Geographic Information System Datasets Research Articles

Groundwater assessment for sustainable development in the Wadi Al-Hamd Basin, Al-Madinah Al-Munawarah, KSA

This study relied on well monitoring many drilled wells data in the Wadi Al-Hamd Basin to determine water level and chemical properties from field measurements. The characteristics of groundwater must be assessed in terms of present and future eases and stresses to ensure sustainable development. Hydrogeological, hydrochemical, groundwater quality indices and Geographic Information Systems (GIS) data sets have been integrated to assess the groundwater aquifer in the study area. To implement this work, we measured the depth to the water table from eighty-eight drilled wells. Also, the major ions were determined from 82 water samples to evaluate the physicochemical properties, groundwater facies, and controlling mechanisms using diagrams such as the modified Piper proposed by Chadha, Total Ionic Salinity (TIS), and Gibbs. In this study, five irrigation water quality indicators (IWQI), including total dissolved solids (TDS), sodium adsorption ratio (SAR), potential salinity (PS), magnesium hazard (MH), and residual sodium carbonate (RSC) have been determined to assess the suitability of groundwater for irrigation. Based on the findings of this study, groundwater flows from the southeast and northwest to the west and south, respectively. In this basin, 56% of water samples have TDS greater than 2000 mg/L, while 62% of water points are categorised as Na–Cl water type, which indicates that evaporation and rock–groundwater interaction influenced groundwater quality evolution. Almost 87% of samples were rated good to excellent for irrigation use with respect to SAR. On the other hand, all water points are suitable for irrigation based on RSC. Likewise, groundwater is suitable for irrigation purposes despite the relatively high PS levels (>5 meq/L in about 49% of samples) and some small spots with high levels of MH (>50% in about 12% of samples), especially in the southern and central portions of the studied basin.

Interlinking BIM and GIS data for a semantic pedestrian network and applications in high-density cities

In high-density cities, pedestrians frequently traverse many publicly accessible indoor spaces, such as metro stations and footbridges, which seamlessly connect with outdoor sidewalks, forming indoor-outdoor combined pedestrian networks. However, the information island phenomenon hinders these connections in the digital world because outdoor sidewalks and indoor spaces are modelled using Geographical Information Systems (GIS) and Building Information Modelling (BIM) technologies, respectively, which challenges obtaining integrated information for intelligent pedestrian services. This study presents an approach for interlinking BIM and GIS data for a semantic pedestrian network using semantic web technologies. The proposed approach automatically converts BIM and GIS data into linked data and establishes interlinkages between BIM and GIS data through semantic queries and inferences. The resulting semantic pedestrian network based on integrated linked data graphs forms a knowledge base, including topological and geometrical information and abundant semantic information from BIM and GIS datasets. The application potential of the semantic pedestrian network is demonstrated through information query and semantic route planning. This research contributes to establishing indoor-outdoor combined semantic pedestrian networks ensuring seamless data integration.

Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation

Images captured by drones are increasingly used in various fields, including geographic information management. This study evaluates a procedure that incorporates active learning semantic segmentation for verifying the building registration ledger. Several semantic segmentation techniques were evaluated to extract building information, with ResNet identified as the most effective method for accurately recognizing building roofs. Using active learning, the training data were refined by removing instances with low similarity, leading to improved network performance of the model. The procedure was demonstrated to identify discrepancies between the building information system and the inferred label images, as well as to detect labeling errors on a training dataset. Through this research, the geographic information system dataset is enhanced with minimal human oversight, offering significant potential for urban planning and building detection advancements.

