Spatial Database Research Articles

Use of water resources and sustainability with concepts, definitions and examples

Water is a vital resource for all living things (people, ecosystems, economic development, energy production, national security). Coordination and harmony in spatial and intersectoral planning and decision-making processes can be achieved through the rational and sustainable use of water resources. Protection and sustainable use of water resources is extremely important. Nowadays, problems such as drought and water pollution arise due to global warming and increasing environmental pollution. This situation reduces drinking water resources. Serious measures need to be taken worldwide to ensure the continuity of life protect water resources from drought and keep them clean. Techniques used to evaluate the potential of water resources, which have become increasingly important in recent years, and to create conscious use of water resources, require effective data systems. Currently used systems do not represent very large areas. For this reason, it is necessary to develop representative methods by integrating the data received from ground stations and the parameters of remote sensing techniques. Many extensions of the programs, which are widely used in the world and evaluated using Geographic Information System (GIS) supported Analytical Hierarchy Process (AHP) methods in database studies for the use of water resources, are extremely useful for basin modeling. The purpose of this study; It is a study on the potential of GIS-based multivariate statistics for evaluating the use and sustainability of water resources and the use of water stored in a geographical database.

An “Earthly” Manifestation of Power: The Architectural and Urban Contexts of Iznik Tiles Produced under the Patronage of the Ottoman Palace

ABSTRACT Çini-making, which is an Ottoman underglaze ceramics tradition included in the UNESCO Representative List of the Intangible Cultural Heritage of Humanity, was used extensively in the monumental architecture of the Ottoman Empire in the fifteenth and seventeenth centuries, including mosques built by the Ottoman sultans and their family members. Iznik tiles reveal undeciphered relationships between Ottoman patronage and monumental architecture, and unwritten architectural rules and norms concerning tile designs. To contribute to the conservation, interpretation, and presentation of the mosque spaces, it is important to understand the tangible and intangible cultural qualities of Iznik tiles. This study examines the cultural qualities of Iznik tiles by analysing the historical and architectural qualities of the five selected sultanic mosques and by integrating the literature and field surveys through Geographic Information Systems (GIS) modelling and database. We aim to analyse and develop an evaluation model to investigate the cultural qualities of Iznik tiles on regional, urban, and architectural scales using GIS.

From shopping centers to cultural centers: Los Angeles strip malls as sites of ethnic and immigrant placemaking

ABSTRACT The strip mall is a ubiquitous urban design type formed by a strip of commercial establishments on a single parcel of land that surround an open, street-facing parking lot. We hypothesize that they are important sites for ethnic and immigrant businesses in LA and, in particular, that their relatively low rents, location, and design (including site layout, unit size, and structure quality) are particularly conducive for ethnic cultural placemaking. Using data about the built environment, real estate market, and our own original “EthniCITY” spatial database of race/ethnicity placemaking patterns in Los Angeles, we use mapping and spatial analysis, descriptive statistics, a hedonic price model, and groundtruthing fieldwork to find that small and medium-sized strip malls are important ethnic places in Los Angeles, hosting the most ethnic commercial establishments amongst all commercial building types. We also find that half of all small strip malls are located within ethnic cultural hubs. Our findings suggest that physical urban design matters in shaping the cultural life of cities and the geographic opportunities for immigrant entrepreneurship.

Bioclimatic zoning for building performance using tailored clustering method and high-resolution climate data

