Spatial Data Types Research Articles

Актуализация пространственных данных методом государственного топографического мониторинга в целях реализации государственной программы Российской Федерации «Национальная система пространственных данных»: проблемы и решения

The scientific, methodological and technological aspects proposed for updating spatial data – national information resources, based on the results of research in the field of arranging and maintaining state nationwide topographic monitoring, in the process of geodetic and cartographic work, including the collection, systematization, processing and use of various types of spatial data are considered. Terminological concepts and their definitions are formulated and offered for clarification and improvement during the discussion in the scientific community. An analysis of the current legal acts, regulatory and technical documents in the field of geodesy, cartography and real estate cadastre, as well as existing newly developed methods, technologies, techniques and algorithms in terms of technical regulating the processes of arranging and maintaining state topographic monitoring, taking into account its integration into the national spatial data system is made. Operational cartographic support updated in the process of state topographic monitoring with accurate, reliable and high-quality spatial data of socio-economic, environmental activities, defense and national security of the country is one of the priorities of the Russian Federation state program “National System of Spatial Data”. The authors propose the main directions of its implementation.

IDENTIFICATION OF LANDSCAPES OF NATIONAL IMPORTANCE USING GIS

INTRODUCTION
 One of the aims of recognising landscapes of national importance is to encourage public authorities to adopt policies and measures at the local, regional and national level for protecting, managing and planning landscapes throughout national states. It covers unique and outstanding landscapes among the ordinary ones, that not only determine the quality of people’s living environment but also contribute to national identity. Different approaches have been used so far internationally in identifying landscapes of national importance, assessing their characteristics, structure and landscape elements, recognising that both – quantitative assessment and expert judgement should be involved for this task. Within this study, the focus is on the quantitative part of the study, using GIS and revealing the traceable sequence of steps and criteria used.
 MATERIALS AND METHODS
 GIS approach was used to determine landscape areas of national importance, using a hexagon grid - (each in an area of 100 ha, 68,407 hexagons), which covers the territory of Latvia. The aggregation of spatial data in regular grids provides an opportunity to normalise different types of spatial data, as well as to address the use of irregularly shaped polygons (e.g., in the case of politically defined boundaries). The hexagon network, due to the shape, forms continuous coverage of the area, while at the same time the hexagon has a similar shape to a circle, which accordingly provides advantages in terms of defining and representing different spatial relationships. Territories of the most valuable landscapes of national significance are spatially separated, assigning values to hexagons in accordance with the landscape values in their territory. Each hexagon is assigned a value according to whether it overlaps with an area that meets one or more of the criteria for the most valuable landscapes of national importance. In the case of larger, continuous area units, the coincidence of areas is determined by the hexagon centroid, but in the case of smaller, individual area units (also point units), the intersect function is used. The criteria for the research part to be quantified include five thematic sections: natural heritage, cultural heritage and historical evidence, identity and community involvement, uniqueness and landscape quality, which can be quantified from the infrastructure created to highlight the visual aspects and aesthetics of landscape.
 RESULTS
 The part of the quantitative analysis data used to determine the value of the landscape by GIS has been realised in several sequent stages. First, after analysing the main criteria for the identification of landscapes of national importance from existing literature and research thematic areas, they were split into concrete criteria: 8 for natural heritage, 5 for cultural heritage and historical evidence, 6 for identity and community involvement, 4 for uniqueness and 1 for landscape quality. Each of the criteria was given an appropriate weight of 0.5 to 1.5 points (using 0.25 points as a step). Several of the criteria are exclusive and do not overlap; the total amount for most outstanding landscapes would be 12 points. This was followed by a phase of structuring and categorising large amounts of data to allow GIS analysis to be performed. Minor adjustments were made to the weights assigned to the criteria in the methodology during the analysis. Each area of 100 ha, 68,407 hexagons got weighted value, and those territories where the concentration of the highest values were identified, were reconsidered during the next stage as a landscape with national importance. As there were more than 100 such places of concentration, discussion on joint territories having less valuable hexagons in-between has been carried out.
 DISCUSSION
 A landscape character assessment technique that is scientifically sound, region-specific and stakeholder orientated, designed to describe landscape character, has been used often recently. It can be applied at a range of scales and it may also integrate landscape character analysis with biodiversity assessments, the analysis of historical character, and socio-economic functions such as recreation etc. Even so it is primarily concerned with documenting landscape character rather than assigning quality or values, implying a distinction between characterisation and judgement; identifying landscapes with national importance still involves the assessment and evaluation process. This is debated widely as the main concern is to carry out ordinary landscape quality in places where people live, recognising that only a limited number of societies will benefit from daily encounters of unique landscapes. However, the GIS method used and criteria applied provide transparent objectivity in the characterisation of landscape uniqueness, and even if it’s relatively easy to recognise them by perception, having a society consensus, spatial aspects and the identification of borders for such landscapes would be much more difficult without GIS.

