Traditional GIS Research Articles

Research on the application technology of expert knowledge graph based on genetic search algorithm

Abstract The present study investigates the application of a genetic search algorithm on an expert knowledge graph. By employing binary coding for candidate experts in the expert database, the matching evaluation function is utilized to define the environmental fitness index, thereby optimizing the selection of expert groups. Experimental results demonstrate that the GES algorithm exhibits a superior matching degree and reduced time consumption when addressing science and technology item-matching requirements in review work. In comparison with the traditional BFS algorithm and GIS algorithm, the GES algorithm showcases enhanced potential for application and stability when confronted with large datasets. This paper further analyzes the impact of factors such as the total number of experts, number of fields, and number of scientific and technological projects on algorithm performance, confirming that adjustment of algorithm parameters can effectively enhance matching degree while reducing time consumption, thus providing technical support for practical implementation.

A web GIS based integrated water resources assessment tool for Javeh Reservoir

The creation of tools and services for tracking, estimating, and predicting water resource indices that might provide authorities access to information in close to real-time is essentially a must. The considerable development of Web-Geographic Information System Decision Support System (Web-GIS DSS) allows for the analysis of geospatial data and scenarios via the internet without a traditional desktop GIS. The web GIS technology is coupled to techniques which determine water resources Carlson, and Organization for Economic Corporation and Development (OECD) Trophic Status Indices (TSIs), the water resource vulnerability index (WRVI), and social water stress index (SWSI). The water quality indices were determined by helping machine learning (ML) techniques, Decision Tree, Random Forest, and XGBoost. The performances of aforementioned MLs were compared, and the best efficient one, XGBoost, was included in the Web-GIS DSS tool. The developed Web-GIS DSS was examined in Javeh Reservoir, Kurdestan, Iran.The developed Web-GIS DSS allows the users to define the relevant features of the Javeh Reservoir system in pre-defined time horizons such as meteorological, hydrological, inflow water quality, reservoir operation strategies, downstream water demands, geographical external water transfers, etc. The results indicate that the Javeh Reservoir experiences eutrophication status according to the Carlson and OECD indices over time horizons. The water quantity assessment reveals the Javeh Reservoir's susceptibility based on WRVI, although no stress is indicated by SWSI. This susceptibility may be attributed to limited precipitation and water inflow during the simulation period, suggesting a need for comprehensive water resources management.The conclusion drawn from this research underscores the essential role of the developed Web-GIS DSS in decision-making processes for water and environment management, substantiating its practicality with accurate and user-friendly water and environmental data analysis. The tool exemplifies the potential of integrating ML and Web-GIS technologies to promote sustainable environmental practices, thereby establishing a model for future research and policy-making initiatives.

Reaching beyond GIS for comprehensive 3D visibility analysis

Visibility analyses are one of the fundamental geospatial processing tasks applied in various fields. Recent technological advancements have accelerated viewshed calculations and improved their accuracy thanks to the growing availability of high-resolution spatial data. However, limitations of standard GIS tools, primarily related to the inability to perform analyses in a fully three-dimensional environment and the restriction to only point-based object analyses, have hindered the development of visibility analyses methods that accurately replicate human perspectives. This article presents a novel approach that combines GIS with 3D graphic software and uses high resolution LIDAR data. The presented method not only addresses the limitations of traditional GIS methods but also opens up new possibilities for comprehensive 3D visibility analyses that can better represent real-world scenarios. The proposed methodology, which ensures compatibility of the results with GIS for further spatial analysis, has the potential to enhance decision-making processes in fields such as urban planning, landscape management and protection, environmental impact assessment, infrastructure planning and development. By presenting a case study involving a photovoltaic farm, this article demonstrates the practical applicability and benefits of the proposed method in real-world scenarios.