Scan to BIM Mapping Process Description for Building Representation in 3D GIS

This paper introduces a novel approach for mapping process description with Scan data to Building Information Modeling (BIM) in a 3D Geographic Information System (GIS). The methodology focuses on automatically generating building mass and facade information on the GIS platform using Point Cloud Data (PCD) of Airborne Laser Scanning (ALS). Advanced scanning techniques capture detailed geometry from the physical site and generate high-resolution point clouds, which are processed to create 3D models for GIS integration. The critical contribution of this research lies in a scalable Scan to BIM mapping process, which can be used for generating building footprints and masses, including attributes, on 3D GIS. The resulting integrated BIM–GIS dataset provides an accurate building mass, facade information, facility asset management, and architectural design and facilitates improved decision-making in urban planning based on enhanced visualization, analysis, and simulation. This study suggests a flexible Scan to BIM mapping process description based on use cases, including algorisms. Through prototype development, a case study demonstrates the effectiveness of the process approach, the automatic generation of BIM on a 3D GIS platform, and reducing the manual efforts. The proposed method automatically creates DEM, SHP, GeoJSON, IFC, and coordinate system information from scan data and can effectively map building objects in 3D GIS.

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we propose a framework for historical city reconstruction. By integrating procedural modeling techniques and machine learning models within a Geographic Information System (GIS) framework, our pipeline allows for effective management of spatial data and the generation of detailed 3D models. We developed an open-source Python module that fills gaps in 2D GIS datasets and directly generates 3D models up to LOD 2.1 from GIS files. The use of the CityJSON format ensures interoperability and accommodates the specific needs of historical models. A practical case study using footprints of the Old City of Jerusalem between 1840 and 1940 demonstrates the creation, completion, and 3D representation of the dataset, highlighting the versatility and effectiveness of our approach. This research contributes to the accessibility and accuracy of historical city models, providing tools for the generation of informative 3D models. By incorporating machine learning models and maintaining the dynamic nature of the models, we ensure the possibility of supporting ongoing updates and refinement based on newly acquired data. Our procedural modeling methodology offers a streamlined and open-source solution for historical city reconstruction, eliminating the need for additional software and increasing the usability and practicality of the process.

Assessment and Spatial Analysis of Land Use and Land Cover (LULC) Dynamics in the Black Volta Basin

The Black Volta Basin, a critical hydrological region, is experiencing rapid changes in Land Use and Land Cover (LULC) patterns due to diverse anthropogenic and environmental factors. This study employs a comprehensive Multi-Sensor Remote Sensing and Geographic Information System (GIS) methodology to assess and analyze the spatiotemporal dynamics of LULC in the basin. Utilizing data from multiple remote sensing platforms, including optical and radar sensors, as well as historical GIS datasets, a detailed LULC classification is performed. The integration of multi-temporal satellite imagery enables the detection and quantification of changes across distinct land cover categories. Through rigorous spatial analysis and statistical techniques, the study reveals the extent and magnitude of LULC transformations over a defined period. Land cover transitions are identified, characterized, and linked to potential drivers such as urbanization, agricultural expansion, and natural processes. The outcomes of this research contribute to an enhanced understanding of the evolving LULC dynamics in the Black Volta Basin, shedding light on both natural and human-induced changes. The results underscore the significance of a multi-sensor approach in accurately capturing complex LULC changes and their implications for regional sustainability and resource management. This study's insights serve as valuable inputs for policymakers, researchers, and stakeholders engaged in land-use planning, environmental conservation, and sustainable development within the Black Volta Basin and similar ecologically sensitive areas.

Investigating the application of a commercial and residential energy consumption prediction model for urban Planning scenarios with Machine Learning and Shapley Additive explanation methods

