Abstracts Track 2022

Area 1 - Data Acquisition and Processing

Nr: 13

OSM Data for Regional Energy Modelling: Traffic Energy Demand Model


Hana Elattar and Grégoire Klaus

Abstract: Global climate change and national climate protection measures in Germany have rendered forecasting and simulation models indispensable. As many studies have shown, OpenStreetMap (OSM) data have become a hot topic in the Geo-informatics community in recent years due to their free availability worldwide. Simultaneously, OSM data is being increasingly used in the planning of energy systems thanks to their potential in energy grid modelling. In the study by Valdes et.al (2020), the optimization and simulation models are fed with GIS data from different volunteered geographic information projects, including OpenStreetMap. In this approach OSM data are enriched and processed together with local energy consumption statistics to establish annual consumption profiles on an hourly basis for each location. With the enriched OSM data, it is possible to visualize the geographical location of different demand profiles. In addition, the enriched data can be used to calculate the electricity consumption. Our current research builds on this methodology and expands it into the modelling of traffic energy demand, which would in turn be included in the regional energy model. Through the consideration of the traffic energy model as a secondary layer of the previous work - and therefore dealing with buildings and POIs as destinations rather than nodes for consumption profiles - the classification of OSM data becomes on basis of usage and times of visits (residential, education, leisure, etc.). Our research will investigate methods proposed by researchers in the field for the creation of a model with high accuracy, such as using open-source data to estimate the attraction factor of destinations, as in the work of Klinkhardt et.al (Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models; 2021). The calculation of attractiveness factor takes into account the surface areas and the accessibility of the building or facility (public/ private / office); all included in OSM data to degree. Our final aim from the creation of the model, is to optimize the placement of charging stations (CS) to correspond with the needs of both the electric vehicles (EVs) and the local energy grid. Data on the local energy grid is already available to us (from the results of the above-mentioned work of Javier et.al along with available data online). To accomplish this goal, more types of local data will need to be taken into account, namely the average number of EVs in the given area and data on existing CS including their energy demand. However, working with OSM for energy modelling in Lower Bavaria, only 71% of the OSM data proved to be usable. Due to the requirement to generate a high-quality dataset of building classification and the various work steps of OSM building data processing, the number of data decreased. This calls for deeper validation processes to be carried on the quality of OSM data and its completion in respect to the types of data needed for energy modelling. Although our research will not go in the details of OSM data validation and completion; we will share our applied methods for the acquisition, filtration, and classification of geographical data. The results of the traffic energy demand model will then be subject to comparison with the result of a simulation for the same area created with real-time data from commercial sources. We therefore will be able to calculate the degree of validity of our open-source model.

Area 2 - Modeling, Representation and Visualization

Nr: 11

Time Series-based Complex Networks for Investigating the Differences between Domestic and International Tourists using Social Geotagged Photos