The integration of GIS location‐based APIs and urban growth modeling for improved geographic access to hospital services

AbstractThis article aims to present an integration model of GIS with open data sourced from application programming interface (API) as a solution for the location set covering problem (LSCP) with an urban land dynamics model. The development of GIS which is increasingly advanced makes traditional GIS transition in the open data era to become more modern. One of the benefits is to help urban planners in determining the allocation of health facilities such as hospitals. This research takes the case of hospital service coverage during emergencies, especially during the COVID‐19 extraordinary event in Metropolitan Semarang, Indonesia. In addition to utilizing API‐base Location, the model process also uses a Cellular Automata‐based land use prediction model. Thus, the facility location plan not only considers service coverage but also land use growth which is a reflection of population growth. To analyze the problem of inequity of hospital services, this research combined the location‐based APIs‐based service area model with the urban growth model to evaluate the existing condition and predict the future of hospital service demand. It also uses the emergency standard with a maximum service distance of 1500 m and a maximum travel time of 7 min. The model confirmed that there are still critical spots not served by hospitals in Semarang City. According to the concept of health and place, it is essential to recommend adding two hospitals in unserved areas so that services are more evenly distributed in the future, especially in emergencies.

Temporal and spatial distribution characteristics of the Ming Great Wall

The Great Wall during the Ming Dynasty (1368–1644) was the most prominent engineering volume and the most perfect military defense structure in the history of China. By combining traditional historical textual research, site surveys, and GIS database analysis, this paper reveals the Ming Great Wall’s spatial and temporal distribution characteristics and the immediate causes of their formation are discussed in the nine military districts of the frontier. The results show that: (1) The general rule of the Ming Great Wall was to set up military forts, passes, piers, and other related defensive facilities before constructing the continuous wall. (2) The Great Wall spatial and temporal distribution of the phases is relatively apparent, divided into five construction phases in the Ming Dynasty (1368–1620): Hongwu to Xuande (1368–1435), unbuilt the wall; Zhengtong to Tianshun (1436–1464), the consecutive wall appeared; Chenghua to Zhengde (1465–1521), the first construction climax of the Great Wall; Jiajing period (1522–1566), the second construction climax of the Great Wall; Longqing to Wanli (1567–1620), the completion of the whole line of the Great Wall.(3)The warfare and defense posture was the immediate factor that prompted the construction of the Great Wall between the Ming Dynasty and Mongolia. This study has some reference significance for restoring the integrity and authenticity of the Ming Great Wall, and contribute to the preservation of the cultural heritage of.

A review of studies on mass-movements on the Moon

The study of the lunar surface is a significant field in exploring the Moon. As a non-negligible geological process shaping the lunar surface, however, the mass movements on the Moon have not been deeply studied regarding all available datasets. In this paper, we report the results of the literature review and quantitative analysis of 965 articles retrieved from the Scopus, WoS, and Astrophysics Data System databases using keyword search terms between the years 1872 and 2022. The main findings show that the lunar mass movements are a lasting but recent prosperous research topic (since 2009). The top three influential journals in this area are Icarus, JGR, and P&SS. Robinson, Lucchitta, and Carr are the most three productive authors. California Institute of Technology, Arizona State University, and Johns Hopkins University are active institutions leading the lunar mass movements research, and the related institutions are mainly located in the United States, Russia, and China. Articles are primarily published in journals specializing in the fields of astronomy and physics. The index analysis of citation indicates the growth of the academic impact of articles about the lunar mass movements. This article summarizes the datasets, methods, and relevant parameters of lunar mass movements to facilitate future research, as well as discussing the limitations and trends in the field. In addition, four unsolved issues are proposed, including: 1) the lack of a comprehensive global database that records all types of landslides incidents; 2) the need for definite classification indicators to quantify the classification criteria for lunar mass movements; 3) the absence of a mathematical model to explain the triggering mechanism of mass movements on the Moon; and 4) the lack of quantitative indexes to evaluate the modification effect of mass movements on the lunar surface morphology. With the support of big data, the combination of artificial intelligence and traditional GIS methods is expected to become the main approach for addressing these issues such a bibliometric analysis can inspire future researchers by exploring the overall characteristics of the published literature on lunar mass movements.

