Spatial Databases

The paper aims to reveal one integrated global map which points out the major geographical inequalities in providing basic utilities across the countries using multivariate analysis and thematic cartography. Sixteen indicators with global coverage were selected taking into account the waste collection services, sanitation facilities, drinking water sources, energy, electricity, habitat and demographic conditions. Several data are broken down for the total, urban and rural population in order to outline the rural-urban disparities between and within countries. A special focus is given to waste collection coverage, in order to compute a comprehensive global assessment of this key indicator of public health, which is one of the poorest monitored basic utility. The world countries were divided into 10 classes according to the hierarchical cluster analysis. Each class has particular features outlining the gaps between high, middle and low-income countries with direct impact on quality of life, public health, and environment.

Cite this article: Raszkowski, A., Bartniczak, B. Towards Sustainable Regional Development: Economy, Society, Environment, Good Governance Based on the Example of Polish Regions. Transformations in Business & Economics, 2018, Vol. 17, No 2 (44), pp.225-245.The presented study discusses problems referring to the concept of sustainable development at regional level, based on the example of Polish regions in the period 2005-2011. The first part presents theoretical aspects of sustainable regional development, raises the problems of primary phenomena hindering such development, sustainable development definitions, the role of creativity, the importance of NGOs, good governance of regional space. The next part of the article provides the characteristics of sustainable development indicators selected for the analysis in accordance with the approach adopted by the Central Statistical Office in Poland towards measuring the level of sustainable regional development. The synthetic measure of development (SMD) represents the applied research method, which offered the basis for constructing the ranking and identifying the position of particular Polish regions. The research results remain the core of the study since they illustrate the indicator values in the years under analysis. Within the framework of the final remarks and conclusions it was emphasized that in none of the studied regions, in terms of implementing sustainable development standards, the situation can be assessed as favourable or very favourable and the reasons for such state of the matter were provided. In spite of the relatively unfavourable results, a gradual improvement of the situation was observed over the years. It was pointed out that one of the fundamental barriers to the implementation of sustainable development standards is the absence of awareness and understanding of the discussed development concept.

Di era yang semakin berkembang, banyak bidang ilmu seperti ekonomi, sosial, lingkungan, kesehatan, meteorologi, klimatologi, geologi dan sebagainya yang menggunakan data yang berkaitan dengan lokasi atau letak gepgrafis suatu tempat. Data yang memuat informasi mengenai lokasi atau letak geografis suatu daerah dan diperoleh dari hasil pengukuran sering disebut data spasial. Buku ini membahas lengkap mengenai metode statistika spasial dan penerapan dalam permasalahan. Bab pertama membahas definisi statistika spasial, Bab dua sampai dengan empat membahas Geographically Weighted Regression (GWR), Geograpichally Weighted Logistic Regression (GWLR), Geograpichally Weighted Logistic Regression Semiparametric (GWLRS), Geographically Weighted Poisson Regression (GWPR) dan bab lima membahas tentang applikasi OpenGeoDa ArcView GIS. Pada buku ini diberikan sejumlah panduan dalam menganalisis dan intepretasi dari metode tersebut khususnya pengoperasian dengan menggunakan software R, ArcView dan OpenGeoDa. R merupakan Bahasa pemrograman untuk komputasi statistik dan grafis.

The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we generalize this algorithm in two important directions. The generalized algorithm-called GDBSCAN-can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes. In addition, four applications using 2D points (astronomy), 3D points (biology), 5D points (earth science) and 2D polygons (geography) are presented, demonstrating the applicability of GDBSCAN to real-world problems.

The purpose of this study is to impute missing monthly maximum temperature data from the department of Valle del Cauca, located in Colombia, over a period of two years (2013 y 2014). For this, a geostatistical technique will be used, which uses spatial data, which in this case are temperature records of meteorological stations located in different areas of the department and at different heights. This technique is a linear model based on the relationship between temperature and height, taking into account that spatially the stations are correlated. A model was constructed per month, and the best spatial correlation structure was added to the residuals of each model. The prediction level of the linear models with spatial correlation was compared with that of other interpolation methods (linear models without correlation and the inverse distance weighted IDW), using mean squared error (MSE) estimated by cross-validation. Linear models with spatial correlation showed the best results when there was a strong spatial correlation structure.

