such as large public spaces in the city centre with low shading levels), the determination of commonly reported symptoms resulting from severe heat (eg, fatigue or lack of concentration), and the assessment of heat adaptation measures that were rated positively, including remaining in the shade and delaying errands. Additionally, we analysed and distinguished between individual and community adaptation strategies. Overall, many respondents did not accurately perceive the risk of heat stress in hot weather, despite severe limitations. As a result, the heat …

Advances in Artificial Intelligence (AI) and, specifically, in Deep Learning (DL) have fostered geospatial analysis and remote sensing, culminating in the establishment of GeoAI [1, 2] and the solidification of research on methodologies and techniques for AI-assisted mapping [3-7]. Nevertheless, a particular challenge lies in the substantial demand for training examples in DL. Manual labelling of these examples is labour-intensive, consuming a considerable amount of time and financial resources. Alternatively, semi or automated labelling of data emerges as a prominent solution, as exemplified by the tool ohsome2label [8], which harnesses data from the OpenStreetMap [9] to label satellite images. However, moving from characterising object types (road, river, building) based on geometry to categorising them by attributes might result in an imbalanced class distribution in the utilised Machine Learning (ML) dataset.Such imbalances are common in numerous practical applications. Learning from skewed datasets can be particularly challenging and often requires non-conventional ML techniques. A comprehensive awareness of the issues associated with class imbalance, as well as strategies for mitigating them, is essential [10]. In the context of spatial data, the distribution of classes can vary from country to country and region to region, adding a new layer of complexity and exacerbating this issue. In this context, an analysis was conducted on the distribution of road types, defined by the values of the OSM" highway" tag, in diverse-profile nations. The aim was to evaluate the extent of class imbalance and to identify any consistent patterns in the …

Spatially explicit information on carbon fluxes related to land use and land cover change (LULCC) is of value for the implementation of local climate change mitigation strategies. However, estimates of these carbon fluxes are often aggregated to larger areas. We estimated committed gross carbon fluxes related to LULCC in Baden-Württemberg, Germany, using different emission factors. In doing so, we compared four different data sources regarding their suitability for estimating the fluxes: (a) a land cover dataset derived from OpenStreetMap (OSMlanduse); (b) OSMlanduse with removal of sliver polygons (OSMlanduse cleaned), (c) OSMlanduse enhanced with a remote sensing time series analysis (OSMlanduse+); (d) the LULCC product of Landschaftsveränderungsdienst (LaVerDi) from the German Federal Agency of Cartography and Geodesy. We produced a high range of carbon flux estimates, mostly caused …

Urban heat islands, characterized by higher temperatures in cities compared to surrounding areas, have been studied using various techniques. However, during heat waves, existing models often underestimate the intensity of these heat islands compared to empirical measurements. To address this, an hourly time-series-based model for predicting heat island intensity during heat wave conditions is proposed. The model was developed and validated using empirical data from the National Monitoring Network in Temuco, Chile. Results indicate a strong correlation (r > 0.98) between the model’s predictions and actual monitoring data. Additionally, the study emphasizes the importance of considering the unique microclimatic characteristics and built environment of each city when modelling urban heat islands. Factors such as urban morphology, land cover, and anthropogenic heat emissions interact in complex ways, necessitating tailored modelling approaches for the accurate representation of heat island phenomena.

1 GIScience Research Group, Heidelberg University, Heidelberg, Germany; francis. andorful@ uni-heidelberg. de, nir. fulman@ uni-heidelberg. de 2 HeiGIT-Heidelberg Institute for Geoinformation Technology, 69120 Heidelberg, Germany; sven. lautenbach@ uni-heidelberg. de, christina. ludwing@ uni-heidelberg. de, herfort@ uni-heidelberg. de, zipf@ uni-heidelberg. de

The disease transmitting mosquito Aedes Aegypti is an increasing global threat. It breeds in small artificial containers such as rainwater tanks and can be characterized by a short flight range. The resulting high spatial variability of abundance is challenging to model. Therefore, we tested an approach to map water tank density as a spatial proxy for urban Aedes Aegypti habitat suitability. Water tank density mapping was performed by a semi-supervised self-training approach based on open accessible satellite imagery for the city of Rio de Janeiro. We ran a negative binomial generalized linear regression model to evaluate the statistical significance of water tank density for modeling inner-urban Aedes Aegypti distribution measured by an entomological surveillance system between January 2019 and December 2021. Our proposed semi-supervised model outperformed a supervised model for water tank detection …