A Survey on Spatio-temporal Data Analytics Systems

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

Evaluation of the Risk Induced by Soil Erosion on Land Use. Case Study: Guruslău Depression

Changes in land use, increasing of agricultural areas to the detriment of wooded ones, and poor management of agricultural land, along with the impact of current changes in the climate (reflected in the increase of the climate aggression index) makes soil erosion one of the main risks associated with improper land use, with a direct impact on its productivity and an indirect impact on human beings. The aim of this study is to assess the risk induced by surface soil erosion on land use, using as our main method of investigation the development of two models of integrated spatial analysis of the territory: a derived model of the universal soil loss equation (USLE) and a qualitative model that integrates the result of soil erosion assessment with the database representing the land use. This was carried out in order to highlight the impact on the territory. The spatial analysis models were developed on a structure of vector spatial databases, through which the soil type, soil texture, climate aggression coefficient, and land use were mapped, and alphanumeric databases, representing the market cost of land, in EUROs, that highlight the quality of cultivated land (in terms of productive economic potential). The induced risk estimation is based on a qualitative rating of soil erosion vulnerability on a scale from 1 to 5 (1-low vulnerability; 5-high vulnerability) and of the reduction of the economic value of the land (according to the vulnerability rating). The implemented methodology highlights the quantitative risk, with a maximum value of about 46.000 EUROs, spatially identified on large surfaces on the outskirts of the Jibou municipality. It is mainly caused by the impact of soil erosion on large areas of orchards, which provide necessary products for human consumption. The present methodology can be implemented on similar areas and can be used as a model of good practices in risk assessment based on financial losses by local public authorities.

3D TOPOLOGICAL SUPPORT IN SPATIAL DATABASES: AN OVERVIEW

The storage of spatial data that consists of spatial and non-spatial properties requires a database management system that possesses spatial functions that can cater to the spatial characteristics of data. These characteristics include the geometrical shape, topological and positional information. Parallel to how geometries describe the shape of an object, topological information is also an important spatial property which describes how the geometries in a space are related to each other. This information describes the connectivity, containment and adjacencies of spatial objects which are the foundation for more complex analysis such as navigation, data reconstruction, spatial queries and others. However, the topological support provided by spatial databases varies. This paper provided an overview on the current implementations of topological support in spatial databases such as ArcGIS, QGIS, PostgreSQL and others. The native topology in most spatial databases was found to be 2D topology maintained by 2D topology rules with limited representation of 3D topological relationships. Consequently, 3D objects represented by 2D topology had to be decomposed into objects of lower dimensions. Approaches to implement additional topological support for spatial databases included the use of topological data models, data structures, operators, and rules. 3D applications such as 3D cadastre required more detailed representations of topological information which required a more comprehensive 3D topological data model. Nonetheless, comprehensive preservation of topological information also mandates voluminous storage and higher computational efficiency. Thus, the appropriate 3D topological support should be provided in spatial databases to accurately represent 3D objects and meet 3D analysis requirements.

An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases

From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but nonoverlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With nonoverlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized KMeans with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.

Definition of the Map

Cartography has undergone great changes in the last 40 years. Many web platforms and location-based services are offering increasing opportunities, paper maps have been largely supplemented by multimedia and digital maps, and spatial databases. The definition of a map has changed throughout history and the differences in their definitions are presented. This paper aims for new central cartographic definitions, corresponding to contemporary cartographic development after presenting the current situation of the topic. Definitions of cartographic mapping, cartography and cartographer are proposed, as well as a new definition of the map. All they are made on the base of logical analyses including different types of maps from traditional and real to virtual, 3D, animation, and digital.

Landslide Susceptibility Assessment Using an AutoML Framework

The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides.

Classification of Landscape Physiognomies in Rural Poland: The Case of the Municipality of Cekcyn

<p>This article presents a methodology and the results of the classification of the rural landscapes physiognomies conducted on the study area located in the municipality of Cekcyn, Poland. The study aimed to develop a landscape identification method that would combine natural, cultural, and visual criteria with which to implement the provisions of the European Landscape Convention. The realization of the European Landscape Convention in Poland is incomplete due to the lack of practical application of landscape assessment in land management and spatial planning at the commune level. The research was intended at helping to fill this void. The study develops a method using which it will be possible to protect the diversity and beauty of Europe’s rural landscapes more effectively. The goal has so far been of little scientific interest in Poland. The physiognomy of the studied area was analyzed with the use of commonly available spatial data and by means of field studies. Physical-geographical units and cultural characteristics have been designated based on spatial databases. Landscape patterns were identified by analyzing visual fields with the use of both GIS applications and field studies. This practice made it possible to determine physiognomic units of the landscape which are internally coherent and relatively homogeneous in terms of physical-geographical, cultural, and visual features. Identifying the landscape physiognomy within the designated landscape physiognomic units serves to harmonize spatial alterations in the area of rural communes in processes of land management and planning.</p>

Refinement Proposals for Geodiversity Assessment—A Case Study in the Bakony–Balaton UNESCO Global Geopark, Hungary

Geodiversity is the variety of natural elements that are excluded from biodiversity, such as: geological, geomorphological, and soil features including their properties, systems, and relationships. Geodiversity assessment measures these features, emphasising the characteristics and physical fragility of the examined areas. In this study, a quantitative methodology has been applied in Bakony–Balaton UGGp, Hungary. The Geopark’s area was divided into 2 × 2 km cells in which geodiversity indices were calculated using various data: maps, spatial databases, and elevation models. However, data sources differ significantly in each country: thematic information may not be entirely public or does not have the appropriate scale and complexity. We proposed to use universal data—geomorphons and a watercourse network—derived from Digital Elevation Models (DEMs) to calculate geomorphological diversity. Making a balance between the base materials was also an aim of this research. As sources with different data densities are used, some abiotic elements may be overrepresented, while others seem to have less significance. The normalisation of thematic layers solves this problem: it gives a proportion to each sub-element and creates a balanced index. By applying worldwide accessible digital base data and statistical standardization methods, abiotic nature quantification may open new perspectives in geoconservation.