arXiv subjects

Thomas Wieland

Publications and source records attributed to Thomas Wieland.

4 recordsLinked to original sources

arXiv2026

huff: A Python package for Market Area Analysis

Market area models, such as the Huff model and its extensions, are widely used to estimate regional market shares and customer flows of retail and service locations. Another, now very common, area of application is the analysis of catchment areas, supply structures and the accessibility of healthcare locations. The huff Python package provides a complete workflow for market area analysis, including data import, construction of origin-destination interaction matrices, basic model analysis, parameter estimation from empirical data, calculation of distance or travel time indicators, and map visualization. Additionally, the package provides several methods of spatial accessibility analysis. The package is modular and object-oriented. It is intended for researchers in economic geography, regional economics, spatial planning, marketing, geoinformation science, and health geography. The software is openly available via the Python Package Index (PyPI) (https://pypi.org/project/huff/); its development and version history are managed in a public GitHub Repository (https://github.com/geowieland/huff_official) and archived at Zenodo (https://doi.org/10.5281/zenodo.18639559).

stat.AP↗

arXiv2026

Assessing the impact of tourist attractions through the integration of causal inference and demand-side economic analysis: A case study of the Sensoria experience museum in Holzminden, Germany

Assessing the economic impact of tourist attractions typically adopts a demand-side economic approach, which is frequently based on visitor surveys. This approach has several limitations, particularly that it is not based on causal analysis and that the results of surveys could be heavily biased. This study proposes an integrated framework combining causal inference and demand-side economic analysis. As a case study, the impact of the Sensoria experience museum in Holzminden, Germany (opened in September 2024) on tourism demand (measured in monthly overnight stays) is examined. Two difference-in-differences (DiD) approaches are employed to quantify the number of additional overnight stays in the treatment city: a conventional DiD model using a control group and a DiD model using a synthetic control unit. The results are converted into industry-specific expenditures, from which the direct and indirect effects of Sensoria are determined. The results are mixed: the DiD model detects a significantly positive impact in the first year of operation of the new tourist attraction, whereas the DiD model with the synthetic control unit shows a positive but insignificant treatment effect. The significant DiD estimate corresponds to 4,964 additional overnight stays in the first year. When this is offset against the average expenditure of overnight guests, the result is an additional gross turnover of approximately 0.60 million EUR across the hospitality and retail industries and other services. The resulting direct effects and indirect effects amount to approximately 0.24 and 0.22 million EUR, respectively. However, long-term effects cannot (yet) be determined. This study demonstrates that combining the two approaches mentioned holds promise, yet requires a more in-depth analysis, for which suggestions are also discussed regarding how it could be conducted.

stat.AP↗

arXiv2024

Road User Classification from High-Frequency GNSS Data Using Distributed Edge Intelligence

Real-world traffic involves diverse road users, ranging from pedestrians to heavy trucks, necessitating effective road user classification for various applications within Intelligent Transport Systems (ITS). Traditional approaches often rely on intrusive and/or expensive external hardware sensors. These systems typically have limited spatial coverage. In response to these limitations, this work aims to investigate an unintrusive and cost-effective alternative for road user classification by using high-frequency (1-2 Hz) positional sequences. A cutting-edge solution could involve leveraging positioning data from 5G networks. However, this feature is currently only proposed in the 3GPP standard and has not yet been implemented for outdoor applications by 5G equipment vendors. Therefore, our approach relies on positional data, that is recorded under real-world conditions using Global Navigation Satellite Systems (GNSS) and processed on distributed edge devices. As a start-ing point, four types of road users are distinguished: pedestri-ans, cyclists, motorcycles, and passenger cars. While earlier approaches used classical statistical methods, we propose Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) as the preferred classification method, as they repre-sent state-of-the-art in processing sequential data. An RNN architecture for road user classification, based on selected motion characteristics derived from raw positional sequences is presented and the influence of sequence length on classifica-tion quality is examined. The results of the work show that RNNs are capable of efficiently classifying road users on dis-tributed devices, and can particularly differentiate between types of motorized vehicles, based on two- to four-minute se-quences.

cs.LG↗

arXiv2020

Change points in the spread of COVID-19 question the effectiveness of nonpharmaceutical interventions in Germany

Aims: Nonpharmaceutical interventions against the spread of SARS-CoV-2 in Germany included the cancellation of mass events (from March 8), closures of schools and child day care facilities (from March 16) as well as a "lockdown" (from March 23). This study attempts to assess the effectiveness of these interventions in terms of revealing their impact on infections over time. Methods: Dates of infections were estimated from official German case data by incorporating the incubation period and an empirical reporting delay. Exponential growth models for infections and reproduction numbers were estimated and investigated with respect to change points in the time series. Results: A significant decline of daily and cumulative infections as well as reproduction numbers is found at March 8 (CI [7, 9]), March 10 (CI [9, 11] and March 3 (CI [2, 4]), respectively. Further declines and stabilizations are found in the end of March. There is also a change point in new infections at April 19 (CI [18, 20]), but daily infections still show a negative growth. From March 19 (CI [18, 20]), the reproduction numbers fluctuate on a level below one. Conclusions: The decline of infections in early March 2020 can be attributed to relatively small interventions and voluntary behavioural changes. Additional effects of later interventions cannot be detected clearly. Liberalizations of measures did not induce a re-increase of infections. Thus, the effectiveness of most German interventions remains questionable. Moreover, assessing of interventions is impeded by the estimation of true infection dates and the influence of test volume.

q-bio.PE↗