The amount of data stored in IoT databases increases as the IoT applications extend throughout smart city appliances, industry and agriculture. Contemporary database systems must process huge amounts of sensory and actuator data in real-time or interactively. Facing this first wave of IoT revolution, database vendors struggle day-by-day in order to gain more market share, develop new capabilities and attempt to overcome the disadvantages of previous releases, while providing features for the IoT. There are two popular database types: The Relational Database Management Systems and NoSQL databases, with NoSQL gaining ground on IoT data storage. In the context of this paper these two types are examined. Focusing on open source databases, the authors experiment on IoT data sets and pose an answer to the question which one performs better than the other. It is a comparative study on the performance of the commonly market used open source databases, presenting results for the NoSQL MongoDB database and SQL databases of MySQL and PostgreSQL

Data exploration and visualization systems are of great importance in the Big Data era. Exploring and visualizing very large datasets has become a major research challenge, of which scalability is a vital requirement. In this survey, we describe the major prerequisites and challenges that should be addressed by the modern exploration and visualization systems. Considering these challenges, we present how state-of-the-art approaches from the Database and Information Visualization communities attempt to handle them. Finally , we survey the systems developed by Semantic Web community in the context of the Web of Linked Data, and discuss to which extent these satisfy the contemporary requirements.

MobTrack:24 is a location based service that belongs to LBS applications for the tracking and management of fleet of vehicles. The design and the development of the first system version were done in 2008 and 2009. The system has been in commercial use since august 2008. During the last few years it has been established as one of the few leading services of this type in Serbia. The development of this system is a typical example of using the latest concepts and the best practices in the software development for this type of applications. Paper contains detailed description of the system and experiences gained during its development. Major problems and solutions are identified and described. Multi-tier architecture of the system is presented, as well as all the major software components and LBS components used. Software platform used for the development is described in detail with special attention paid to the components providing GIS functionalities of the system. Web services enabling system interoperability and integration with business and other information systems are covered too. Experiences gained during the system implementation and maintenance are also given in the paper.

Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and
heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems
operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing
with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the
availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints
for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly
scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for
information abstraction and summarization. Further, they must take into account different user-defined exploration scenarios
and user preferences. In this work, we present a generic model for personalized multilevel exploration and analysis over large
dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently
constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale
model. We define two versions of this structure, that adopt different data organization approaches, well-suited to exploration and
analysis context. In the proposed structure, statistical computations can be efficiently performed on-the-fly. Considering different
exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction
and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough
theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based
prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets. Finally, we
provide a performance evaluation and a empirical user study employing real datasets.

Improving the use of vector data in web mapping is often shown as an important challenge. Such shift from raster to vector web maps would open web mapping and GIS to new innovations and new practices. The main obstacle is a performance issue: Vector web maps in nowadays web mapping environments are usually too slow and not usable. Existing techniques for vector web mapping cannot solve alone the performance issue. This article describes a unified framework where some of these techniques are integrated in order to build efficient vector web mapping clients and servers. This framework is composed of the following elements: Specific formats for vector data and symbology, vector tiling, spatial index services, and generalization for multiscale data. A prototype based on this framework has been implemented and has shown satisfying results. Some principles for future standards to support the development of vector web mapping are given.

Earth’s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities (e.g., cropland, pastureland, urban land, wood harvest). Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover (resulting from both indirect anthropogenic and natural causes), heterogeneity in land-use/cover (LUC) conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5° 0.5° resolution and describe the area of 28 land- cover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeo- physics, biogeochemistry, water cycle, and global climate.

The data can be downloaded from: http://www.atmos.illinois.edu/~meiyapp2/datasets.htm

This paper presents a proposal to create a graph representation for GIS, using both spatial and non-spatial data and also including spatial relations between spatial objects. Because graphs are a powerful and flexible knowledge representation we will be able to combine spatial and non-spatial data at the same time and this is one of the strengths of the proposal. We hope to apply this knowledge representation to the data mining process with GIS data including three types of spatial relations: topological, orientation and distance.

The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we generalize this algorithm in two important directions. The generalized algorithm-called GDBSCAN-can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes. In addition, four applications using 2D points (astronomy), 3D points (biology), 5D points (earth science) and 2D polygons (geography) are presented, demonstrating the applicability of GDBSCAN to real-world problems.

Internet maps could be improved with the use automated mapping methods, especially automated generalisation techniques. This article analyses why automated generalisation is not used in web mapping and presents a some solutions to plug generalisation on the web.

Organizations adopt different databases for big data which is huge in volume and have different data models. Querying big data is challenging yet crucial for any business. The data warehouses traditionally built with On-line Transaction Processing (OLTP) centric technologies must be modernized to scale to the ever-growing demand of data. With rapid change in requirements it is important to have near real time response from the big data gathered so that business decisions needed to address new challenges can be made in a timely manner. The main focus of our research is to improve the performance of query execution for big data.