Building environments are specific and complex bioclimatic systems. Thus, well-suited climate classification methods for buildings are essentially needed to develop building design guidelines and standards. To address it, the present research introduces a novel fit-for-purpose clustering method for bioclimatic zoning based on the hygrothermal and energy performance of buildings. This bioclimatic zoning was developed to update the Brazilian standard and has been validated across various climates and building typologies (residential and commercial) in Brazil. In a preliminary analysis, three classification methods were developed using K-means and Decision Tree to classify climates according to building performance. Subsequently, a final bioclimatic zoning method was developed using a tailored version of the best method designed for real-world applications (Decision Tree) in the Brazilian context. The performance of the bioclimatic zoning achieved was compared with three existing climate classifications: Köppen-Geiger, ASHRAE 169-2020, and ABNT-NBR 15220-3 (Brazilian Standard). The results showed that the new bioclimatic zoning method outperformed the existing ones to cluster the building performance indicators. Moreover, high-resolution spatial climate databases, such as NASA-POWER, CRU, and ERA5-Land, were processed and analyzed to be employed in locations without properly measured data. Three metamodels of climate indicators were developed and compared with these databases to select the most accurate climate data sources. Finally, these databases were employed to classify all 5570 Brazilian municipalities according to the final bioclimatic zoning, which enabled the development of an accurate and high-resolution map.

Location-Based Management of Paleontological Collections using Open Source GIS Software

Management of Natural History collections, particularly paleontological collections, is usually structured around specimens. However, it might not be feasible when the collections are incorporated from sources where the whole cycle of specimen collection is unknown, making it hard to determine what fossils belong to a single specimen and if these specimens belong to a single fossil-assemblage, taxon or individual. The Sociedade de História Natural from Torres Vedras (Portugal) manages such collections using a management strategy approach based on the spatial location from where the fossils were found. To achieve this, we have implemented a methodology for field collection and developed a spatial database in PostgreSQL/PostGIS to manage the entire stages of paleontological collections management. On top of the database, a second layer of web services, Web GIS (Geographical Information System) applications, and desktop GIS, all based on QGIS, are also under development to facilitate access to data stored in the database to internal and external researchers and partners, as well public institutions. This effort is made in close collaboration with in-house actors and stakeholders and has yielded promising results. The development of this system provides four advantages: (i) it played an essential role in supporting the transfer of the collection to new storage facilities; (ii) it provides overviews of the distribution of the characteristics of the collections; (iii) it enhances institutional partnerships by providing consistent and reliable data and(iv) it supports the establishment and development of research lines.

Optimizing Performance in Distributed Cloud Architectures: A Review of Optimization Techniques and Tools

This research paper presents a groundbreaking hybrid transactional/analytical processing (HTAP) architecture designed to revolutionize real-time point cloud data processing, particularly in autonomous driving environments. Integrating elements from both columnar and row-based tables within a spatial database, the proposed architecture offers unparalleled efficiency in managing and updating point cloud data in real-time. The architecture's distributed nature operates through a seamless synergy of Edge and Cloud components. The Edge segment operates within the Robot Operating System (ROS) environment of the vehicle, while the Cloud counterpart functions within the PostgreSQL environment of cloud services. The communication between these components is facilitated by Kafka, ensuring rapid and reliable data transmission. A pivotal aspect of the proposed system lies in its ability to autonomously detect changes in point cloud data over time. This is achieved through a sophisticated algorithm that analyzes dissimilarities in the data, triggering real-time updates in areas where high dissimilarity is detected. The system ensures the maintenance of the latest state of point cloud data, contributing significantly to the generation of safe and optimized routes for autonomous vehicles. In terms of optimization, the paper demonstrates how the HTAP architecture achieves real-time online analytical processing through query parallelization in a distributed database cluster. The system's efficacy is evaluated through simulations conducted in the CloudSim framework, showcasing its scalability, adaptability, and robustness in handling point cloud data processing for a single vehicle. While acknowledging the achievement of the proposed architecture, certain limitations are recognized. The study highlights the need for further investigation into the system's performance under simultaneous analysis and updates from multiple vehicles. Additionally, ensuring seamless scalability and robustness for uninterrupted operation and expansion during runtime is identified as an area requiring further development.

