Geospatial Constraints Research Articles

Optimization model for urban ecological network connectivity considering geospatial constraints

ABSTRACT Connectivity enhancement of ecological networks is an efficient and advantageous spatial planning strategy, and it is a hot research topic to analyze ecological networks based on complex systems theory and perform model optimization. As a typical geographic network, the evolution process of ecological network, such as node and edge growth, will be subject to geospatial constraints, and the current research lacks optimization models that take the geospatial constraints into account. This study focused on urban ecological network connectivity and the distribution of nodes as well as structural fractures where the connectivity of the ecological network was hindered were analyzed. Then it introduced a local world network model to optimize network connectivity and designed a network growth mechanism with limited added nodes and optimal connections. An ecological network optimization model considering geospatial constraints was constructed, and the network closeness index was used to fit the objective function curve to select optimization schemes. This study used Shenzhen, China as a case study, and the results indicated that the initial ecological network was significantly fractured by urban construction. In the optimization scenario, the network evolution reached its optimal state at the simulation step size of t = 26. At this point, the closeness index of the optimized network was increased by 15%. The optimization model constructed in this paper emphasizes the importance of functional restoration of existing network nodes, and the results of the study can provide support for ecospatial layout optimization and management.

The formation and proximity mechanism of population flow networks under multiple traffic in China

Population flow within a specific spatial range can promote social and economic reaggregation and diffusion. However, few empirical studies have paid close attention to population flow networks (PFNs) under different traffic. Tencent migration data were used to analyse the spatial patterns and aims to identify how proximity affects PFNs among 338 Chinese cities in 2018. The results indicated that the weighted average distance of population flow by highways and railways are 252.39 km and 566.16 km, showed restriction by distance decay in space. However, the weighted average distance of population flow by aviation was 1282.71 km, was less affected by geospatial constraints. PFNs involving highways, railways, and aviation display core-periphery, corridor, and hub-spoke structures, respectively. China's PFNs forms a diamond structure under multiple traffic, with Beijing, Shanghai, Shenzhen, Guangzhou, Chongqing, and Chengdu as the vertices. Negative binomial regression models were used to determine how proximities affect PFNs under multiple traffic. The estimated coefficients for geographical proximity, institutional proximity, and cultural proximity were significantly positive at the 1 % level. The influence of climate proximity on the PFNs may be complex, with a positive impact on PFNs on highways and railways but a negative impact on PFNs involving aviation.

A newly isolated alkaliphilic cyanobacterium for biomass production with direct air CO2 capture

Large-scale microalgal biomass production usually relies on concentrated CO2 as the carbon source which is a major logistical and economic challenge because of the required infrastructure and geospatial constraints of siting large algal production facilities close to concentrated CO2 sources. By taking advantage of enhanced CO2 mass transfer within alkaline media due to the reaction between CO2 and OH-, carbon can be directly captured from the ambient air and used by the algae. Achieving such goal requires algal strains that can grow fast under alkaline conditions. In this study, a newly isolated alkaliphilic cyanobacterial strain Cyanobacterium sp. PNNL-SSL1 was characterized for its temperature, pH, and salinity tolerance. The result shows that PNNL-SSL1 could sustain grow at pH above 11.2 but exhibited the highest growth rates at pH below 10.5. The strain is a warm season strain and grows the best at 10 PSU (practical salinity unit). The strain was then evaluated in indoor climate simulation ponds under outdoor-relevant pond cultivation conditions with carbon supplied only through direct air CO2 capture at the culture surface. Two batch culture runs, at slightly different initial pH, exhibited average biomass productivity of 15.2 g m-2 day-1 on ash-free dry weight basis. CO2 was directly captured from the air at an average rate of 21.6 g CO2 m-2 day-1 (78.8 tons ha-1 yr-1), contributing 74% of the carbon fixed in the biomass. The results demonstrated that PNNL-SSL1 is a promising strain for biomass production using air CO2 as the carbon source.

A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output.

Cohesive Group Nearest Neighbor Queries on Road-Social Networks under Multi-Criteria

The group nearest neighbor (GNN) search on a road network <inline-formula><tex-math notation="LaTeX">$G_r$</tex-math></inline-formula> , i.e., finding the spatial objects as activity assembly points with the smallest sum of distances to query users on <inline-formula><tex-math notation="LaTeX">$G_r$</tex-math></inline-formula> , has been extensively studied; however, previous works neglected the fact that social relationships among query users, which ensure the maximally favorable atmosphere in the activity, can play an important role in GNN queries. Meanwhile, the ratings of spatial objects can also be used as recommended guidelines. Many real-world applications, such as location-based social networking services, require such queries. In this paper, we study two new problems: (1) a GNN search on a road network that incorporates cohesive social relationships (CGNN) and (2) a CGNN query under multi-criteria (MCGNN). Specifically, both the query users of highest closeness and the corresponding top- <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> objects are retrieved. To address critical challenges on the effectiveness of results and the efficiency of computation over large road-social networks: (1) for CGNN, we propose a filtering-and-verification framework. During filtering, we prune substantial unpromising users and objects using social and geospatial constraints. During verification, we obtain the object candidates, among which the top <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> are selected, with respect to the qualified users; (2) for MCGNN, we propose threshold-based selection and expansion strategies, where different strict boundaries are proposed to ensure that correct top- <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> objects are found early. Moreover, we further optimize search strategies to improve query performance. Finally, experimental results on real social and road networks significantly demonstrate the efficiency and efficacy of our solutions.