Las bases de datos espaciales (spatial database) se utilizan para almacenar datos espaciales, o en otras palabras, los datos relacionados con los espacios en el mundo físico.
En este tipo de bases de datos es imprescindible establecer un cuadro de referencia (un SRE, Sistema de Referencia Espacial) para definir la localización y relación entre objetos, ya que los datos tratados en este tipo de bases de datos tienen un valor relativo. Los sistemas de referencia espacial pueden ser de dos tipos: georreferenciados y no georreferenciados.
La construcción de una base de datos geográfica implica un proceso de abstracción para pasar de la complejidad del mundo real a una representación simplificada. La estructuración de la información espacial procedente del mundo real en capas conlleva cierto nivel de dificultad, de tal forma que toda la complejidad de la realidad ha de ser reducida a puntos, líneas o polígonos.

We present a novel platform for the interactive visualization of very large graphs. The platform enables the user to interact with the visualized graph in a way that is very similar to the exploration of maps at multiple levels. Our approach involves an offline preprocessing phase that builds the layout of the graph by assigning coordinates to its nodes with respect to a Euclidean plane. The respective points are indexed with a spatial data structure, i.e., an R-tree, and stored in a database. Multiple abstraction layers of the graph based on various criteria are also created offline, and they are indexed similarly so that the user can explore the dataset at different levels of granularity, depending on her particular needs. Then, our system translates user operations into simple and very efficient spatial operations (i.e., window queries) in the backend. This technique allows for a fine-grained access to very large graphs with extremely low latency and memory requirements and without compromising the functionality of the tool. Our web-based prototype supports three main operations: (1) interactive navigation, (2) multi-level exploration, and (3) keyword search on the graph metadata.

Skyline queries return the set of non-dominated tuples, where a tuple is dominated if there exists another with better values on all attributes. In the past few years the problem has been studied extensively, and a great number of external memory algorithms have been proposed. We thoroughly study the most important scan-based methods, which perform a number of passes over the database in order to extract the skyline. Although these algorithms are specifically designed to operate in external memory, there are many implementation details which are neglected, as well as several design choices resulting in different favors for these basic methods. We perform an extensive experimental evaluation using real and synthetic data. We conclude that specific design choices can have a significant impact on performance. We also demonstrate that, contrary to common belief, simpler skyline algorithm can be much faster than methods based on pre-processing

Spatial data is inherently uncertain. The main sources of uncertainty include uncertainty in the collection, processing and representation of data (e.g., missing or ambiguous data, measurement uncertainties, raster-to-vector conversion, indeterminate borders of geographical objects, ambiguities in object identification, uncertainty in the interpretation and digitization of data, image classifications, interpolation of values and determination of attributes, etc.). In many situations it is necessary to make important decisions based on an uncertain data analysis. The functions and operations which are performed with the data in information systems are mostly implemented by making use of crisp rules and criteria based on Boolean logic, which often leads to a loss of information resulting ABSTRACT This paper deals with uncertainty modeling in spatial object-relational databases by the use of Structured Query Language (SQL). The fundamental principles of uncertainty modeling by fuzzy sets are applied in the area of geographic information systems (GIS) and spatial databases. A spatial database system includes types of spatial data and implements the spatial extension of SQL. The implementation of the principles of fuzzy logic to spatial databases brings an opportunity for the efficient processing of uncertain data, which is important, especially when using various data sources (e.g., multi-criteria decision making (MCDM) on the basis of heterogeneous spatial data resources). The modeling and data processing of uncertainties are presented in relation to the applicable International Organization for Standardization (ISO) standards (standards of the series 19100 Geographic information) and the relevant specifications of the Open Geospatial Consortium (OGC). The fuzzy spatial query approach is applied and tested on a case study with a fundamental database for GIS in Slovakia.

Nowadays, in Geographical Information Systems (GIS), usage of Location Based Services (LBS) has been exploded and map based web/mobile applications have been rapidly increased. With the occurrence of this event, spatial data accessing, storing and querying operations have become more important than before. 
There is a new challenge, which is about the efficient access to geographical map data at multiple detail of level. Multiple detail of level is a process of derivation of a map at a particular resolution. This process is called as Multi Resolution Database (MRDB). It is an expensive job and needs advance knowledge about map generalization techniques such as simplification, amalgamation tasks. Existing spatial databases currently do not support this innovation. Current spatial databases only support some basic geometry types to model features and spatial operators/operands for determining geospatial measurements like distance, area, and length. MRDB has another usage domain called information drilling. Information drilling provides linking between spatial objects at different spatial databases. Unfortunately, there is no support for data drilling in current spatial databases. 
In this study, a multi resolution database concept is modeled and prototype applications are developed. Information drilling methodology between multiple spatial databases is constructed. This drilling scenario contains a scale based linked data retrieval features. Sample location is selected as Ankara, which is the capital city of Turkey. Schools, roads, districts and power transformers data have been used as example of different data layers. Server side application is created to serve services to clients by using Java Enterprise Edition (J2EE) technologies. Also a native mobile application is implemented for Android platform to demonstrate information drilling in MRDB concept in mobile domain.