A comparative analysis of forest area differences between statistics information and spatial thematic maps

Securing reliable data on forest areas is necessary for the establishment of various policies and decision-making for forest administration. In this study, the definition of forest, minimum partitioning criteria, purpose of production, production method, and period of update were analyzed, which were prescribed for the statistics (Forest Basic Statistics [FBS] and Cadastral Statistical Annual Report [CSAR]) and spatial data (digital forest type map, sub-divided land cover map, continuous cadastral map). Forested area was calculated according to the statistics and spatial data for Wonju, Gangwon-do, and the forest area between statistics and spatial data was quantitatively compared. In terms of the definition of forest and minimum partitioning criteria, the FBS and digital forest type maps were similar, and the land cover map, CSAR, and continuous cadastral maps were different in these aspects. About forest area, there was a difference in each forestry data. The highest was CSAR(61,406 ha) and the lowest was Sub-divided Land Cover Map(57,818 ha). This is thought to be because there were some types of spatial areas that were classified as forest in the digital forest type map but classified as cropland, grassland, settlement, and bare land in the sub-divided land cover map and continuous cadastral map. Moreover, in the case of the continuous cadastral map, it is thought that there was an error in the area calculation due to differences between the land category in the map and the actual land use status, which led to differences in the calculated area between different types of spatial data. For future statistics and spatial data, appropriate measures should be established to address the issue of the differences between the calculated area due to misclassification during visual reading, and the difference between the land category in the map and the actual land use status. The calculated forest area should be used for research on the definition of forest, the purposes of map production, and production methods for each type of information and data

Analyzing public transport in the city of Buenos Aires with MobilityDB.

The General Transit Feed Specification (GTFS) is a data format widely used to share data about public transportation schedules and associated geographic information. GTFS comes in two versions: GTFS Static describing the planned itineraries and GTFS Realtime describing the actual ones. MobilityDB is a novel and free open-source moving object database, developed as a PostgreSQL and PostGIS extension, that adds spatial and temporal data types along with a large number of functions, that facilitate the analysis of mobility data. Loading GTFS data into MobilityDB is a quite complex task that, nevertheless, must be done in an ad-hoc fashion. This work describes how MobilityDB is used to analyze public transport mobility in the city of Buenos Aires, using both, static and real-time GTFS data for the Buenos Aires public transportation system. Visualizations are also produced to enhance the analysis. To the authors’ knowledge, this is the first attempt to analyze GTFS data with a moving object database.

GIS-Based Review for Monitoring the Spatial Distribution of Covid-19: A Case Study of Haryana

Abstract: Worldwide COVID-19 cases were noticed from its first variant to new variant very fast which is becoming threatening to human health, social function, production and supply of goods, and international relations. In Haryana, cases are increasing rapidly. GIS techniques contribute to controlling the epidemic by mapping of visualizing epidemic data, spatial visualization of protected and risk zones, spatial tracking of spreading, and route networking to fulfill demanded supply. These types of big spatial data can support taking decisions, formulating measurements, and effectiveness to control and prevent COVID-19. In the study area, total cases, daily reported cases, active cases, recovered cases, and fatalities were mapped. Based on daily reported cases, a high to less affected zone map was prepared by using the IDW technique. Results showed that the NCR region was highly affected and the rest of the part was less affected. Mapping of spreading epidemics on spatial distribution and variability of corona can provide the most useful insights to the authorities for decision making. Keywords: COVID-19, GIS, Spatial Distribution, IDW, Haryana