Abnormal Trajectory Detection Based on Geospatial Consistent Modeling

Anomalous trajectory detection plays a significant role in fraud detection and adverse events monitoring for ride-hailing services. The spatial and temporal dynamics of road networks and the sparsity of trajectories make anomalous trajectory detection a challenging task. Most existing methods are based on density and isolation approaches, which ignore geographical information. Motivated by these challenges and shortcomings, we propose a novel method, which considers geospatial constraints of the trajectories and avoids sparsity issues. In our method, the geographical information and topological constraints of trajectories are embedded into structured vector space. Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are used to model common trajectory features. Our method could identify anomalous trajectories and determine which parts are responsible for anomalies by using these features. Experiments on two real-world datasets have been conducted, and results demonstrate the effectiveness and feasibility of the proposed method.

Spatiotemporal analysis of industrial excess heat supply for district heat networks in Switzerland

Industrial Excess Heat (IEH) is an underutilised resource which could contribute to decarbonising the heat supply. It is particularly well suited for supplying district heat networks (DHN), thereby enabling the capture and distribution of excess energy from industries and incineration plants. However, as heat cannot be readily transported over long distances, there is a need to analyse the balance of supply and demand over time taking into account the geospatial constraints placed on the linking of IEH supplies and DHN demands. This work presents an analysis of the potential for the supply of DHN systems using high and low network temperatures by IEH in Switzerland. A spatial clustering method is used to link potential supplies and demands, and monthly supply and demand curves are used to calculate the potential for IEH supply subject to spatiotemporal constraints. A further analysis deals with the technical potential for seasonal storage to shift surplus IEH energy from summer to winter. A total resource of 12TWh/y of IEH was found, but spatial and temporal constraints limited its utilisation to between 7.7TWh/y and 10.5TWh/y depending the scenario considered. 17.4% of total heat demand could be supplied by IEH using low temperature DHN and seasonal storage.

Вибір майданчика для розташування вітроелектростанцій на підставі ГІС-підходу

Запропоновано алгоритм вибору майданчика для розташування вітроелектростанцій (ВЕС) шляхом застосування ГІС-підходу, який містить, по-перше, вибір досліджуваної території зі сприятливими кліматичними умовами за результатами оцінки вітроенергетичного потенціалу; по-друге, врахування на досліджуваній території геопросторових обмежень шляхом збирання даних, їх аналізу та побудови карт компонентів довкілля, які можуть впливати чи зазнавати впливу від ВЕС, встановлення критеріїв геопросторових обмежень та їх графічного представлення у вигляді покомпонентних карт орографічних, технічних, екологічних обмежень та комплексної карти геопросторових обмежень; по-третє, виділення перспективних майданчиків розташування ВЕС шляхом накладання карт перспективних швидкостей вітру на заданій висоті та карти геопросторових обмежень. Теоретично обґрунтовано врахування трьох типів обмежень: орографічних, технічних (дорожньої мережі та енергомережі) та природних екологічних (природно-заповідного фонду (ПЗФ), водні об'єкти, ліси) і соціально-економічних екологічних (населені пункти, аеродроми) обмежень шляхом встановлення геопросторових виключень та екологічно безпечних відстаней. Застосовано алгоритм геопросторового планування майданчика для розташування ВЕС для Івано-Франківської області. Побудовано комплексну карту геопросторових обмежень території зі сприятливими вітрокліматичними умовами. Побудовано карту перспективних майданчиків розташування ВЕС. Виділено 24 безпечні перспективні майданчики розташування ВЕС загальною площею 4514,22 га в межах досліджуваної території. На прикладі Vestas V112 3.3 МВт розраховано можливу потужність ВЕС на перспективних майданчиках, яка для 88 ВЕУ становила 290,4 МВт.

Geospatially Constrained Workflow Modeling and Implementation

With rapid development and application of mobile internet, geographic information in the field of business process is now more widely used. There are more and more researches in the field of the relationships between geographic information and workflow modeling. According to the workflow with geospatial constraints, this paper first discusses the geospatial constraints theory deeply, proposes a new concept of geospatial constraints unit, and then designs a geospatial constraint net model (GCNet). Secondly, this paper designs a new workflow model with geospatial constraints (GCWF-net) based on GCNet and workflow net (WF-net), and then analyzes some properties of the model. Finally, this paper discusses how to put GCWF-net into application practice from three aspects: extending PNML (Petri Net Markup Language) labels for GCWF-net, converting PNML to BPEL (Business Process Execution Language) and implementing BPEL.