Data collection for assessment of the natural capital at the regional level: case study of LTSER Trnava region

ContextThe landscape provides not only a living space for all life forms, including humans, but also a spatial base and set of resources for the implementation of individual human activities. Inappropriate implementation of human activities, disrespecting the properties of the landscape's natural resources, causes the degradation of natural resources and, consequently, the human living.ObjectivesThe aim of this paper is to develop new methodological procedures and algorithms for effective assessment of natural capital based on the geosystem approach.MethodsEach territorial unit (geosystem) represents a unique combination of natural assets that create a certain potential for the development of individual activities and eco-stabilization functions. In this study, we developed a new approach and algorithms to assess the natural capital of landscapes for sustainable use. This involves selecting indicators and their functional interpretation, as well as collecting available spatial data and statistics for GIS analysis, synthesis, and modeling.ResultsThe methodological procedure consists of the determination of indicators for natural capital assessment, the determination of their functional values and weighting coefficients, the determination of the suitability of the geosystem for the implementation of individual activities based on the value of natural capital, and the determination of restrictions and limiting factors. The set of data on landscape assets can be categorized into abiotic, land cover and biotic, and socio-economic indicators, which can either support human activities or limit them. Options for sustainable use of natural capital were split into two groups of potential activities: (I) natural capital for landscape planning activities and (II) specific activities or functions (e.g., natural capital for energy use, recreation, regulation services). The modeling of eco-stabilizing natural capital in Trnava LTSER pointed to low spatial ecological stability, mainly in the central part of the district.DiscussionDiscussion pointed to strength, novelty and opportunities of implementing methodological approach to natural capital assessment.ConclusionsAs an output of this methodological approach, a comprehensive digital spatial database of landscape-ecological data for the assessment of natural capital and the suitability of its use for socio-economic activities has been created in Slovakia. The database represents a set of consistent spatial information on natural capital assets and other indicators, including land cover and socio-ecological indicators. The methodological approach can be applicable to any territory on the basis of a modification.

Large multi-decade beaver ponding changes in the subarctic Hudson Bay Lowlands, Canada observed using satellite remote sensing

Beavers strongly impact hydrology and ecosystems through their widespread dam building that creates ponds and wetlands. Monitoring the relative abundance of beavers and their waterbodies is needed to assess these effects and factors influencing population levels. However, the ability to do this over vast, remote regions is limited with conventional aerial or field-based surveying. To address this challenge, we developed a satellite remote sensing method to track beaver ponding changes over multiple decades and applied it to a 5127 km2 region of the coastal Hudson Bay Lowlands in Manitoba, Canada. Annual, sub-pixel surface water mapping using 30 m resolution Landsat satellite data, combined with a spatial database of beaver dams, permitted the mapping of 37 year (1985–2021) beaver ponding dynamics. We identified 1714 beaver dams and 1085 beaver pond complexes covering 31 km2, indicating that beavers have an important influence on stream hydrology in this high subarctic landscape. The total area of ponding decreased by 53% from 1986–1989 and by 80% by 1995, and then gradually recovered to initial levels by 2015. The early, steep drop in beaver ponding corresponded to a 13% decline in regional surface water area, while a similar wetness decline during 2015–2018 resulted in little change in beaver ponding. We suggest that strong beaver ponding dynamics were likely caused by the interaction between streamflow levels and beaver populations living near their northern range limit and cold tolerance. The pond mapping method can be applied to other regions if the long-term distribution of beaver dams is known, and ponds are large enough to be identified using a Landsat sub-pixel approach.

Testing for concordance between predicted species richness, past prioritization, and marine protected area designations in the western Indian Ocean.

