Search results for: spatial structure

Authors: Reinhold Kosfeld, Andreas Gohs

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In the reduction of CO₂ emissions, the transition to environmentally friendly transport modes has a high significance. In Germany, the climate protection programme 2030 includes various measures for promoting electromobility. Although electric cars at present hold a market share of just over one percent, its stock more than doubled in the past two years. Special measures like tax incentives and a buyer’s premium have been put in place to promote the shift towards electric cars and boost their diffusion. Knowledge of the future expansion of electric cars is required for planning purposes and adaptation measures. With a view of these objectives, we particularly investigate the effect of spatial spillovers on forecasting performance. For this purpose, time series econometrics and panel econometric models are designed for pure electric cars and hybrid cars for Germany. Regional forecasting models with spatial interactions are consistently estimated by using spatial econometric techniques. Regional data on the stocks of electric cars and their determinants at the district level (NUTS 3 regions) are available from the Federal Motor Transport Authority (Kraftfahrt-Bundesamt) for the period 2017 - 2019. A comparative examination of aggregated regional and national predictions provides quantitative information on accuracy gains by allowing for spatial spillovers in forecasting electric mobility.

Keywords: electric mobility, forecasting market diffusion, regional panel data model, spatial interaction

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Authors: Parul Suraia, Harshit Sosan Lakra

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Indigenous communities represent some of the most marginalized populations globally, with India labeled as tribals, experiencing particularly pronounced marginalization and a concerning decline in their numbers. These communities often inhabit geographically challenging regions characterized by low population densities, posing significant challenges to providing essential infrastructure services. Jharkhand, a Schedule 5 state, is infamous for its low-level health status due to disparities in access to health care. The primary objective of this study is to investigate the spatial inequalities in healthcare accessibility among tribal populations within the state and pinpoint critical areas requiring immediate attention. Health indicators were selected based on the tribal perspective and association of Sustainable Goal 3 (Good Health and Wellbeing) with other SDGs. Focused group discussions in which tribal people and tribal experts were done in order to finalize the indicators. Employing Principal Component Analysis, two essential indices were constructed: the Tribal Health Index (THI) and the Tribal Health Intervention Index (THII). Index values were calculated based on the district-wise secondary data for Jharkhand. The bivariate spatial association technique, Moran’s I was used to assess the spatial pattern of the variables to determine if there is any clustering (positive spatial autocorrelation) or dispersion (negative spatial autocorrelation) of values across Jharkhand. The results helped in facilitating targeting policy interventions in deprived areas of Jharkhand.

Keywords: tribal health, health spatial disparities, health status, Jharkhand

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Authors: Aybike Ayfer Karadağ

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Today, the amount of water available is decreasing day by day due to global warming, environmental problems and population increase. To protect water resources, it is necessary to take a lot of measures from the global scale to the local scale. Some of these measures are related to spatial planning studies. In this study, the impact of water process analysis was assessed in the development of spatial planning for water conservation. The study was conducted in the Kovada sub-watershed (Isparta, Turkey). By means of water process analysis, the way to reach underground water of surface water in the study area is mapped. In this context, plant cover, soil and rock permeability were evaluated holistically with geographic information systems technologies. Then, on the map, water permeability is classified and this is spatially expressed. The findings show that the permeability of the water is different in the study case. As a result, the water permeability map needs to be included in the planning for water conservation planning.

Keywords: water, conservation, spatial planning, water process analysis

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Authors: Panagiotis Roupas, Yota Passia