Optimal Design of Transmission Line Route Based on GIS

Transmission line route reflects the geospatial constraints on the grid. Environmental factors affecting the power transmission line route design are diverse and complex. This paper studies the method based on GIS (Geographic Information System) and Dijkstra algorithm. Establish environmental factors hierarchical model of transmission line route design using IAHP and gridded map and obtain each grid’s weight. Take into account weight of each grid’s environmental factors and number of the corner in order to build the branch weights of Dijkstra algorithm. After that the optimal route can be obtained using Dijkstra algorithm.

Agenames a stratigraphic information harvester and text parser

A common task for earth scientists is the search for stratigraphic background information on a certain rock unit, e.g. its age and properties and its position within the hierarchy of stratigraphic units. Analogously, when geoscientists search for information in databases or within the internet the stratigraphic and geospatial constraints serve as a first orientation within a huge amount of data. However, spatio-temporal information is mostly only implicitly encoded e.g. in the title and abstract given in bibliographic databases and library catalogues. Means to decode spatial information from such texts has become commonly available through gazetteers and geocoding services, but the paleotemporal information remains elusive. Agenames is a stratigraphic information harvester and text parser which offers a web-service to parse geological texts and identify stratigraphic terms. The service has both a web-based GUI and a REST interface. The Agenames ontology records the stratigraphic rank of e.g. a chronostratigraphic or lithostratigraphic unit and the hierarchical relations between terms. Any geologic body has an associated age of origin, assigned by relation to a geochronologic unit from the geologic time scale. In a given text Agenames will identify potential stratigraphic keywords and use these terms to assign a geological age estimate. The web-service can also be used to augment web portals or library catalogues. Agenames can be used to generate indices to infer the relations of library catalogue entries or web pages to the approximate geological epoch that is covered in a text. This allows, for instance, including stratigraphic age into a catalogue search without the need to specify all possibly relevant terms in complex queries.

Study of the variation of schistosomiasis risk in Lake Poyang in the People's Republic of China using multiple space-borne sensors for the monitoring and modelling.

The dynamics of the Poyang Lake in Jiangxi province, People's Republic of China has been monitored to demonstrate the association of various variables with the distribution of schistosomiasis transmission with particular reference to the annual variation of the habitats for the Oncomelania snail, the intermediate host of Schistosoma japonicum. This was studied with multiple space-borne sensors, including the ENVISAT radar altimeter (RA-2) and MODIS/Terra radiometry data products such as the 16-day enhanced vegetation index, the 8-day sun reflectance, and the derived modified normalized difference water index. The measurements of physical properties were in good accordance with previous reports based on in situ gauge data, spectroradiometry and other optical methods, which encouraged us to build a predictive model based on reported geospatial constraints to assess the limits of potential variation of the snail habitat areas. The simulated results correspond fairly well with surveys conducted by local authorities showing a correlation coefficient of 0.82 between highpotential habitat areas and local estimates in a 9-year (2002-2010) analysis. Taken together, these data indicate that spaceborne observations and in situ measurements can be integrated and used as a first step of a monitoring system for control and analysis of the potential of schistosomiasis dissemination. Since the true range and intensity of transmission in the study region remain elusive at present, a long-term survey around the lake is warranted to build a robust, parametric model.

Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions

That the multi-source remote sensing information integrates knowledge-based geospatial constraints to develop efficient and practical Land cover classification algorithm has become one of the most important developing directions in the field of remote sensing ground object classification. Remote sensing classification is a strictly incompatible problem, but the spectra distribution of remote sensing data has compatible attributes especially in mountainous regions, and such contradiction is one of the main reasons leading to uncertainties in remote sensing classification. In this paper, the remote sensing spectra feature compatible information is transformed into the probability of the association degree firstly, and then the matter-element theory is introduced to establish models to achieve the integrated classification of multi-source data to fuse knowledge-based geographical constrained condition probability. Taking the grassland–wetland fragile ecosystem in Ruoergai plateau of China as a case study, this paper selected the multi-source data including images of Landsat TM and CBERS, ASTER-GDEM and MODIS-NDVI to construct a comprehensive classifier, in which the relationship between topography and land cover, and the prior knowledge on vegetation growth difference were taken as constraints to support the decision-making. The classification accuracy was evaluated by a field investigation and existing land cover map. The test result shows that, the overall accuracy (89.89%) and Kappa coefficient (0.8870) are better than those derived by the Maximum Likelihood method. It indicates that the proposed classification method is not subject to the dimensionality and form of data sources, and it can integrate the data source information to improve the classification accuracy, so that it is very useful to apply multi-source data and prior knowledge to land cover classification in mountainous regions.