Using detailed Global Navigation Satellite System tracing data emitted by all trucks having a gross vehicle weight of over 3.5 tons in Belgium, this paper assesses the efficiency of the current ...Belgian distance tax system by analyzing its spatial coverage and the matching of the distance taxes with the external costs, globally and locally. Specifically, three research questions are addressed. First, how well do the present charge rates match with external costs? Second, the operationalization of the system requires a good spatial coverage of truck movements. Does the present system guarantee an almost universal coverage? Third, do the distance charges match the external costs? We find that if the distance tax scheme differentiates regionally, it still misses large variations in noise costs. The current tracing infrastructure also captures only part of the truck operations on the territory. If distance tolls for trucks remain the backbone of the taxation of truck operations, it then needs further refinement in time and space if one wants it to be the major tool to correct for the external costs.
Detecting communities in large networks has become a common practice in socio-spatial analyses and has led to the development of numerous dedicated mathematical algorithms. Nowadays, however, ...researchers face a deluge of data and algorithms, and great care must be taken regarding methodological questions such as the values of the parameters and the geographical characteristics of the data. We aim here at testing the sensitivity of multi-scale modularity optimized by the Louvain method to the value of the resolution parameter (introduced by Reichardt and Bornholdt (Phys Rev Lett 93(21):218701,
2004
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https://doi.org/10.1103/PhysRevLett.93.218701
) and controlling the size of the communities) and to a number of spatial issues such as the inclusion of internal loops and the delineation of the study area. We compare the community structures with those found by another well-known community detection algorithm (Infomap), and we further interpret the final results in terms of urban geography. Sensitivity analyses are conducted for commuting movements in and around Brussels. Results reveal slight effects of spatial issues (inclusion of the internal loops, definition of the study area) on the partition into job basins, while the resolution parameter plays a major role in the final results and their interpretation in terms of urban geography. Community detection methods seem to reveal a surprisingly strong spatial effect of commuting patterns: Similar partitions are obtained with different methods. This paper highlights the advantages and sensitivities of the multi-scale Louvain method and more particularly of defining communities of places. Despite these sensitivities, the method proves to be a valuable tool for geographers and planners.
atlas.brussels is an interactive online geovisualization tool that questions the spatial extension and the internal fragmentation of the Brussels metropolitan area. To do this, the tool focuses in an ...original way on spatial interaction networks considered at both the national and the metropolitan scales. Among these networks appear movements of people (as home-to-work commuting), communications (as phone calls) and economic interactions (through the road freight interactions). Therefore, atlas.brussels combines, supplements, expands and promotes researches made on the metropolitan area of Brussels, and remains open to the integration of other datasets.
Avec la multiplication des capteurs, nous disposons désormais de quantités massives de données à l’échelon individuel dans divers champs de la géographie et tout particulièrement en géographie des ...transports. Dans cette contribution, nous illustrons comment une nouvelle source de données issue du système de prélèvement kilométrique des camions en Belgique peut présenter une plus-value pour le géographe. Dans ce cas précis, le suivi spatio-temporel exhaustif de la quasi-totalité des camions est utilisé afin de dessiner une géographie précise des circulations de camions en Belgique. Utilisées comme proxy des échanges de marchandises entre lieux, ces données nous permettent d’explorer la place spécifique d’une ville donnée dans le réseau logistique belge. Tout en discutant de l’apport de ces « big-data » spatialisées, nous présentons tout d’abord la méthodologie mise en œuvre pour passer de données GPS brutes à une matrice origines-destinations plus classique synthétisant les interactions entre lieux. Plusieurs méthodologies sont ensuite appliquées à cette matrice pour révéler comment les big-data peuvent nous aider à identifier une des facettes de la polarisation économique de Liège.