GIS-Based Review for Monitoring the Spatial Distribution of Covid-19: A Case Study of Haryana

Abstract: Worldwide COVID-19 cases were noticed from its first variant to new variant very fast which is becoming threatening to human health, social function, production and supply of goods, and international relations. In Haryana, cases are increasing rapidly. GIS techniques contribute to controlling the epidemic by mapping of visualizing epidemic data, spatial visualization of protected and risk zones, spatial tracking of spreading, and route networking to fulfill demanded supply. These types of big spatial data can support taking decisions, formulating measurements, and effectiveness to control and prevent COVID-19. In the study area, total cases, daily reported cases, active cases, recovered cases, and fatalities were mapped. Based on daily reported cases, a high to less affected zone map was prepared by using the IDW technique. Results showed that the NCR region was highly affected and the rest of the part was less affected. Mapping of spreading epidemics on spatial distribution and variability of corona can provide the most useful insights to the authorities for decision making. Keywords: COVID-19, GIS, Spatial Distribution, IDW, Haryana

Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

ФОТОГРАММЕТРИЯ В РАЗВИТИИ ГОРОДСКИХ АГЛОМЕРАЦИЙ

The development of urban areas requires accurate and high-quality spatial data (SD). State pro-grams and regulations define the requirements for them: relevance, informativeness, versatility, ease of use and prompt and cheap monitoring. Existing SD in the form of topographic plans and orthomo-saics do not meet these requirements for urban areas. World experience shows that a 3D city model could solve this problem, but the costs of creating it today are large and comparable to the cost of a 1:500 scale topoplane. A new type of spatial data – a single 3D stereo model of the territory is a real alternative, since it meets the previously specified requirements, and the costs of creating it are signifi-cantly lower than other SD. Therefore, in this paper, the goal was to consider the use of a single 3D stereo model in the management of urban areas, as well as the use of the stereo photogrammetric method on specific examples. It is shown that the stereo model is an optimal product of spatial data, which is created quickly and cheaply and can be used multifunctionaly. The stereo model is used to solve such tasks as operational management of the city, without entering the territory, performing ca-dastral works, including complex cadastral works, performing an inventory of urban infrastructure ob-jects, determining the volume of major repairs of apartment buildings, creating vector models and inte-grating projected BIM models into existing infrastructure, etc. The 3D stereo model was presented at Russian and international exhibitions and forums, such as INNOPROM and "Traditions and Innova-tions", dedicated to the 10th anniversary of Rosreestr, in the period from 2017 to 2021, where it re-ceived recognition. In addition, the stereo model has already been introduced in the Republic of Bash-kortostan, the Kaliningrad and Sverdlovsk regions, the city of Izhevsk, Sarapul and others. The crea-tion and use of the stereo model is based exclusively on the Russian hardware and software complex, which allows it to be implemented for managing cities on the territory of the Russian Federation.

Возможности NOSQL СУБД для обработки пространственных данных

An increasing number of database management systems are expanding their functionality to work with various types of spatial data. This is true for both relational and NoSQL data models. The article pre-sents the main features of those data models for which the functions of storing and processing spatial data are implemented. The methods of artificial intelligence, which are implemented on the basis of a particular data model, are considered. A comparative analysis of the performance of typical spatial queries for database management systems based on various data models, including multi-model ones, is performed. The data set on which the comparison is performed is presented as three blocks of OpenStreetMap vector data for the territory of the Novosibirsk region. Based on the results of the study, recommendations are given on the use of certain data models, depending on the available data and the tasks being solved.