We focused on applying parallel computing technique to the bulk loading of X-tree in other to improve the performance of DBSCAN clustering algorithm. We have given a full description of how the system can be archived. We proposed a new parallel algorithm for DBSCAN and another algorithm to extend the X-tree spatial indexing structure. Spatial database systems incorporate space in database systems, they support non-traditional data types and more complex queries, therefore in order to optimise such systems for efficient information processing and retrieval, appropriate techniques must be adopted to facilitate the construction of suitable index structures.

During the course of 2008 and 2009 the Qatar Museums Authority (QMA) and the VISTA Centre at Birmingham University collaborated together on a project to analyse remotely sensed data in both the marine and terrestrial areas of Qatar. This was with a view to identifying not only archaeological sites, but also deposits that would provide information about changes in the environment during the late Pleistocene and early Holocene. The interpreted data from this was subject to ground truthing and accessed via a GIS platform. At the same time the QMA were considering the development of an Historic Environment Record in order to be able to effectively manage archaeological sites within Qatar and enable the Department of Antiquities to maintain effective digital records of sites as required by Article 4 of Qatar's 'Law No. 2 of 1980 on Antiquities'.

Nowadays revitalization is often defined as a comprehensive effort including revalorization, restoration, reconstruction, modernization and actions aimed at the revival of a building, a district or a town devastated in different aspects, also the economic and social ones. The social aspect is among the most significant ones. In other words, there is no complete revitalization without solving social problems. The presented study discusses problems related to the broadly approached revitalization projects and their main directions of development, based on the example of Polish revitalization programmes of territorial self-governmental units. The first part of the paper presents the definitional aspects of revitalization taking into account social, economic, environmental, spatial, functional, cultural and technical dimensions. Moreover, the definition, structure and purpose of a revitalization programme was characterized. Next the primary objectives and current developmental directions of revitalization in Poland were discussed based on the selected applicable revitalization programmes. Today the main directions of revitalization processes in Poland cover: the reduction of poverty and social exclusion, supporting local business, cleaner environment activities, sustainable spatial and functional development, supporting technical infrastructures, promoting culture-based socioeconomic development.

This article presents the steps taken to ensure the consistency and usability of the geographic information system of soil resources of Romania SIGSTAR-200 within INSPIRE, the Infrastructure for Spatial Information of the European Community. At present, SIGSTAR-200 contains tens of thousands of polygons characterized by three attributes collected from legacy paper maps, namely (i) soil mapping unit (SMU), including soil association, (ii) topsoil texture class and (iii) skeleton class, all defined as stated by the national methodologies. In addition, each soil is characterized concerning the risk of land degradation (by water erosion, wind erosion, salinization, alkalization, gleyzation and waterlogging) through attributes inferred by expert system rules built on pedogenesis. To achieve the compatibility and usability within INSPIRE, SIGSTAR-200 has been transformed in accordance with the common Implementing Rules in force. To this end, first, the SMUs have been correlated at the dominant soil type level with the international soil classification system World Reference Base of Soil Resources (WRB) to ensure the semantic interoperability. Second, the SMUs, modeled as INSPIRE feature type SoilBody, have been populated with new attributes. This step was performed for SIGSTAR-200 dataset in three coordinate reference systems (CRSs): EPSG:3844 (CRS of Romania), EPSG:3035 and EPSG: 4258 (the last two CRSs being required or recommended by INSPIRE). Finally, the data transformed in INSPIRE-compliant GML have been checked by the INSPIRE Validator (Executable Test Framework), passing all the tests currently available for soil datasets, i.e., regarding (i) data consistency, (ii) INSPIRE GML application schemas, (iii) information accessibility, and (iv) reference systems. The next steps of the work, synchronized with the availability of the Executable Test Suites for the themes defined in Annex III of the Directive, aim to fully validate SIGSTAR-200 GML and to conclude its first integration into INSPIRE (Accession Number: WOS: 000526176700012).