The objective is to refresh the geography of Belgium using interactions between places by means of a community detection algorithm (Louvain Method) inspired by Complex theory and Data Sciences. ...Places that are tightly related are optimally clustered into communities, leading to a new and optimal partition of Belgium. Migrations and commuting movements (Census11) are here analysed. We obtain a mosaic of “interaction fields” that are here interpreted in terms of methodological choices, human and urban geography as well as Belgian political dilemmas. They give the opportunity to remind that researchers have to control the impact of their methodological choices and that each type of data leads to a different geographical partitioning, with one major unexpected common spatial feature in Belgium: the pre-eminence of the provincial borders. This perfectly fits with current political questioning.
This paper uses on-line railway travel requests from the iRail schedule-finder application for assessing the suitability of that kind of big data for transportation planning and to examine the ...temporal and regional variations of the travel demand by train in Belgium. Travel requests are collected over a two-month period and consist of origin-destination flows between stations operated by the Belgian national railway company in 2016. The Louvain method is applied to detect communities of tightly-connected stations. Results show the influence of both the urban and network structures on the spatial organization of the clusters. We also further discuss the implications of the observed temporal and regional variations of these clusters for transportation travel demand and planning.
A l’aide de données relationnelles conventionnelles (migrations résidentielles, navettes de travail) et moins conventionnelles (appels de téléphonie mobile), l’espace dans et autour de la Région de ...Bruxelles-Capitale est partitionné en groupes de lieux fortement inter-reliés à l’aide d’une méthode mathématique de détection de communautés. Les partitions obtenues conduisent à des structures spatiales fortes alors que ni la distance ni les caractéristiques des lieux ne sont prises en compte par la méthode. Cet article illustre comme les grandes bases de données (big data) et leurs méthodes spécialement dédiées offrent de nouvelles opportunités pour les analyses urbaines (délimitation des bordures urbaines, formalisation des structures intra-urbaines) et donnent ici l’occasion de rappeler qu’aucune structure ne peut s’interpréter sans la maîtrise des données, des outils utilisés mais aussi des théories régionales et urbaines.
Aan de hand van conventionele (woonmigraties en woon-werkverplaatsingen) en minder conventionele (mobiele telefoongesprekken) relationele gegevens wordt de ruimte in en rond het Brussels Hoofdstedelijk Gewest opgedeeld in groepen van plaatsen, die nauw met elkaar verbonden zijn, op basis van een wiskundige methode voor de detectie van gemeenschappen. De verkregen indelingen (partities) leiden tot sterke ruimtelijke structuren, hoewel de methode geen rekening houdt met de afstand en de karakteristieken van de plaatsen. Dit artikel licht toe dat de grote databanken (big data) en hun speciaal daarvoor bestemde methodes nieuwe mogelijkheden voor stadsanalyses (afbakening van de stadsranden en formalisering van de intrastedelijke structuren) aanreiken en een kans bieden om eraan te herinneren dat geen enkele structuur geïnterpreteerd kan worden zonder de gegevens, gebruikte instrumenten maar ook de regionale en stedelijke theorieën te kennen.
Using conventional relational data (residential migrations, commutes to and from the workplace) and less conventional relational data (mobile telephony calls), the space in and around the Brussels-Capital Region is partitioned into groups of closely inter-related places using a mathematical community detection method. The partitions obtained lead to strong spatial structures, while neither the distance nor the characteristics of the places are taken into account in this method. This article illustrates how large databases (big data) and their specific methods provide new opportunities for urban analyses (delimitation of urban borders, formalisation of intra-urban structures), and remind us here that no structure may be interpreted without a thorough understanding of data, the tools used and regional and urban theories.