Анализ методов и средств искусственного интеллекта для анализа и интерпретации данных активного дистанционного зондирова-ния

Remote sensing data, like most types of spatial data, are complex, dynamic, semi-structured, which makes it difficult to create an unambiguous and universal process for their processing and use. At the same time, the development of hardware, methods and algorithms of artificial intelligence and ma-chine learning has led to the fact that the areas of information technology are used in almost all areas of science and technology, including the processing of spatial data. The article formulates the main difficulties and tasks of processing remote sensing data, presents the most common methods and tools for their processing at present, using artificial intelligence technologies to automate processes. The possibilities of using specific algorithms and methods of artificial intelligence for all stages of pro-cessing data from active remote sensing are considered.

Reviewing Methods for Controlling Spatial Data Quality from Multiple Perspectives

Spatial data is the core and operation object of geographic information system (GIS). The quality of spatial data determines the application of GIS and the effectiveness of decision-making to a great extent. This article introduces two important types of spatial data, vector data and raster data. Then, this paper discusses the uncertainty and sources of errors in spatial data, and discusses the methods of checking and preventing uncertainty and errors from the aspects and processes of digitization, so as to ensure the quality of spatial data. Finally, this paper explores cutting-edge approaches to improving spatial data quality, including the Area preserving method for improved categorical raster resampling, and using hierarchical grid index to detect and correct errors in vector elevation data. By studying effective data quality control methods, the quality of spatial data in GIS can be guaranteed, and the basic guarantee for the wide application and development of geographic information science can be provided.

Enabling Efficient Distributed Spatial Join on Large Scale Vector-Raster Data Lakes

Both the increasing number of GPS-enabled mobile devices and the geographic crowd-sourcing initiatives, such as Open Street Map, are determinants for the large amount of vector spatial data that is currently being produced. On the other hand, the automatic generation of raster data by remote sensing devices and environmental modeling processes was always leading to very large datasets. Currently, huge data generation rates are reached by improved sensor observation systems and data processing infrastructures. As an example, the Sentinel Data Access System of the Copernicus Program of the European Space Agency (ESA) was publishing 38.71 TB of data per day during 2020. This paper shows how the assumption of a new spatial data model that includes multi-resolution parametric spatial data types, enables achieving an efficient implementation of a large scale distributed spatial analysis system for integrated vector-raster data lakes. In particular, the proposed implementation outperforms the state-of-the-art Spark-based spatial analysis systems by more than one order of magnitude during vector-raster spatial join evaluation.

Spatial modelling of tsunami exposure areas at Ujung Genteng coastal, Sukabumi, West Java

Indonesia is a country located between three tectonic plates. It is the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. The location caused Indonesia to be prone to disasters caused by the movement of plates, one of which was a tsunami. Tsunamis are high waves that hit ports or beaches. Relatively sloping beaches with low beaches can cause larger tsunami waves. This study was carried out on the coastal of Ujung Genteng, Sukabumi. The purpose of this study is to analysis the area of tsunami exposure that occurred on the coast of Ujung Genteng. GIS-based spatial modelling is used to combine different types of data, both spatial and non-spatial data to be processed into tsunami exposure area information. Modelling of the inundation was carried out with mathematical calculations developed by Berryman (2006). By modeling the tsunami with a worst-case scenario of wave height of 20 meters, it was obtained that the tsunami exposed area reached 663.29 Ha. The area belongs to a low hazard class of 2.59 Ha, a medium hazard class of 29.05 Ha, and an area that belongs to a high hazard class of 631.65 Ha. The results of this modeling are expected to be a reference for tsunami disaster mitigation planning in Ujung Genteng.