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This paper aims at establishing an index of design mechanisms - immanent in spatial objects - based on the affective capacity of their material formations. While spatial objects (design objects, buildings, urban configurations, etc.) are regarded as systems composed of interacting parts, within the premises of assemblage theory, their ability to affect and to be affected has not yet been mapped or sufficiently explored. This ability lies in excess, a latent potentiality they contain, not transcendental but immanent in their pre-subjective aesthetic power. As spatial structures are theorized as assemblages - composed of heterogeneous elements that enter into relations with one another - and since all assemblages are parts of larger assemblages, their components' ability to engage is contingent. We thus seek to unfold the mechanisms inherent in spatial objects that allow to the constituent parts of design assemblages to perpetually enter into new assemblages. To map architectural assemblage's affective ability, spatial objects are analyzed in two axes. The first axis focuses on the relations that the assemblage's material and expressive components develop in order to enter the assemblages. Material components refer to those material elements that an assemblage requires in order to exist, while expressive components includes non-linguistic (sense impressions) as well as linguistic (beliefs). The second axis records the processes known as a-signifying signs or a-signs, which are the triggering mechanisms able to territorialize or deterritorialize, stabilize or destabilize the assemblage and thus allow it to assemble anew. As a-signs cannot be isolated from matter, we point to their resulting effects, which without entering the linguistic level they are expressed in terms of intensity fields: modulations, movements, speeds, rhythms, spasms, etc. They belong to a molecular level where they operate in the pre-subjective world of perceptions, effects, drives, and emotions. A-signs have been introduced as intensities that transform the object beyond meaning, beyond fixed or known cognitive procedures. To that end, from an archive of more than 100 spatial objects by contemporary architects and designers, we have created an effective mechanisms index is created, where each a-sign is now connected with the list of effects it triggers and which thoroughly defines it. And vice versa, the same effect can be triggered by different a-signs, allowing the design object to lie in a perpetual state of becoming. To define spatial objects, A-signs are categorized in terms of their aesthetic power to affect and to be affected on the basis of the general categories of form, structure and surface. Thus, different part's degree of contingency are evaluated and measured and finally, we introduce as material information that is immanent in the spatial object while at the same time they confer no meaning; they only convey some information without semantic content. Through this index, we are able to analyze and direct the final form of the spatial object while at the same time establishing the mechanism to measure its continuous transformation.

Keywords: affective mechanisms index, architectural assemblages, a-signifying signs, cartography, virtual

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Tomasz Wejrzanowski, Łukasz Kaczmarek, Michał Maksimczuk

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High-resolution X-ray computed microtomography (µCT) is a non-destructive technique commonly used to determine the internal structure of reservoir rock sample. This study concerns µCT analysis of Silurian and Ordovician shales and mudstones from a borehole in the Baltic Basin, north of Poland. The spatial resolution of the µCT images obtained was 27 µm, which enabled the authors to create accurate 3-D visualizations and to calculate the ratio of pores and fractures volume to the total sample volume. A total of 1024 µCT slices were used to create a 3-D volume of sample structure geometry. These µCT slices were processed to obtain a clearly visible image and the volume ratio. A copper X-ray source filter was used to reduce image artifacts. Due to accurate technical settings of µCT it was possible to obtain high-resolution 3-D µCT images of low X-ray transparency samples. The presented results confirm the utility of µCT implementations in geoscience and show that µCT has still promising applications for reservoir exploration and characterization.

Keywords: fractures, material density, pores, structure

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Authors: Sofia Barbosa, Mariana Pinto, José António Almeida, Edgar Carvalho, Catarina Diamantino

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The aim of this work was to test a methodology able to generate spatial-temporal maps that can synthesize simultaneously the trends of distinct hydrochemical indicators in an old radium-uranium tailings dam deposit. Multidimensionality reduction derived from principal component analysis and subsequent data aggregation derived from clustering analysis allow to identify distinct hydrochemical behavioural profiles and to generate synthetic evolutionary hydrochemical maps.

Keywords: Contamination plume migration, K-means of PCA scores, groundwater and mine water monitoring, spatial-temporal hydrochemical trends

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Authors: Sofia M. Karadimitriou, Kostas Triantafyllopoulos, Timothy Heaton

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Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data.

Keywords: multidimensional Laplace prior, particle filtering, spatio-temporal modelling, wavelets

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Authors: Moustafa Osman Mohammed