Aan de hand van conventionele (woonmigraties en woon-werkverplaatsingen) en minder conventionele (mobiele telefoongesprekken) relationele gegevens wordt de ruimte in en rond het Brussels ...Hoofdstedelijk Gewest opgedeeld in groepen van plaatsen, die nauw met elkaar verbonden zijn, op basis van een wiskundige methode voor de detectie van gemeenschappen. De verkregen indelingen (partities) leiden tot sterke ruimtelijke structuren, hoewel de methode geen rekening houdt met de afstand en de karakteristieken van de plaatsen. Dit artikel licht toe dat de grote databanken (big data) en hun speciaal daarvoor bestemde methodes nieuwe mogelijkheden voor stadsanalyses (afbakening van de stadsranden en formalisering van de intrastedelijke structuren) aanreiken en een kans bieden om eraan te herinneren dat geen enkele structuur geïnterpreteerd kan worden zonder de gegevens, gebruikte instrumenten maar ook de regionale en stedelijke theorieën te kennen.
A l’aide de données relationnelles conventionnelles (migrations résidentielles, navettes de travail) et moins conventionnelles (appels de téléphonie mobile), l’espace dans et autour de la Région de Bruxelles-Capitale est partitionné en groupes de lieux fortement inter-reliés à l’aide d’une méthode mathématique de détection de communautés. Les partitions obtenues conduisent à des structures spatiales fortes alors que ni la distance ni les caractéristiques des lieux ne sont prises en compte par la méthode. Cet article illustre comme les grandes bases de données (big data) et leurs méthodes spécialement dédiées offrent de nouvelles opportunités pour les analyses urbaines (délimitation des bordures urbaines, formalisation des structures intra-urbaines) et donnent ici l’occasion de rappeler qu’aucune structure ne peut s’interpréter sans la maîtrise des données, des outils utilisés mais aussi des théories régionales et urbaines.
Using conventional relational data (residential migrations, commutes to and from the workplace) and less conventional relational data (mobile telephony calls), the space in and around the Brussels-Capital Region is partitioned into groups of closely inter-related places using a mathematical community detection method. The partitions obtained lead to strong spatial structures, while neither the distance nor the characteristics of the places are taken into account in this method. This article illustrates how large databases (big data) and their specific methods provide new opportunities for urban analyses (delimitation of urban borders, formalisation of intra-urban structures), and remind us here that no structure may be interpreted without a thorough understanding of data, the tools used and regional and urban theories.
Using conventional relational data (residential migrations, commutes to and from the workplace) and less conventional relational data (mobile telephony calls), the space in and around the ...Brussels-Capital Region is partitioned into groups of closely inter-related places using a mathematical community detection method. The partitions obtained lead to strong spatial structures, while neither the distance nor the characteristics of the places are taken into account in this method. This article illustrates how large databases (big data) and their specific methods provide new opportunities for urban analyses (delimitation of urban borders, formalisation of intra-urban structures), and remind us here that no structure may be interpreted without a thorough understanding of data, the tools used and regional and urban theories.
Despite the fact that freight transport has a huge impact on the economy and the environment, Belgian datasets have always been scarce or restricted to very small a-spatial samples. Spatial data ...collected in Belgium for toll-paying trucks are here examined, and geographical structures and dynamics are extracted from this massive dataset. The originality of this dataset is its exhaustivity and its real-time approach: the location of all the trucks circulating in Belgium is collected every 30 s.
The paper first relates to the methodology applied when using and transforming big data generated by On Board Units GNSS (cleaning, transforming and pre-processing). Second, it maps and comments on the movements (traffic) and stops of trucks within the whole country, providing a clear picture of the Belgian situation, useful for regional planners and logistics companies. Finally, the flows of trucks observed between Belgian locations enable the country to be divided into mathematical communities of places that interact the most. Analyses are performed for sub-categories based on the country of registration, underlining the spatial specificities of freight transit in Belgium. This exploratory spatial data analysis enables to reveal not only multi-level spatial structures associated with urban hierarchies and the transport infrastructure, but also firm locations or political organizations and to consider the complexity and interconnectivity of any measure taken for a more sustainable future. With a clear methodological framework to cope with the data pre-processing, this paper opens the way to various potential applications linked with freight transportation in Belgium.
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