Scientific advances in environmental data coverage and machine learning algorithms have improved the ability to make large-scale predictions where data are missing. These advances allowed us to develop a spatially resolved proxy for predicting numbers of tropical nearshore marine taxa. A diverse marine environmental spatial database was used to model numbers of taxa from ∼1000 field sites, and the predictions were applied to all 7039 6.25-km2 reef cells in 9 ecoregions and 11 nations of the western Indian Ocean. Our proxy for total numbers of taxa was based on the positive correlation (r2=0.24) of numbers of taxa of hard corals and 5 highly diverse reef fish families. Environmental relationships indicated that the number of fish species was largely influenced by biomass, nearness to people, governance, connectivity, and productivity and that coral taxa were influenced mostly by physicochemical environmental variability. At spatial delineations of province, ecoregion, nation, and strength of spatial clustering, we compared areas of conservation priority based on our total species proxy with those identified in 3 previous priority-setting reports and with the protected area database. Our method identified 119 locations that fit 3 numbers of taxa (hard coral, fish, and their combination) and 4 spatial delineations (nation, ecoregion, province, and reef clustering) criteria. Previous publications on priority setting identified 91 priority locations of which 6 were identified by all reports. We identified 12 locations that fit our 12 criteria and corresponded with 3 previously identified locations, 65 that aligned with at least 1 past report, and 28 that were new locations. Only 34% of the 208 marine protected areas in this province overlapped with identified locations with high numbers of predicted taxa. Differences occurred because past priorities were frequently based on unquantified perceptions of remoteness and preselected priority taxa. Our environment-species proxy and modeling approach can be considered among other important criteria for making conservation decisions.

Geoinformation model for risk management in Burundi

This paper is focused on the development of a geographic information system model for decision preparation. The application of indicator approach for visualization of risks associated with agricultural activities is presented. The paper describes not only the functioning of the geographic information model, but also the interaction between the geographic information model, database and web application in order to present in real time the geographic risks inhibiting agricultural activities.

GIS-Based Spatial Analysis Model for Assessing Impact and Cumulative Risk in Road Traffic Accidents via Analytic Hierarchy Process (AHP)—Case Study: Romania

On a global scale, traffic incidents are a leading cause of mortality and material damage. Romania exhibits the highest rate of road traffic fatalities both in the European Union and worldwide, requiring a comprehensive examination of its overall influence on a national level. The current study uses an extensive approach centering on a spatial analysis model based on the Analytic Hierarchy Process (AHP). Employing a series of spatial databases, this model delineates the geographical distribution and characteristics of road accidents to establish both their cumulative national impact and the identification of high-risk areas. The spatial database, containing traffic incident data, is constructed using geolocation techniques and integrated through network analysis to evaluate the impact in relation to distance. The AHP framework is applied in analyzing the impact across five key dimensions: accident severity, occurrence mode, prevailing weather conditions, traffic restrictions, and road markings. This multi-level AHP analysis not only identifies high-risk hotspots but also confirms the effectiveness of the proposed spatial model.

Pathways and technologies for modern irrigation services: Iran country paper

AbstractI.R. Iran, with 8.8 million ha of irrigated area, is fifth in the world with respect to irrigated land. Scarcity of water resources and great difficulty with water accessibility have brought about initiatives of technology building, as well as approaches to agricultural water management by local communities. During recent decades a huge public budget has been invested in order to improve irrigation services. Improving performance of irrigation systems needs proper diagnosis of the problems and a systematic approach towards modernization. Also having been recently developed and modernized, adjustment of the laws and regulations of the water sector has caused management frameworks of irrigation networks to be gradually improved. In general, in addition to the management framework, equipping with modern tools for measuring water flow and applying tools such as geographic information systems and databases have led to the improvement of water distribution and management. Technical, managerial and policies programmes, as well as investment in water resources and irrigation infrastructures in addition to training and education of farmers and field engineers in water saving in agriculture and enhancing water productivity, resulted in positive impacts on better management of limited fresh water in irrigation.