Ahmed Derdouri and Toshihiro Osaragi

Abstract: Tourism studies, in particular those examining the distinctions between domestic and international visitors (DOM and INT), seldom use complex networks. This study suggests an approach based on transforming the time series of DOM and INT's itinerary total times (TM) and traveled distances (DST) into networks where the topology is quantitatively investigated. The Horizontal Visibility Graph (HVG) algorithm is used to turn these series into networks that retain some underlying system characteristics. The time series of yearly, monthly, weekly, and daily TM and DST are considered. These variables are calculated based on collected geotagged photos uploaded to the sharing platform Flickr and taken between 2008 and 2019 in the 23 special wards of the Tokyo Metropolitan Area. The following research questions guide the study: (1) Relying on recorded decadal TM and DST, what are the characteristics of the resulting networks? (2) What such an approach can tell us about the major differences between DOM and INT? (3) Could TM and DST be used as proxies for representing and predicting their mobility trends? and (4) Do weather conditions influence such representativity and predictability? We developed a four-step methodology to address these questions. First, we preprocessed the geotagged photographs to remove noisy records caused by malfunctioning hardware or GPS accuracy issues, then used machine learning to categorize users as local or foreign visitors. Second, using a convolutional neural network, photos were labeled into eight categories to consequently extract time- and distance-based clusters. This was followed by calculating and extracting time series representing the mean of yearly, monthly, weekly, and daily TM and DST recorded by both groups. The third step consisted of mapping the extracted time series into networks using HVG and conducting typical network analyses including mainly node degree distribution 𝑁(π‘˜) and the calculation of the scaling parameter πœ† of 𝑁(π‘˜)=π‘’βˆ’πœ†π‘˜. Then, to investigate the resulted networks’ dynamics, we employed the assumption that suggests the critical value πœ†π‘=𝑙𝑛(3/2) differentiate between stochastic (πœ†>πœ†π‘) and chaotic (πœ†<πœ†π‘) dynamics. The more stable and predictable the system is, the higher πœ† of the exponential degree distribution. Moreover, for comparison and interpretation purposes, we employed four series with known dynamics as reference networks (random series, fractional Brownian motions, Logistic map, and the chaotic Lorenz map). Finally, we investigated possible weather influences by the previous process during bad and ideal conditions. Preliminary results suggest that TM- and DST-based networks exhibit complex dynamics given the resulted exponential distributions. While all TM- and DST-based networks are ruled out for being chaotic systems with few exceptions, TM and DST are still close to the edge of chaos that could be similar to the behavior of random series. TM- and DST-based monthly routines of both DOM and INT during bad, ideal, and mixed weather conditions are much more predictable than those obtained daily and weekly. Overall, DST is found to be the suitable proxy for predicting INT’s daily routines, while TM is much more adequate for forecasting those of DOM. This study highlights the significance of combining social data and non-linear methods for further understanding tourists’ behaviors in general and the differences between those of DOM and INT in particular.

Area 3 - Knowledge Extraction and Management

Nr: 8

GNSS Enhancement in Marine Application of Team Awareness Information System


Maciej Gucma, Remigiusz Lysik, Krzysztof Naus and Mariusz Waz

Abstract: Article presents current status of implementing GNSS enhancement tools that supports human activity in marine offshore application. Geoinformation team awareness applications are expanding domain for safety and security in marine environment. Since their evolvement form as typical military tools now it can be utilized as a civil systems. Main areas where TAK (Team Awareness Kit)software that can be addressed at sea are command/control of USV/ROV, mapping, plotting, geo referenced data exchange and positioning of crafts and vessels. Higher resolutions of mapping and better availability of position requires tools for raw GNSS measurement interpretation and visualization. Some RTK based receivers can operate as a source of TLE (target location error) at Cat1 level (high accuracy of tracked target) but this requires series of transformations for usage in marine applications. Article describes enhancement of GNSS for usage it inside the tracking software based on TAK system for unmanned surface vehicle application.

Nr: 14

What's Behind the Sampling Effort Bias of the Taxonomic Target Groups?


Petr Balej

Abstract: Declining biodiversity and its monitoring is an ongoing challenge for modern biogeography and applied ecology. Environmental niche models (ENMs) are, in this context, a widely used tool. The objective of the modelling is to relate species occurrence data and environmental variables, to describe these relationships and/or to predict unknown values of the biodiversity response variable. Accurate input data are an elementary prerequisite for well-performing models. All applications of ENMs, however, assume that species occurrence data are largely free of spatial error. Nonetheless, such error is inherently present in all species occurrence data. Sampling bias is the most common type of such error, usually caused by uneven sampling efforts or data sharing. Although various methods have been proposed to compensate for sampling bias in species occurrences (e.g. including manipulation of background data, spatial filtering, environmental filtering), their efficiency is questionable. Just a few recently published studies attempted to solve this problem through direct modelling of the sampling bias from unfiltered occurrence data. This method allows creation of accessibility maps (AMs, also called maps of ignorance) that generalize observers' behaviour pattern for individual taxonomic target groups (TGs) depending on their (observers’) specialization. The presented project proposes a new method for creating AMs based on the selection of top 5, 10, or 100 most commonly observed species of any particular target group (TG). I assume that species groups selected in this way would facilitate the analysis of principal drivers of sampling efforts (and, therefore, of sampling bias) in the generated AMs. This information is crucial, for example, for better directing of planned systematic species monitoring campaigns into less investigated regions and, in effect, for improving the effectiveness in the field of biodiversity protection management. At the same time, this approach should help identify the principal environmental variables (sampling effort drivers) with the highest predictive power. The sampbias framework newly introduced this year will make it for the first time possible to quantify the effect of individual sampling drivers within as well as between TGs.

