Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. Defining urban boundaries that consider socio-economic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, non-overlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.Comment: 32 pages, 7 figures, International Journal of Geographic Information Scienc

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices

SLiCE: An Open Data Model for Scalable High-Definition Life Cycle Engineering, Hotspot Analysis and Dynamic Assessment of Buildings.

Abstract Building construction and operation are responsible for around 40% of global energy-related greenhouse gas emissions. To identify emissions reduction and removal potentials as well as wider environmental impacts, researchers, policy, and decision makers need comprehensive life cycle sustainability assessment insights on individual buildings and building stocks at large. This article proposes a data model for scalable, high-definition life cycle analysis of building – the SLiCE data model – as a promising solution to overcome the limitations identified for existing models. The article conceptualizes the problem within the Space-Time-Indicator Nexus; presents the proposed SLiCE data structure; and showcases practical uses of SLiCE data for environmental hotspot analysis as well as for dynamic assessment of climate impacts. The open SLiCE data model and SLiCE hotspot analysis tool are henceforth available for implementation within life cycle assessment of building and building stocks, enabling comprehensive insights on buildings’ environmental impacts across spatiotemporal scales. Software and data availability The SLiCE building data model as well as the presented implementation in the SLiCE hotspot analysis prototype are open source and available with this article. The SLiCE hotspot analysis, implemented as an IPython Jupyer Notebook with interactive widgets, tool is available on Github (https://github.com/mroeck/slice_hotspots/), with the submission pre-release published via Zenodo (https://zenodo.org/badge/latestdoi/645859866). All items are published under a GNU General Public License v3.0. We encourage you to review, reuse, and refine the model and scripts and share-alike. Preprint (not peer-reviewed) Röck M, Passer A, Allacker K. “SLiCE: An Open Data Model for Scalable High-Definition Life Cycle Engineering, Hotspot Analysis and Dynamic Assessment of Buildings.” 2023, Preprint DOI: https://doi.org/10.5281/zenodo.836924

Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction

Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets predominantly feature zero values, indicating no incidents, with sporadic high-risk values for severe incidents. Notably, a majority of current models, especially deep learning methods, focus solely on estimating risk values, overlooking the uncertainties arising from the inherently unpredictable nature of incidents. To tackle this challenge, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks (STZITD-GNNs). Our model merges the reliability of traditional statistical models with the flexibility of graph neural networks, aiming to precisely quantify uncertainties associated with road-level traffic incident risks. This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity. Furthermore, a zero-inflated component helps to identify the non-incident risk scenarios. As a result, the STZITD-GNNs effectively capture the dataset's skewed distribution, placing emphasis on infrequent but impactful severe incidents. Empirical tests using real-world traffic data from London, UK, demonstrate that our model excels beyond current benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also in its adeptness at curtailing uncertainties, delivering robust predictions over short (7 days) and extended (14 days) timeframes