The Role of Geomatics In Combating Crime and Terrorism Iraq : An Applied Study

Today, geographic technologies are considered one of the most important decision support systems in the world, as they are indispensable in the areas of development and economic development. It has been applied at the security level and the preservation of societal peace through its support for command and control rooms in various security sectors, and assistance in identifying areas of crime dens and spatially distributing criminal phenomena. This study aims to answer the following questions: 1- What is meant by geomatics technology? What characteristics do you have?. 2- How is this technology employed in the field of crime and combating terrorism, Iraq as an applied model?. 3- What is the best way to build a database and spatial information that the security services can benefit from? It was found through the study that geomatics technology has a high potential in the field of combating crime and terrorism, which is done by building digital models and spatial databases for the security aspect through which it is possible to follow up hotspots of crime concentration, and to identify the characteristics of criminals according to the different type of crimes committed, as well as The ability to refer to and update data according to close schedules whenever needed

Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability

Abstract. Spatial soil databases can help model complex phenomena in which soils are a decisive factor – for example, evaluating agricultural potential or estimating carbon storage capacity. The Latin America and Caribbean Soil Information System, SISLAC, is a regional initiative promoted by the Food and Agriculture Organization's (FAO) Latin America and the Caribbean Soil Partnership to contribute to sustainable management of soil. SISLAC includes data from 49 084 soil profiles distributed unevenly across the continent, making it the region's largest soil database. In addition, there are other soil databases in the region with about 40 000 soil profiles that can be integrated into SISLAC and improve it. However, some problems hinder its usages, such as the quality of the data and their high dimensionality. The objective of this research is evaluate the quality of the SISLAC data and the other available soil databases to generate a new improved version that meets the minimum quality requirements to be used for different purposes or practical applications. The results show that 15 % of the existing soil profiles had an inaccurate description of the diagnostic horizons and 17 % of the additional profiles already existed in SISLAC; therefore, a total of 32 % of profiles were excluded for these two reasons. Further correction of an additional 4.5 % of existing inconsistencies improved overall data quality. The improved database consists of 66 746 profiles and is available for public use at https://doi.org/10.5281/zenodo.7876731 (Díaz-Guadarrama and Guevara, 2023). This revised version of SISLAC data offers the opportunity to generate information that helps decision-making on issues in which soils are a decisive factor. It can also be used to plan future soil surveys in areas with low density or where updated information is required.

Management zone classification for variable-rate soil residual herbicide applications

AbstractThe use of soil residual herbicides, along with other practices that diversify weed management strategies, have been recommended to improve weed management and deter the progression of herbicide resistance. Although soil characteristics influence recommended application rates for these herbicides, the common practice is to apply a uniform dose of soil residual herbicides across fields with variable soil characteristics. Mapping fields for soil characteristics that dictate the optimal dose of soil residual herbicides could improve the efficiency and effectiveness of these herbicides, as well as improve environmental stewardship. The objectives of this research were to develop and quantify the accuracy of management zone classifications for variable-rate residual herbicide applications using multiple soil data sources and soil sampling intensities. The maps were created from soil data that included (i) Soil Survey Geographic database (SSURGO), (ii) soil samples (SS), (iii) soil samples regressed onto soil electrical conductivity (EC) measurements (SSEC), (iv) soil samples with organic matter (OM) data from SmartFirmer® (SF) sensors (SSSF), and (v) soil samples regressed onto EC measurements plus OM data from SmartFirmer® sensor (SSECSF). A modified Monte Carlo cross validation method was used on ten commercial Indiana fields to generate 36,000 maps across all sources of spatial soil data, sampling density, and three representative herbicides (pyroxasulfone, s-metolachlor, and metribuzin). Maps developed from SSEC data were most frequently ranked with the highest management zone classification accuracy compared to maps developed from SS data. However, SS and SSEC maps concurrently had the highest management zone classification accuracy of 34% among maps developed across all fields, herbicides, and sampling intensities. One soil sample per hectare was the most reliable sampling intensity to generate herbicide application management zones compared to one soil sample for every 2 or 4 hectares. In conclusion, soil sampling with ECa data should be used for defining the management zones for variable-rate (VR) residual herbicide applications.