METHODOLOGICAL PRINCIPLES OF THE ORGANIZATION OF REAL ESTATE OBJECTS IN THREE-DIMENSIONAL CADASTЕRS

The purpose of the article is to study methodological provisions for the organization of real estate objects in the three-dimensional cadastre. The principles of combining two-dimensional information about land plots and three-dimensional information about real estate objects, taking into account legal information about each element of the system, are determined. Special attention is paid to the principles of selection and implementation of the geospatial data base of the three-dimensional cadastre system. This database of geospatial data is based on modern developments and effective tools. The three-dimensional cadastral system is responsible for registering the legal status of real estate objects and has the ability to provide this information to clients in the future. The increase in the use of space under and above the surface of the Earth presented the cadastral system with the need to determine and store information about objects of the real world under and above the surface. Entitlement to specified objects is still registered using 2D surface areas. In the third dimension, it is important to be able to penetrate into the actual situation, which is determined by the factor of confirming the legal status of the land plot or real estate object. Therefore, the developed three-dimensional cadastre system provides an opportunity to collect, model and store, request and visualize data on real estate objects under and above the surface in combination with already existing two-dimensional data on land plots. With the help of the specified proposals of the tool of the three-dimensional cadastral system, it is possible to use real estate objects more effectively, taking into account rights and restrictions. Thus, the study presents methodical regulations on the organization of real estate objects in the three-dimensional cadastre. The principles of combining two-dimensional information about land plots and three-dimensional information about real estate objects, taking into account legal information about each element of the system, are determined. Special attention is paid to the principles of selection and implementation of the geospatial data base of the three-dimensional cadastre system. This database of geospatial data is based on modern developments and effective tools. To access data and view query results, a viewer will be built on top of the geospatial database. It also looks at how data can be viewed administratively: as two-dimensional (traditional GIS interface) and three-dimensional (perspective, stereo) and in combination with each other in the same environment. The studied provisions make a significant contribution to the development of cadastral systems, the formation of cadastral data, the creation and management of geospatial databases of three-dimensional cadastre. Keywords: land plot parameters, three-dimensional cadastre, cadastral data, legal restrictions, real estate object, geospatial database, real estate registration.

Traditional environmental knowledge and transport efficiency of a communal canal network, Tafilalt oasis, Morocco: A historical GIS analytics perspective

The provision of water in the arid Tafilalt – the largest natural oasis in Morocco – changed little over a span of centuries, though the network of surface canals and khettara (qanats) gradually expanded to yield more water to a growing oasis. The introduction of newer water technologies (pumped wells and dams) in the 1970s reduced the availability of water to the oasis but has not diminished reliance on the traditional canal network. All agriculture in this Sahara oasis is irrigated and most water is still distributed through an intricate system of earthen canals incrementally built over centuries and maintained to this day. Here, a traditional environmental knowledge (TEK) and transportation GIS framework is used to explore the efficacy of this common property canal network. Aerial photographic and field survey, interviews with users and managers of canal waters, and GIS network analysis were employed to examine the functional characteristics and communal issues inherent in the canal system, and to assess the efficiency and effectiveness of the traditional system as a water delivery network. GIS analytics employed to assess the distributive utility of the canal network include the Detour Index, Alpha Index, Beta Index, Gamma Index, and Network Density. The first four indices address the efficiency and connectivity of the complex canal network while Network Density assesses the effectiveness of the system in distributing water throughout the oasis. Results demonstrate the strength and adaptability of local TEK, revealing that the historic canal system was incrementally but not haphazardly constructed, and the system that evolved exhibits an efficiency of water distribution closely approximating the efficiency that could be obtained using advanced design and modern planning methods.