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The photocurrent generations are influencing ultra-high efficiency solar cells based on self-assembled quantum dot (QD) nanostructures. Nanocrystal quantum dots (QD) provide a great enhancement toward solar cell efficiencies through the use of quantum confinement to tune absorbance across the solar spectrum enabled multi-exciton generation. Based on theoretical predictions, QDs have potential to improve systems efficiency in approximate regular electrons excitation intensity greater than 50%. In solar cell devices, an intermediate band formed by the electron levels in quantum dot systems. The spatial architecture is exploring how can solar cell integrate and produce not only high open circuit voltage (> 1.7 eV) but also large short-circuit currents due to the efficient absorption of sub-bandgap photons. In the proposed QD system, the structure allows barrier material to absorb wavelengths below 700 nm while multi-photon processes in the used quantum dots to absorb wavelengths up to 2 µm. The assembly of the electronic model is flexible to demonstrate the atoms and molecules structure and material properties to tune control energy bandgap of the barrier quantum dot to their respective optimum values. In terms of energy virtual conversion, the efficiency and cost of the electronic structure are unified outperform a pair of multi-junction solar cell that obtained in the rigorous test to quantify the errors. The milestone toward achieving the claimed high-efficiency solar cell device is controlling the edge causes of energy bandgap between the barrier material and quantum dot systems according to the media design limits. Despite this remarkable potential for high photocurrent generation, the achievable open-circuit voltage (Voc) is fundamentally limited due to non-radiative recombination processes in QD solar cells. The orientation of voltage recovery system is compared theoretically with experimental Voc variation in mediation upper–limit obtained one diode modeling form at the cells with different bandgap (Eg) as classified in the proposed spatial architecture. The opportunity for improvement Voc is valued approximately greater than 1V by using smaller QDs through QD solar cell recovery systems as confined to other micro and nano operations states.

Keywords: nanotechnology, photovoltaic solar cell, quantum systems, renewable energy, environmental modeling

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Authors: Zhang Yuqi

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Service facilities are the boosters for the cultivation and development of innovative functions in innovative cluster areas. At the same time, reasonable service facilities planning can better link the internal functional blocks. This paper takes Zhejiang University Zijin Co-Maker Town as the research object, based on the combination of network data mining and field research and verification, combined with the needs of its internal innovative groups. It studies the distribution characteristics and existing problems of service facilities and then proposes a targeted planning suggestion. The main conclusions are as follows: (1) From the perspective of view, the town is rich in general life-supporting services, but lacking of provision targeted and distinctive service facilities for innovative groups; (2) From the perspective of scale structure, small-scale street shops are the main business form, lack of large-scale service center; (3) From the perspective of spatial structure, service facilities layout of each functional block is too fragile to fit the characteristics of 2aggregation- distribution' of innovation and entrepreneurial activities; (4) The goal of optimizing service facilities planning should be guided for fostering function of innovation and entrepreneurship and meet the actual needs of the innovation and entrepreneurial groups.

Keywords: the cultivation of innovative function, Zhejiang University Zijin Co-Maker Town, service facilities, network data mining, space optimization advice

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Authors: Karitikeya Sonker

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Religion as a discrete system through its lawful internal working produces an output in the form of realised spatial order with its social logic and a social order with its spatial logic. Thus, it appears to exhibit its duality of spatial and trans-spatial. The components of this system share a relevance forming a collective. This shared relevance creates meaning forming a group where all collectives share one identity. This group with its new social order and its spatial logic revive the already existing spatial order. These religious groups do so having a tendency to expand resulting in the production of space in a situation of encounter where they have found relevance. But an encounter without a lawful internal working of a discrete system results in anomaly because groups do not find relevance due to the absence of collective identity. Events happen all around. One of the main reasons we could say that something became an event is because of conflict. Conflict not in its definitive sense but any occurrence that happens because of an intervention that creates an event worth remembering. The unfolding of such events creates Cities and Urban spaces which exhibit their duality of spatial and trans-spatial by behaving as a discrete system. This system through its lawful internal working produces an output in the form of realized spatial order with its social logic and a social order with spatial logic. The components of this system form a collective through a shared a relevance. This shared relevance creates meaning forming a group where all collectives share one identity. This group with its new social order and its spatial logic revives the already existing spatial order. These groups do so having a tendency to expand resulting in the production of space in a situation of encounter where they have found relevance. But an encounter without a lawful internal working of the discrete system results in anomaly because groups do not find relevance due to the absence of collective identity. This paper makes an effort to explore one such even in the case of Babri Mosque and Ramjanmabhumi, Ayodhya to explain the anomaly as transposition of social and spatial. The paper through the case studies makes an attempt to generate an equation explaining the two different situations of religious encounters, former reviving the social and spatial order and the other resulting in anomaly. Through the case study, it makes an attempt to generate an equation explaining the two different situations of religious encounters, former reviving the social and spatial order and the other resulting in anomaly.