Integrating telemetry data at several scales with spatial capture–recapture to improve density estimates

AbstractAccurate population estimates are essential for monitoring and managing wildlife populations. Mark–recapture sampling methods have regularly been used to estimate population parameters for rare and cryptic species, including the federally listed Mojave desert tortoise (Gopherus agassizii); however, the methods employed are often plagued by violations of statistical assumptions, which have the potential to bias density estimates. By incorporating spatial information into conventional density estimation models, spatial capture–recapture (SCR) models can account for common assumption violations such as spatially heterogeneous detection probabilities and temporary emigration when animals leave plots during a survey. We conducted mark–recapture surveys at 10 1‐km2 plots in and adjacent to the Ivanpah Valley of California and Nevada from 2015 to 2019. Locality data were collected concurrently using radio‐telemetry and GPS data loggers. GPS data demonstrated that desert tortoises frequently exhibited temporary emigration outside a plot during the survey periods, thereby complicating standard approaches for closed‐model density estimation. We integrated mark–recapture survey data for subadults and adults at each plot with corresponding spatial capture locations and supplementary spatial data using a modified SCR model fitted in a Bayesian framework. We compared density estimates modeled with conventional non‐spatial methods, as well as three SCR models based on symmetrical usage areas described by various levels and types of supplementary spatial data. The conventional model consistently resulted in inflated estimates of density while the SCR models allowed us to generate spatially corrected estimates for a species where detectability and densities are low. However, we found that if not properly specified, the temporal scale of supplementary data may result in an unintended source of bias in parameter estimates. Integrating spatial data over a larger temporal scale than mark–recapture surveys were conducted resulted in higher detection probabilities and lower density estimates, due to an overestimation of space use. Our results not only demonstrate the importance of accounting for spatial information but also the value of understanding the potential for bias when integrating multiple data sets at different temporal resolutions. The methods presented can be used to enhance monitoring efforts for the Mojave desert tortoise and other species where mark–recapture methods are used.

Grazing intensity and human activity intensity data sets on the Qinghai‐Tibetan Plateau during 1990–2015

AbstractAs the ‘third pole’ of the world, the Qinghai‐Tibetan Plateau (QTP) is extremely ecologically sensitive and fragile, while also facing increasing levels of human activity, especially overgrazing. In this study, livestock data from the statistical yearbook were transformed to 1 km grazing intensity raster data by integrating the net primary productivity (NPP) data for 1990, 1995, 2000, 2005, 2010 and 2015. Furthermore, the human activity intensity (HAI) data at 1 km resolution were evaluated by the entropy weight method, which applied eight types of spatial data, including grazing intensity, Night‐Time Light, population density, Gross Domestic Product (GDP) density, the ratio of cultivated land, distance to road, distance to town and the slope of the Normalized Difference Vegetation Index (NDVI). Also, the five‐year interval human activity intensity data on the QTP from 1990 to 2015 were evaluated. By preparing the historical spatial data sets of grazing intensity and human activity intensity, our study will help to explore the influence of human disturbance on the alpine ecosystems on the QTP, as well as provide effective support for the decision‐making of the government aiming to achieve regional ecosystem management and sustainable development.

Multilevel modeling of geographic information systems based on international standards

Even though different applications based on Geographic Information Systems (GIS) provide different features and functions, they all share a set of common concepts (e.g., spatial data types, operations, services), a common architecture, and a common set of technologies. Furthermore, common structures appear repeatedly in different GIS, although they have to be specialized in specific application domains. Multilevel modeling is an approach to model-driven engineering (MDE) in which the number of metamodel levels is not fixed. This approach aims at solving the limitations of a two-level metamodeling approach, which forces the designer to include all the metamodel elements at the same level. In this paper, we address the application of multilevel modeling to the domain of GIS, and we evaluate its potential benefits. Although we do not present a complete set of models, we present four representative scenarios supported by example models. One of them is based on the standards defined by ISO TC/211 and the Open Geospatial Consortium. The other three are based on the EU INSPIRE Directive (territory administration, spatial networks, and facility management). These scenarios show that multilevel modeling can provide more benefits to GIS modeling than a two-level metamodeling approach.