Quantitative Morphometric 3D Terrain Analysis of Japan Using Scripts of GMT and R

In this paper, we describe two related scripting methods of cartographic data processing and visualization that provide 2D and 3D mapping of Japan with different algorithm complexity. The first algorithm utilizes Generic Mapping Toolset (GMT), which is known as an advanced console-based program for spatial data processing. The modules of GMT combine the functionality of scripting with the aspects of geoinformatics, which is especially effective for the rapid analysis of large geospatial datasets, multi-format data processing, and mapping in 2D and 3D modes. The second algorithm presents the use of the R programming language for cartographic visualization and spatial analysis. This R method utilizes the packages ‘tmap’, ‘raster’, ‘maps’, and ‘mapdata’ to model the morphometric elements of the Japanese archipelago, such as slope, aspect, hillshade and elevation. The general purpose graphical package ‘ggplot2’ of R was used for mapping the prefectures of Japan. The two scripting approaches demonstrated an established correspondence between the programming languages and cartography determined with the use of scripts for data processing. They outperform several well-known and state-of-the-art GIS methods for mapping due to their high automation of data processing. Cartography has largely reflected recent advances in data science, the rapid development of scripting languages, and transfer in the approaches of data processing. This extends to the shift from the traditional GIS to programming languages. As a response to these new challenges, we demonstrated in this paper the advantages of using scripts in mapping, which consist of repeatability and the flexible applicability of scripts in similar works.

Study on the characteristics of the potential rise of shell under VFTO in the GIS pumped storage power station

Compared with the traditional GIS station, complex wiring and operation mode of pumped-storage plant diversity, working condition of frequent transformation, makes the plant to produce very fast transient overvoltage (VFTO) probability increase greatly. VFTO will form a transient coupled to the GIS shell casing transient earth voltage (TEV), and then to station personnel safety and the reliability of the secondary equipment poses a great threat. But at the moment because of the limitation of testing technology such as understanding of the academic study of TEV is lack of system. Based on a typical 500 kV gis pumped-storage power station, this paper established a system equivalent model of GIS, network frame and calculates the amplitude, frequency spectrum characteristic of the TEV and influence on VFTO characteristics, and analyzes the fault location, power supply mode, branch bus, the influence of vertical grounding electrodes on the TEV characteristics, For the characteristics of the TEV rules and parameters. Research on TEV characteristics of GIS of pumped storage power station has important reference value for further perfecting relevant theories.

PHOTOGRAMMETRIC PROCESSING AND FRUITION OF PRODUCTS IN OPEN-SOURCE ENVIRONMENT APPLIED TO THE CASE STUDY OF THE ARCHAEOLOGICAL PARK OF POMPEII

Abstract. The paper presents the geomatic survey campaign carried out in the Domus V of Pompeii Archaeological site, the photogrammetric processing of the collected images and the following fruition of the deriving products deploying open-source software. Among all the produced results, the orthophotos of the vertical walls of one of the Domus V rooms are made available through a “master/slave” GIS environment, where each orthophoto is uploaded in a “slave” project whose visualization is triggered by querying the corresponding geometry representing the wall in the “master” project. This strategy allows to include the display of the third dimension, i.e., the altimetric one, within a traditional GIS environment, so to constitute a 3D GIS representation. This is particularly useful to realize a catalogue of all the archaeological site in the future to be viewed, queried and exploited also by non-specialists in geomatics or archaeology fields of knowledge.

RINX: A SOLUTION FOR INFORMATION EXTRACTION FROM BIG RASTER DATASETS

Abstract. Processing Earth observation data modelled in a time-series of raster format is critical to solving some of the most complex problems in geospatial science ranging from climate change to public health. Researchers are increasingly working with these large raster datasets that are often terabytes in size. At this scale, traditional GIS methods may fail to handle the processing, and new approaches are needed to analyse these datasets. The objective of this work is to develop methods to interactively analyse big raster datasets with the goal of most efficiently extracting vector data over specific time periods from any set of raster data. In this paper, we describe RINX (Raster INformation eXtraction) which is an end-to-end solution for automatic extraction of information from large raster datasets. RINX heavily utilises open source geospatial techniques for information extraction. It also complements traditional approaches with state-of-the- art high-performance computing techniques. This paper discusses details of achieving big temporal data extraction with RINX, implemented on the use case of air quality and climate data extraction for long term health studies, which includes methods used, code developed, processing time statistics, project conclusions, and next steps.