Keywords: Babri Masjid, Ayodhya, conflict, religion

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Authors: Z. Bouattou, R. Laurini, H. Belbachir

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This paper describes a new approach for the automatic generation of the visual summaries dealing with cartographic visualization methods and sensors real time data modeling. Hence, the concept of chorems seems an interesting candidate to visualize real time geographic database summaries. Chorems have been defined by Roger Brunet (1980) as schematized visual representations of territories. However, the time information is not yet handled in existing chorematic map approaches, issue has been discussed in this paper. Our approach is based on spatial analysis by interpolating the values recorded at the same time, by sensors available, so we have a number of distributed observations on study areas and used spatial interpolation methods to find the concentration fields, from these fields and by using some spatial data mining procedures on the fly, it is possible to extract important patterns as geographic rules. Then, those patterns are visualized as chorems.

Keywords: geovisualization, spatial analytics, real-time, geographic data streams, sensors, chorems

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Authors: Jae Moon Im, Kwang Bok Shin, Sang Woo Lee

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In this paper, evaluation of structural integrity for composite lattice structure was conducted by compressive test. Composite lattice structure was manufactured by carbon fiber using filament winding method. In order to evaluate the structural integrity of composite lattice structure, compressive test was done using anti-buckling fixture. The delamination occurred 84 Tons of compressive load. It was found that composite lattice structure satisfied the design requirements.

Keywords: composite material, compressive test, lattice structure, structural integrity

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Authors: Praveena Nair Sivasankaran, Thomas Arthur Ward, Rubentheren Viyapuri

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Paper describes a methodology to fabricate a simplified dragonfly wing structure using balsa wood and red prepreg fibre glass. These simplified wing structures were created for use in Biomimetic Micro Air Vehicles (BMAV). Dragonfly wings are highly corrugated and possess complex vein structures. In order to mimic the wings function and retain its properties, a simplified version of the wing was designed. The simplified dragonfly wing structure was created using a method called spatial network analysis which utilizes Canny edge detection method. The vein structure of the wings were carved out in balsa wood and red prepreg fibre glass. Balsa wood and red prepreg fibre glass was chosen due to its ultra- lightweight property and hence, highly suitable to be used in our application. The fabricated structure was then immersed in a nanocomposite solution containing chitosan as a film matrix, reinforced with chitin nanowhiskers and tannic acid as a crosslinking agent. These materials closely mimic the membrane of a dragonfly wing. Finally, the wings were subjected to a bending test and comparisons were made with previous research for verification. The results had a margin of difference of about 3% and thus the structure was validated.

Keywords: dragonfly wings, simplified, Canny edge detection, balsa wood, red prepreg, chitin, chitosan, tannic acid

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Authors: Sara Ramezanzadeh, Nashid Nabian

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Historically, pandemics in different periods have caused compulsory changes in human life. In the case of Covid-19, according to the limitations and established care instructions, spatial alignment with the conditions is important. Following the outbreak of Covid-19, the question raised in this study is how to do spatial design in five scales, namely object, space, architecture, city, and infrastructure, in response to the consequences created in the realms under study. From the beginning of the pandemic until now, some changes in the spatial realm have been created spontaneously or by space users. These transformations have been mostly applied in modifiable parts such as furniture arrangement, especially in work-related spaces. To implement other comprehensive requirements, flexibility and adaptation of space design to the conditions resulting from the pandemics are needed during and after the outbreak. Studying the effects of pandemics from the past to the present, this research covers eight major realms, including three categories of ramifications, solutions, and paradigm shifts, and analytical conclusions about the solutions that have been created in response to them. Finally, by the consideration of epidemiology as a modern discipline influencing the design, spatial solutions in the five scales mentioned (in response to the effects of the eight realms for spatial adaptation in the face of pandemics and their following conditions) are presented as a series of guidelines. Due to the unpredictability of possible pandemics in the future, the possibility of changing and updating the provided guidelines is considered.

Keywords: pandemics, Covid 19, spatial design, ramifications, solutions, paradigm shifts, guidelines

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Authors: Panudet Saengseedam, Nanthachai Kantanantha

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This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.

Keywords: Bayesian method, linear mixed model, multivariate conditional autoregressive model, spatial time series

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Authors: Guilan Huang

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In 2013 and 2014, the U.S. Food and Drug Administration (FDA) collected data from selected fast food restaurants and full service restaurants for tracking changes in the occurrence of foodborne illness risk factors. This paper discussed how we customized spatial random sampling method by considering financial position and availability of FDA resources, and how we enriched restaurants data with location. Location information of restaurants provides opportunity for quantitatively determining random sampling within non-government units (e.g.: 240 kilometers around each data-collector). Spatial analysis also could optimize data-collectors’ work plans and resource allocation. Spatial analytic and processing platform helped us handling the spatial random sampling challenges. Our method fits in FDA’s ability to pinpoint features of foodservice establishments, and reduced both time and expense on data collection.