Analysis and Improvement of Geographic Information Systems for Problem Solving and Decision Making

The complexity of our way of life has increased due to the numerous parts of life developing quickly, which results in ongoing issues. To address these issues and respond to these rapid changes in the environment, solutions that were practical, quick, and easy to implement were needed. The use of geographic information tools, a contemporary innovation, allows for the implementation of difficult issues by enhancing users' abilities to comprehend problems thoroughly through the analysis of spatial data and the creation of digital maps. This allows decision-makers to save time, effort, and money by making informed choices that will result in the best possible solution to the issue at hand. In addition to a significant urban expansion, an increase in the number of people using vehicles, and a significant emigration of people from rural and small towns, the traffic jams that have recently engulfed much of the world, particularly in the major capitals and cities, are a result of these factors for transportation, have contributed to a complex problem in modern times. The goal of this study is to examine proposed elements and gauge their impact on the issue of traffic bottlenecks. It then suggests both long- and short-term remedies for this issue based on the study's findings, which are generally not found in the pertinent departments. In order to create a unique surface for these components and provide a comprehensive picture of the research region, it also makes satellite photos of the area available. geographical database for each of them, evaluate each layer's influence on the traffic jam independently by analyzing it, and then merge these levels to create full map that shows their location and the extent to which they affect the study problems. This process was done in order to arrive at the aforementioned solution.

Analysing Inequity in Accessibility to Services with Neighbourhood Location and Socio‐Economic Characteristics in Delhi

The lack of comprehensive spatial data for neighbourhoods in cities in the global South has posed a significant challenge for examining socio‐economic inequities in accessibility to services. By combining the primary (survey data) and secondary data sources with new spatial data sources (Earth observation data, Google Maps), we create a spatial database of 4,145 residential locations in Delhi, aggregating them into 1 km grid‐shaped neighbourhoods. The neighbourhood's economic status is evaluated using a composite index of the built environment, land price, and household income. Social characteristics are examined through the percentage of the scheduled caste (SC) population, considering their historical marginalization in Indian society. Using the E‐2SFCA method, we calculate accessibility to four key services and employ the geographically weighted regression (GWR) model to explore inequities in accessibility based on neighbourhood location and socio‐economic characteristics. Findings reveal inequity in accessibility to services at the neighbourhood level is primarily driven by spatial location rather than income or percentage of SC population. Moreover, the influence of socio‐economic characteristics on accessibility varies across locations. The spatial data mapping approach employed in this article can be applied to numerous rapidly urbanizing cities in the global South lacking block or neighbourhood‐level spatial data.

A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka

AbstractIdentifying flood‐prone areas is essential for preventing floods, reducing risks, and making informed decisions. A spatial database with 595 flood inventory and 13 flood predictors were used to implement five boosting algorithms: gradient boosting machine (GBM), extreme gradient boosting, categorical boosting, logit boost, and light gradient boosting machine (LGBM) to map flood susceptibility in Rathnapura while evaluating trained model's generalizing ability and assessing the feature importance in flood susceptibility mapping (FSM). The model performance was evaluated using the F1‐score, kappa index, and area under curve (AUC) method. The findings revealed that all the models were effective in identifying the overall flood susceptibility trends while LightGBM model had superior results (F1‐score = 0.907, Kappa value = 0.813 and AUC = 0.970), securing the top scores across all performance metrics compared to the other models (for testing dataset). Based on kappa evaluation, most of the models had finer performance (AUC min = 0.737) while LightGBM had moderate performance for predictions beyond the training region. According to the results, regions with lower altitudes and topographic roughness values, moderate rainfall, and proximity to rivers are more susceptible to flooding. This framework can be adapted for rapid FSM in data‐deficient regions.