Types and historical roles of secret gates: a new understanding of the Ming Great Wall based on a digital heritage survey

ABSTRACT The database of images of the Ming Great Wall, along with on-site investigation and verification, has allowed more than 120 secret gate sites to be located and classified for the first time. Based on typological analyses of the relationship between each type of secret gate’s form and function, we created a classification system of the Ming Great Wall’s secret gates. By combining traditional historical textual research, site surveys, and GIS micro-terrain analysis, we found that secret gates, as a key element, integrate other facility types into a coordinated regional defense system, revealing several active and flexible defense modes and more sophisticated planning intentions than were initially recognized. Finally, based on historical textual research, we found that secret gates played a broad and positive role in political interaction and socio-economic exchanges between the Ming Dynasty and Mongolian tribes, offering a new perspective on the historical function and the “open” or “closed” nature of the Ming Great Wall. Moreover, the methods introduced are likely to be applicable to the examination of other types of cultural route heritage.

Seismicity in the Afar Depression and Great Rift Valley, Ethiopia

Integrated mapping is essential in geological studies to assess risks of earthquake hazards. Cartographic techniques have become a commonplace approach to visualizing data in the continuous geologic and geophysical fields. However, traditional GIS mapping is a manual process with a time-consuming workflow that can lead to mistakes and misinterpretation of data. This study applied two mapping approaches to address this problem: Generic Mapping Tools (GMT) used for automated cartographic workflow employing scripts and QGIS used for traditional geologic mapping. The study area includes Ethiopia, notable for its complex geologic setting. The study aimed to analyse the relationships between the geophysical, geological, topographic and seismic setting of the country by presenting six new thematic maps:1 topography based on the GEBCO/SRTM15+ high-resolution grid;2 geological units with consistent lithology and age from the USGS database;3 geological provinces with major Amhara Plateau and Somali Province using USGS data;4 geoid based on the Earth Gravitational Model 2008 (EGM-2008) grid;5 free-air gravity anomaly model using satellite-based remote sensing data;6 seismicity showing earthquakes and volcanos from 05/03/1990 to 27/11/2020.The comparison of the topography, seismicity, geophysics and surface geology of the Afar Depression and the Great Rift Valley was based partly on extant literature on the geologic setting of Ethiopia which primarily focuses upon discussing tectonic processes that took place in the East African Rift System in the past. The current study contributes to the previous research and increases cartographic data on the geology and geophysics of Ethiopia. The outcomes can be implemented in similar regional projects in Ethiopia for geophysical and geological monitoring.

BIG EARTH DATA PROCESSING USING MACHINE LEARNING FOR INTEGRATED MAPPING OF THE DEAD SEA FAULT, JORDAN

In this research, an integrated framework on the big Earth data analysis has been developed in the context of the geomorphology of Jordan. The research explores the correlation between several thematic datasets, including machine learning and multidisciplinary geospatial data. GIS mapping is widely used in geological mapping as the most adequate technical tool for data visualization and analysis. GIS applications encourage geological prospective modeling by visualizing data aimed at the prognosis of mineral resources. However, automatization using machine learning for big Earth data processing provides the speed and accurate processing of multisource massive datasets. This is enabled by the application of scripting and programming in cartographic techniques. This study presents the combined machine learning methods of cartographic analysis and big Earth data modeling. The objective is to analyze a correlation between the factors affecting the geomorphological shape of Jordan with respects to the Dead Sea Fault and geological evolution. The technical methodology includes the following three independent tools: 1) Generic Mapping Tools (GMT); 2) Selected libraries of R programming language; 3) QGIS. Specifically, the GMT scripting program was used for topographic, seismic and geophysical mapping, while QGIS was used for geologic mapping and R language for geomorphometric modeling. Accordingly, the workflow is logically structured through these three technical tools, representing different cartographic approaches for data processing. Data and materials include multisource datasets of the various resolution, spatial extent, origin and formats. The results presented cartographic layouts of qualitative and quantitative maps with statistical summaries (histograms). The novelty of this approach is explained by the need to close a technical gap between the traditional GIS and scripting mapping, which is wider for big data mapping and where the crucial factors are speed and precision of data handling, as well as effective visualization achieved by the machine graphics. The paper analyzes the underlying geologic processes affecting the formation of geomorphological landforms in Jordan with a 3D visualization of the selected fragment of the Dead Sea Fault zone. The research presents an extended description in methodology, including the explanations of code snippets from the GMT modules and examples of the use of R libraries ‘raster’ and ‘tmap’. The results revealed strong correlation between the geological and geophysical settings which affect geomorphological patterns. Integrated study of the geomorphology of Jordan was based on multisource datasets processed by scripting. A thorough analysis presented regional correlations between the geomorphological, geological and tectonic settings in Jordan. The paper contributed both to the development of cartographic engineering by introducing scripting techniques and to the regional studies of Jordan including the Dead Sea Fault as a special region of Jordan. The results include 12 new thematic maps including a 3D model.