Keywords: geospatial technology, restaurant, retail food risk factor study, spatially random sampling

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Authors: Chengming Li, Yong Yin, Peipei Guo, Xiaoli Liu

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Without taking account of the attribute richness of POI (point of interest) data and spatial distribution limited by roads, a POI generalization method considering both attribute information and spatial distribution has been proposed against the existing point generalization algorithm merely focusing on overall information of point groups. Hierarchical characteristic of urban POI information expression has been firstly analyzed to point out the measurement feature of the corresponding hierarchy. On this basis, an urban POI generalizing strategy has been put forward: POIs urban road network have been divided into three distribution pattern; corresponding generalization methods have been proposed according to the characteristic of POI data in different distribution patterns. Experimental results showed that the method taking into account both attribute information and spatial distribution characteristics of POI can better implement urban POI generalization in the mapping presentation.

Keywords: POI, road network, selection method, spatial information expression, distribution pattern

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Authors: Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

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In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.

Keywords: binary vector quantization (BVQ), DCT coefficients, face recognition, local binary patterns (LBP)

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Authors: S. K. Appiah, E. N. Aidoo, D. Asamoah Owusu, M. W. Nuonabuor

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Urbanization remains one of the unique predominant factors which is linked to the destruction of urban environment and its associated cases of soil contamination by heavy metals through the natural and anthropogenic activities. These activities are important sources of toxic heavy metals such as arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), and lead (Pb), nickel (Ni) and zinc (Zn). Often, these heavy metals lead to increased levels in some areas due to the impact of atmospheric deposition caused by their proximity to industrial plants or the indiscriminately burning of substances. Information gathered on potentially hazardous levels of these heavy metals in soils leads to establish serious health and urban agriculture implications. However, characterization of spatial variations of soil contamination by heavy metals in Ghana is limited. Kumasi is a Metropolitan city in Ghana, West Africa and is challenged with the recent spate of deteriorating soil quality due to rapid economic development and other human activities such as “Galamsey”, illegal mining operations within the metropolis. The paper seeks to use both univariate and multivariate geostatistical techniques to assess the spatial distribution of heavy metals in soils and the potential risk associated with ingestion of sources of soil contamination in the Metropolis. Geostatistical tools have the ability to detect changes in correlation structure and how a good knowledge of the study area can help to explain the different scales of variation detected. To achieve this task, point referenced data on heavy metals measured from topsoil samples in a previous study, were collected at various locations. Linear models of regionalisation and coregionalisation were fitted to all experimental semivariograms to describe the spatial dependence between the topsoil heavy metals at different spatial scales, which led to ordinary kriging and cokriging at unsampled locations and production of risk maps of soil contamination by these heavy metals. Results obtained from both the univariate and multivariate semivariogram models showed strong spatial dependence with range of autocorrelations ranging from 100 to 300 meters. The risk maps produced show strong spatial heterogeneity for almost all the soil heavy metals with extremely risk of contamination found close to areas with commercial and industrial activities. Hence, ongoing pollution interventions should be geared towards these highly risk areas for efficient management of soil contamination to avert further pollution in the metropolis.

Keywords: coregionalization, heavy metals, multivariate geostatistical analysis, soil contamination, spatial distribution