Definitions, Classification Schemes for Active Faults, and Their Application

Active faults are generally defined as faults that have moved in the past and will continue to be active in the future. They are expected to cause deformation and potential disasters if they are localized close to human activities. The definition and classification of active faults are important bases for evaluating the risk. This paper summarizes and compares the history, status, and progress of their definition and classification schemes used in representative countries and regions, as well as in some relevant standards, in active fault mapping, in the construction of spatial databases, and in some other aspects. It is concluded that the current geodynamic setting, existing technical means, geological operability, application purpose, and social acceptability of active faulting hazard in a specific area comprehensively determine the selection of the definition and classification. The key parameter in defining active faults is the time limit. It usually involves four time scales, i.e., Neotectonic (post-Neogene), Quaternary, Late Quaternary, and Holocene. The definition using a short time scale, such as Late Quaternary and Holocene, is usually suitable for the plate boundary zone, which has a high strain rate, but active faults in the intraplate deformation region and stable continental region should be defined with a long time scale, such as the Quaternary and Neotectonics. In addition, the magnitude standard can determine the activity intensity of active faults, which most generally includes three classes, namely, M ≥ 5.0 damaging earthquakes, M ≥ 6.0 strong earthquakes, and M ≥ 6.5 earthquakes that may produce surface displacement or deformation. The M ≥ 5.0 earthquake is generally applicable to regional earthquake prevention and risk mitigation in many countries or regions, but the M ≥ 6.5 earthquake magnitude benchmark is generally used as the standard in rules or regulations regarding active fault avoidance. The most common classification schemes in many countries or regions are based on fault activity, which is reflected mainly by the fault slip rate and fault recurrence interval (FRI), as well as by the last activation time. However, when determining the specific quantitative parameters of the different activity levels of faults, it is necessary to comprehensively consider the differences in activity and ages of the faults in the study region, as well as the amount and validity of existing data for the purpose of classifying different active levels of faults effectively.

ПРИКЛАДНАЯ ЛИНГВИСТИКА В ОРДЕНА ЗНАК ПОЧЕТА ИНСТИТУТЕ ИСТОРИИ, ЯЗЫКА И ЛИТЕРАТУРЫ УФИЦ РАН: ОСНОВНЫЕ ВЕХИ

The article analyzes the development of applied linguistics within the framework of Bashkir linguistics, as well as the formation of this field. Most researchers working in the field of applied linguistics consider it to be a multidisciplinary field that encompasses all existing research. However, there is still no clear definition of what constitutes this term. In the article, applied linguistics is defined as referring to areas that involve the use of mathematical methods, computer-aided text processing, and the creation and analysis of linguistic databases. These areas have been active fields of research at the Institute of History, Language and Literature of the Ufa Federal Research Center of the Russian Academy of Sciences for over 30 years. The first steps towards this goal in the Bashkir linguistics were taken by the group “Automation of Lexicography”, which was founded in 1991. During its 11-year existence, the team developed frequency dictionaries for the language in the works of classical Bashkir literature by D. Yulty, as well as frequency dictionaries on different styles of Bashkir, primary school textbooks, and other materials. Due to the widespread adoption of computer technologies, which required the creation of standards for software, localization, support for operating systems, and promotion of the Bashkir language online, a laboratory for linguistics and information technology has been established on the basis of this group. The laboratory successfully completed these tasks: they presented standards for keyboard layouts and code tables; they participated in the localization of Lexicon, FineReader, Mozilla, Disco Commander and Chameleon programs; and they created national fonts and implemented language support for Windows and Linux operating systems. These works were the foundation for the full functionality of the language in the digital world. On their basis, the laboratory developed a terminological database operating on the internet, an information system for the Machine Fund of the Bashkir language, and corpus projects on prose, journalist and folklore texts in the Bashkir language. Based on these latter projects, certain issues regarding the syntactic structures of proverbs and sayings in the national language have been investigated. In order to continue dialectological research and to create a database of dialects of the national language, the laboratory was transformed into the Department of Applied Linguistics and Dialectology in 2022. Today, the department continues to work on corpus projects of the Bashkir language and collects expedition materials for a geographic information database on regional dialects. Based on these materials, a great deal of scientific work is carried out to clarify problems of the function of oral Bashkir speech within the media, as well as to determine phonetic and lexico-grammatical characteristics of the Northwest dialect.