A Performance Comparison between GIS-based and Neuron Network Methods for Flood Susceptibility Assessment in Ayutthaya Province

Flooding has been a long withstanding issue in Thailand. Due to its geographical setup, mitigation and management of floods are challenging and hard to execute. One of the tools used in managing the events is “flood susceptibility mapping,” in which an incident probability as well as a rescue path is estimated and planned. To create one, the traditional GIS method called FRAM (flood risk assessment model), combined with AHP (analytical hierarchy process), is used and implemented on ArcGIS software. In this method, we first created a comparison table to compute weights for each of the selected factors. Then the computed weights were used in the FRAM model in ArcGIS to create a flood susceptibility map for each region. Each region was then classified as very high, high, medium, low, and very low risk. On the other hand, in computer science, machine learning and AI are prevalent and being adopted to various domains, promising the effectiveness of the method, potentially beat the forementioned traditional method. Therefore, ANN (artificial neural network) is adopted in this work to create the flood susceptibility map. The ANN technique is developed by using causal factors. The ANN classifies areas as either flood areas or flood-free areas. The 2 methods from different disciplines (GIS and Computer Science) are applied and described in this paper with the intention to prove whether the machine learning is really efficient and can outperform the traditional GIS approach. Data on Thailand’s Ayutthaya Province is used in this work as a case study - in order to assess flood prone areas and compared for performance evaluation. Both of which use the 6 selected factors according to the literature: (i) flow accumulation, (ii) elevation, (iii) land use, (iv) rainfall intensity, (v) slope and (vi) soil types. The results from the 2 methods were verified with historical flood data and compared. The results showed that ANN (obtained via sensitivity analysis) outperformed the FRAM with precision of 79.90 %, recall of 79.04 %, F1-score of 79.08 % and accuracy of 79.31 %. In addition, we found that (according to our ANN experiments) the main causal factors related to flood susceptibility map only included 3 factors: flow accumulation, elevation, and soil types. Therefore, the proposed methodology for assessment of flood susceptibility areas using these 3 factors could be considered sufficient and applied to other regions in related applications, when needed.

Georeferencing 3D Tiles Generated from Photogrammetry-Derived Mesh Using Ground Control Points

In order to achieve efficient visualization of massive 3D photogrammetry models on the web-based virtual globe, original photogrammetry 3D models should be converted into 3D Tiles with accurate georeferencing information to render on the correct surface position. There are a few software tools and online platforms for generating 3D Tiles and various ways to generate 3D Tiles were reported, but none of them do not provide a way to georeference 3D Tiles with GCPs. This paper is intended to suggest an approach to georeference 3D Tiles with GCPs based on the Kabsch algorithm which is very similar to georeferencing raster data with GCPs in the traditional GIS. First, we mathematically describe how to apply the Kabsch algorithm for georeferencing 3D Tiles with GCPs. Following this description, we demonstrate our implementation allowing constructing GCPs and calculating the “transform” property of 3D Tiles. Finally, we tested the suggested method in Construkted Reality which is the platform to stream and visualize massive photogrammetry and point clouds datasets.