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Authors: Taisiia Gorshkova

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Since the 1980s, spatial correlation models have been used more often to model regional indicators. An increasingly popular method for studying regional indicators is modeling taking into account spatial relationships between objects that are part of the same economic zone. In 2000s the new class of model – spatial vector autoregressions was developed. The main difference between standard and spatial vector autoregressions is that in the spatial VAR (SpVAR), the values of indicators at time t may depend on the values of explanatory variables at the same time t in neighboring regions and on the values of explanatory variables at time t-k in neighboring regions. Thus, VAR is a special case of SpVAR in the absence of spatial lags, and the spatial panel data model is a special case of spatial VAR in the absence of time lags. Two specifications of SpVAR were applied to Russian regional data for 2000-2017. The values of GRP and regional CPI are used as endogenous variables. The lags of GRP, CPI and the unemployment rate were used as explanatory variables. For comparison purposes, the standard VAR without spatial correlation was used as “naïve” model. In the first specification of SpVAR the unemployment rate and the values of depending variables, GRP and CPI, in neighboring regions at the same moment of time t were included in equations for GRP and CPI respectively. To account for the values of indicators in neighboring regions, the adjacency weight matrix is used, in which regions with a common sea or land border are assigned a value of 1, and the rest - 0. In the second specification the values of depending variables in neighboring regions at the moment of time t were replaced by these values in the previous time moment t-1. According to the results obtained, when inflation and GRP of neighbors are added into the model both inflation and GRP are significantly affected by their previous values, and inflation is also positively affected by an increase in unemployment in the previous period and negatively affected by an increase in GRP in the previous period, which corresponds to economic theory. GRP is not affected by either the inflation lag or the unemployment lag. When the model takes into account lagged values of GRP and inflation in neighboring regions, the results of inflation modeling are practically unchanged: all indicators except the unemployment lag are significant at a 5% significance level. For GRP, in turn, GRP lags in neighboring regions also become significant at a 5% significance level. For both spatial and “naïve” VARs the RMSE were calculated. The minimum RMSE are obtained via SpVAR with lagged explanatory variables. Thus, according to the results of the study, it can be concluded that SpVARs can accurately model both the actual values of macro indicators (particularly CPI and GRP) and the general situation in the regions

Keywords: forecasting, regional data, spatial econometrics, vector autoregression

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Authors: Francky Fouedjio

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Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical clustering methods.

Keywords: clustering, geostatistics, multivariate data, non-parametric

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Authors: Sana Hamdi, Emna Bouazizi, Sami Faiz

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In recent years, real-time spatial applications, like location-aware services and traffic monitoring, have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of our computing infrastructure. For instance, in real-time spatial Big Data, users expect to receive the results of each query within a short time period without holding in account the load of the system. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly especially in overload situations. To solve this problem, we propose the use of data partitioning as an optimization technique. Traditional horizontal and vertical partitioning can increase the performance of the system and simplify data management. But they remain insufficient for real-time spatial Big data; they can’t deal with real-time and stream queries efficiently. Thus, in this paper, we propose a novel data partitioning approach for real-time spatial Big data named VPA-RTSBD (Vertical Partitioning Approach for Real-Time Spatial Big data). This contribution is an implementation of the Matching algorithm for traditional vertical partitioning. We find, firstly, the optimal attribute sequence by the use of Matching algorithm. Then, we propose a new cost model used for database partitioning, for keeping the data amount of each partition more balanced limit and for providing a parallel execution guarantees for the most frequent queries. VPA-RTSBD aims to obtain a real-time partitioning scheme and deals with stream data. It improves the performance of query execution by maximizing the degree of parallel execution. This affects QoS (Quality Of Service) improvement in real-time spatial Big Data especially with a huge volume of stream data. The performance of our contribution is evaluated via simulation experiments. The results show that the proposed algorithm is both efficient and scalable, and that it outperforms comparable algorithms.

Keywords: real-time spatial big data, quality of service, vertical partitioning, horizontal partitioning, matching algorithm, hamming distance, stream query

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Authors: Krisada Lekdee, Sunee Sammatat, Nittaya Boonsit

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This paper proposes a GLMM with spatial and temporal effects for malaria data in Thailand. A Bayesian method is used for parameter estimation via Gibbs sampling MCMC. A conditional autoregressive (CAR) model is assumed to present the spatial effects. The temporal correlation is presented through the covariance matrix of the random effects. The malaria quarterly data have been extracted from the Bureau of Epidemiology, Ministry of Public Health of Thailand. The factors considered are rainfall and temperature. The result shows that rainfall and temperature are positively related to the malaria morbidity rate. The posterior means of the estimated morbidity rates are used to construct the malaria maps. The top 5 highest morbidity rates (per 100,000 population) are in Trat (Q3, 111.70), Chiang Mai (Q3, 104.70), Narathiwat (Q4, 97.69), Chiang Mai (Q2, 88.51), and Chanthaburi (Q3, 86.82). According to the DIC criterion, the proposed model has a better performance than the GLMM with spatial effects but without temporal terms.