Area 4 - Domain Applications

Nr: 3

Remote Sensing Analysis of Urban Sprawl and Its Impact on Natural Habitats in Dakar-Senegal


Birane Cisse, Abdou Aziz Diouf, Babacar Ndao and Aissatou Sene

Abstract: 1. Context A Sahelian country with an agricultural economy, Senegal suffered from the drought of the 1970s and 1980s. This climatic phenomenon considerably deteriorated agricultural activities in the interior. Many people found it necessary to move to the cities as a means of resilience in order to improve their living conditions (Roquet, 2008). Coastal cities have been the main destinations for rural people (Diop, 2001). The strong centrality of Dakar makes it the first choice of destination for rural people. When they arrived in the city, they were confronted with the problem of housing, and the only sites available to accommodate them were natural areas (Salem, 1998). The process of colonization of this land is still ongoing and has spread to the agricultural areas of the Dakar region. They are the cradle of biodiversity in the region with vegetated areas composed of crop zones and natural woods (Seck, 1998). The development of neighborhoods in Dakar has led to a considerable reduction and disappearance of these natural areas. In this context, the aim is to use its spatial analysis tools to study the impact of urban growth on biodiversity areas in the Dakar region. The objective of this research is to analyze trends in urban growth and its impact on the regression and disappearance of natural habitats in the Dakar region. 3. Research questions - What is the degree of artificialization of natural habitats in the Dakar region from 1988 to 2020? - How have natural habitats evolved according to the different phases of growth of the city? - How has urban growth reduced natural habitats in the Dakar region? 4. Methods Answering the research questions and achieving the objectives will require the development of two approaches to analyze the urban growth and its impact on natural habitat loss in the Dakar region. Landsat data will be used to analyze urban dynamics and areas of interest for biodiversity. Many indexes will be calculated for the analysis of built-up areas, vegetation, and water. In addition, statistical tests will be carried out to highlight the correlations between the growth of the city and the regression or not of natural habitats in the Dakar region with the Spearman method. 5. Expected results - Urbanisation dynamics in the Dakar region from 1988 to 2020; - Evolution of natural habitats in the Dakar region from 1988 to 2020; - Correlation between urban growth and reduction of natural habitats in the Dakar region from 1988 to 2020. 6. References Diop A., 2001. Les inondations à Dakar et banlieue : Contraintes géologiques, Impacts urbanistiques et aménagement durable. Faculté des Sciences et Techniques 1 Institut des Sciences de la Terre. Mémoire de fin d’étude. Roquet D., « Partir pour mieux durer : la migration comme réponse à la sécheresse au Sénégal ? », Espace populations sociétés [En ligne], 2008/1 | 2008, mis en ligne le 01 juin 2010, consulté le 22 octobre 2021. Salem G. (1998). « La santé dans la ville : géographie d’un petit espace dense : Pikine (Sénégal) », Paris, Karthala, 355p. Seck P. A., Moustier Paule. 1998. L'agriculture périurbaine dakaroise : les enjeux de son suivi. In : Agriculture périurbaine en Afrique subsaharienne = Peri-urban agriculture in Sub-Saharan Africa. Moustier P.; CORAF. Montpellier : CIRAD Atelier CIRAD-CORAF, Montpellier, France, 20 April 1998/24 April 1998.