An integrated Artificial Intelligence and GIS spatial analyst tools for Delineation of Groundwater Potential Zones in complex terrain: Fincha Catchment, Abay Basi, Ethiopia

In this paper, the performance of Artificial Intelligence (AI) in Geospatial analysis and GIS platforms for the prospecting of potential groundwater zones was evaluated in Fincha catchment, Abay, Ethiopia. Components of geospatial data under morphometric, hydrologic, permeability, and surface dynamic change were confirmed as the criteria for prospecting groundwater potential zones. The influence of the individual criterion was ranked and weighted in Artificial Neural Networks (ANN) training model and Analytical Hierarchy Process (AHP). The correctness of the weights fixed in the ANN and AHP was evaluated with target data assigned to the networks and consistency index (CI) respectively. The weighted overlay analysis in the GIS environment was implemented to generate the promising zones in both approaches (ANN and GIS). The results obtained in the ANN model and GIS were evaluated based on pumping rate and ground-truthing points. Groundwater potential zones of five and four classes were delineated in AI and GIS techniques respectively, and this is an indicator for the effectiveness of AI in geospatial analysis for prospecting of potential zones than the traditional GIS technique. The percentage of accuracy in both methods was measured from the ROC curve and AUC. Therefore, it was found that the delineated groundwater potential zones and the ground-truthing points were agreed with 96% and 91% in the AI and GIS platforms respectively. Finally, it is concluded that the ANN model is an effective tool for the delineation of groundwater prospective zones.

Scripting methods in topographic data processing on the example of Ethiopia

This study evaluates the geomorphometric parameters of the topography in Ethiopia using scripting cartographic methods by applying R languages (packages 'tmap' and 'raster') and Generic Mapping Tools (gmt) for 2D and 3D topographic modelling. Data were collected from the open source repositories on geospatial data with high resolution: gebco with 15 arc-second and etopo1 with 1 arc-minute resolution and embedded dataset of srtm 90 m in 'raster' library of R. The study demonstrated application of the programming approaches in cartographic data visualization and mapping for geomorphometric analysis. This included modelling of slope steepness, aspect and hillshade visualized using dem srtm90 to derive geomorphometric parameters of slope, aspect and hillshade of Ethiopia and demonstrate contrasting topography and variability climate setting of Ethiopia. The topography of the country is mapped, including Great Rift Valley, Afar Depression, Ogaden Desert and the most distinctive features of the Ethiopian Highlands. A variety of topographical zones is demonstrated on the presented maps. The results include 6 new maps made using programming console-based approach which is a novel method of cartographic visualization compared to traditional gis software. The most important fragments of the codes are presented and technical explanations are provided. The presented series of 6 new maps contributes to the cartographic data on Ethiopia and presents the methodology of scripting mapping techniques.

Scripting methods in topographic data processing on the example of Ethiopia

This study evaluates the geomorphometric parameters of the topography in Ethiopia using scripting cartographic methods by applying R languages (packages 'tmap' and 'raster') and Generic Mapping Tools (GMT) for 2D and 3D topographic modelling. Data were collected from the open source repositories on geospatial data with high resolution: GEBCO with 15 arc-second and ETOPO1 with 1 arc-minute resolution and embedded dataset of SRTM 90 m in 'raster' library of R. The study demonstrated application of the programming approaches in cartographic data visualization and mapping for geomorphometric analysis. This included modelling of slope steepness, aspect and hillshade visualized using DEM SRTM90 to derive geomorphometric parameters of slope, aspect and hillshade of Ethiopia and demonstrate contrasting topography and variability climate setting of Ethiopia. The topography of the country is mapped, including Great Rift Valley, Afar Depression, Ogaden Desert and the most distinctive features of the Ethiopian Highlands. A variety of topographical zones is demonstrated on the presented maps. The results include 6 new maps made using programming console-based approach which is a novel method of cartographic visualization compared to traditional GIS software. The most important fragments of the codes are presented and technical explanations are provided. The presented series of 6 new maps contributes to the cartographic data on Ethiopia and presents the methodology of scripting mapping techniques.