Keywords: Bayesian method, generalized linear mixed model (GLMM), malaria, spatial effects, temporal correlation

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Authors: Jong-Hwa Kim, Mu-Wook Pyeon, Yang-dam Eo, Ill-Woong Jang

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Construction of geo-spatial information recently tends to develop as multi-dimensional geo-spatial information. People constructing spatial information is also expanding its area to the general public from some experts. As well as, studies are in progress using a variety of devices, with the aim of near real-time update. In this paper, getting the stereo images using GoPro device used widely also to the general public as well as experts. And correcting the distortion of the images, then by using SIFT, DLT, is acquired the point clouds. It presented a possibility that on the basis of this experiment, using a video device that is readily available in real life, to create a real-time digital map.

Keywords: GoPro, SIFT, DLT, point clouds

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Authors: Bilal Saoud

Abstract:

In this paper, we propose a new method to find the community structure in networks. Our method is based on bee colony and the maximization of modularity to find the community structure. We use a bee colony algorithm to find the first community structure that has a good value of modularity. To improve the community structure, that was found, we merge communities until we get a community structure that has a high value of modularity. We provide a general framework for implementing our approach. We tested our method on computer-generated and real-world networks with a comparison to very known community detection methods. The obtained results show the effectiveness of our proposition.

Keywords: bee colony, networks, modularity, normalized mutual information

Procedia PDF Downloads 378

Authors: Mehrnaz Alibeikloo, Hadi Khabbaz, Behzad Fatahi

Abstract:

The spectral random field method is one of the widely used methods to obtain more reliable and accurate results in geotechnical problems involving material variability. Karhunen-Loeve (K-L) expansion method was applied to perform random field discretization of cross-correlated creep parameters. Karhunen-Loeve expansion method is based on eigenfunctions and eigenvalues of covariance function adopting Kernel integral solution. In this paper, the accuracy of Karhunen-Loeve expansion was investigated to predict long-term settlement of soft soils adopting elastic visco-plastic creep model. For this purpose, a parametric study was carried to evaluate the effect of K-L expansion terms and spatial correlation length on the reliability of results. The results indicate that small values of spatial correlation length require more K-L expansion terms. Moreover, by increasing spatial correlation length, the coefficient of variation (COV) of creep settlement increases, confirming more conservative and safer prediction.

Keywords: Karhunen-Loeve expansion, long-term settlement, reliability analysis, spatial correlation length

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Authors: Abdul Hakim Khan

Abstract:

The aim of the present paper is to study the Banach Space and Combinatorial Algebraic Structure of R. It is further aimed to study algebraic structure of set of all q-extension of classical formula and function for 0 < q < 1.

Keywords: integral functions, q-extensions, q numbers of metric space, algebraic structure of r and banach space

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Authors: Rahaf Suleiman Altallaa

Abstract:

City planning involves making plans, organizing and dealing with the cities urban areas. It attempts to organize socio-spatial relationships at exceptional ranges of governance Urban planning offers the social, monetary and environmental effects of defining spatial obstacles and the influence on the spatial distribution of resources. The dreams and methods of reaching such dissemination vary extensively traditionally and geographically and are often challenged through traditional strategies that expose the political nature of application interventions and the bounds of technical know-how claims. Space, network, argument, and postcolonial debates address how present-day socio-spatial organization is formed, what needs to or should not trade, and the way it underscores whether or not a good plan will contribute to a given situation. Inside the absence of an agreed-upon technical justification for the planning exercise, the planning idea has a tendency to focus on normative processes, positioning making plans as an area for participatory democracy.

Keywords: environmental governance, environmental planning, environmental management, sustainable competitiveness, sustainability

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Authors: Somkait Udomhunsakul

Abstract:

In this paper, a multi-focus image fusion method using Spatial Frequency Measurements (SFM) and Wavelet Packet was proposed. The proposed fusion approach, firstly, the two fused images were transformed and decomposed into sixteen subbands using Wavelet packet. Next, each subband was partitioned into sub-blocks and each block was identified the clearer regions by using the Spatial Frequency Measurement (SFM). Finally, the recovered fused image was reconstructed by performing the Inverse Wavelet Transform. From the experimental results, it was found that the proposed method outperformed the traditional SFM based methods in terms of objective and subjective assessments.

Keywords: multi-focus image fusion, wavelet packet, spatial frequency measurement

Procedia PDF Downloads 454