NTEP‐DB 1.0: A relational database for the national turfgrass evaluation program

AbstractGiven field experiment data collected by the National Turfgrass Evaluation Program (NTEP), we aim to design and create a relational database to store the data and support efficient queries. As one of the most widely‐known turfgrass research programs in the world, NTEP has generated large volumes of data on turfgrass cultivars and experimental germplasm since the early 1980s, providing invaluable information for a variety of user groups (e.g., homeowners, seed companies, golf course managers, retailers, turfgrass researchers) to select cultivars that best fit their needs (e.g., winter survival, pest tolerance, turf quality). The datasets have historically been stored in large sets of text files and spreadsheets. Currently, NTEP data are delivered to users through a website (www.ntep.org) as summary reports and it can be extremely tedious (e.g., hundreds of clicks, data merging, jargon) to perform a simple query (e.g., best cultivar selection with typical conditions). This significantly limits the use of NTEP data and hides its value from the public. To address these limitations, we carried out an interdisciplinary effort with horticulture and computer science researchers to design and create the first NTEP database – NTEP‐DB 1.0 – to reduce the manual efforts and expert knowledge currently required to extract meaningful information from the data. Experiments confirm that the query results are correct, and that the database can greatly reduce manual efforts. Anticipating next‐generation advances, we also recommend incorporating spatial data types and analytical techniques into future designs of the database.

Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework

In modern spatial statistics, the structure of data has become more heterogeneous. Depending on the types of spatial data, different modeling strategies are used. For example, kriging approaches for geostatistical data; Gaussian Markov random field models for lattice data; or log Gaussian Cox process models for point-pattern data. Despite these different modeling choices, the nature of underlying data-generating (latent) processes is often the same, which can be represented by some continuous spatial surfaces. A unifying framework is introduced for process-based multivariate spatial fusion models. The framework can jointly analyze all three aforementioned types of spatial data or any combinations thereof. Moreover, the framework accommodates different likelihoods for geostatistical and lattice data. It is shown that some established approaches, such as linear models of coregionalization, can be viewed as special cases of the proposed framework. A flexible and scalable implementation using R-INLA is provided. Simulation studies confirm that the prediction of latent processes improves as one moves from univariate spatial models to multivariate spatial fusion models. The framework is illustrated via a case study using datasets from a cross-sectional study linked with a national cohort in Switzerland. The differences in underlying spatial risks between respiratory disease and lung cancer are examined in the case study.

Qualitative species vulnerability: Model sensitivity to climate and spatial parameters

Natural resource managers model plant and animal climate vulnerability to efficiently plan and prioritize conservation actions. We assessed a commonly used online decision support tool, NatureServe’s Climate Change Vulnerability Index (CCVI) and its sensitivity to different generations of climate models, socioeconomic climate scenarios, and different types and scales of spatial data. Our goal was to help mangers better interpret CCVI species vulnerability rankings and provide a workflow for generating the required climate metrics when those data are not already calculated. We found vulnerability scores were much larger when using an older generation as compared to a newer generation of climate projections, especially for water associated species. Socioeconomic climate scenarios were very important and resulted in differences of 130% between optimistic and pessimistic scenarios. Hence, we caution when comparing across studies to consider the generation of climate models and scenarios because the scores may not be directly comparable. The online tool was robust to the type and scale of spatial data. However, this data is only applied in calculating exposure and is not included in species specific sensitivity via adaptive capacity. Consequently, we suggest that managers use range size as an index of adaptive capacity when interpreting similar scores. Overall, we find the species climate vulnerability model supported by NatureServe a valuable and robust assessment that is easy to use and provides an objective method for prioritizing conservation action and justifying these actions to policy makers and the general public.

A generic test for the similarity of spatial data

Two spatial data sets are considered to be similar if they originate from the same stochastic process in terms of their spatial structure. Many tests have been developed over recent years to test the similarity of certain types of spatial data, such as spatial point patterns, geostatistical data and images. This research proposes a generic spatial similarity test able to handle various types of spatial data, for example images (modelled spatially), point patterns, marked point patterns, geostatistical data and lattice patterns. A simulation study is done in order to test the method for each spatial data set. After the simulation study, it was concluded that the proposed spatial similarity test is not sensitive to the user-defined resolution of the pixel image representation. From the simulation study, the proposed spatial similarity test performs well on lattice data, some of the unmarked point patterns and the marked point patterns with discrete marks. We illustrate this test on property prices in the City of Cape Town and the City of Johannesburg, South Africa.