Building energy forecasting methodologies utilized by municipal governments tend to be geared heavily towards depicting broader qualitative representations of regional change and are in need of complementary data-driven models that can produce quantitatively reliable depictions of future energy consumption at the neighborhood-level. The current research demonstrates an application of a Machine Learning (ML) model in the form of an Extreme Gradient Boosting (XGBoost) algorithm for forecasting the energy use of commercial and residential buildings. The methodology serves to improve on municipal scenario planning by providing a more spatially granular representation of future energy use. In this way, city government and urban planners can more accurately set carbon emission benchmarks and target specific locales for sustainability initiatives. The second major contribution of the study is to demonstrate how scenario planning approaches can utilize existing Machine Learning techniques to compensate for gaps in the data. This work is developed through a case study of Philadelphia. The study begins with the construction of residential and commercial energy models for the year 2015 and corresponding models for the year 2045. The forecast models integrate regional socioeconomic trends from the Delaware Valley Regional Planning Commission (DVRPC) scenario of Enduring Urbanism. The commercial energy use model is developed from the DVRPC’s open-source Geographic Information System (GIS) datasets, the Commercial Buildings Energy Consumption Survey (CBECS), and CoStar commercial real estate data. The residential model applies the Residential Energy Consumption Survey (RECS), the Public Use Microdata Sample (PUMS), and Census Bureau American Community Survey (ACS) estimates. A corresponding SHAP (SHapley Additive exPlanations) analysis is implemented to pinpoint feature contributions to the model’s energy estimates. By using the PopGen software, the model’s energy estimates could be analyzed at the household level, the smallest possible scale. To provide a useful resource for key stakeholders, the study aggregates model output by Traffic Analysis Zone (TAZ) and Public Use Microdata Area (PUMA) to display a detailed forecast of energy use. The results indicate that the DVRPC Enduring Urbanism trends in income and employment do not significantly affect energy consumption for the study area. However, features related to lower building intensity (e.g., lower square footage, fewer floors per building) were associated with reduced energy use in both models. Additionally, the study found residential buildings under the “single-family attached” zoning designation to correspond with higher energy estimates.

Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression

Flood has long been known as one of the most catastrophic natural hazards worldwide. Mapping flood-prone areas is an important part of flood disaster management. In this study, a flood susceptibility mapping framework was developed based on a novel integration of nature-inspired algorithms into support vector regression (SVR). To this end, various remote sensing (RS) and geographic information system (GIS) datasets were applied to the hybridized SVR models to map flood susceptibility in Ahwaz township, Iran. The proposed framework has two main steps: 1) updating the flood inventory (historical flooded locations) using the proposed RS-based flood detection method developed within the google earth engine (GEE) platform. The mosaicked images of multi-temporal Sentinel-1 synthetic aperture radar (SAR) data have been used in this step; 2) producing flood susceptibility map using the standalone SVR and hybridized model of SVR. The hybridized methods were derived from a novel integration of SVR with meta-heuristic algorithms, hence forming the SVR-bat algorithm (SVR-BA), SVR-invasive weed optimization (SVR-IWO), and SVR-firefly algorithm (SVR-FA). A spatial database of flood locations and 11 conditioning factors (altitude, slope angle, aspect, topographic wetness index, stream power index, normalized difference vegetation index (NDVI), distance to stream, curvature, rainfall, soil type, and land use/cover) were built for the susceptibility modelling. The accuracy of the proposed model was evaluated using the statistical and sensitivity indices, such as root mean square error (RMSE), receiver operating characteristic (ROC) and area under the ROC curve (AUROC) index. The results indicated that all hybridized models outperformed the standalone SVR. According to AUROC values, the predictive power of the SVR-FA was the highest with the value of 0.81, followed by SVR-IWO, SVR-BA, and SVR with values of 0.80, 0.79, and 0.77, respectively.

Development of the spectral forest index in the Khangai region, Mongolia using Sentinel-2 imagery

Mongolian forests have low productivity and growth and are vulnerable to disturbances. Additionally, it is difficult to control and evaluate the forested areas. Therefore, satellite data and surveillance methods are needed to study mountain forests. This study aimed to determine the changes in the main forest cover classes of Khangal soum using remote sensing and geographical information system datasets. A spectral forest index (SFI) using Sentinel-2 imagery was developed for forest cover estimations and applied to the study area during 2015–2020. The SFI was based on the forest index (FI) and the concept of Dark Objects. Each SFI was compared to existing vegetation indices (ratio vegetation index, normalized difference vegetation index, leaf area index, and forest index) for forest data analysis. The highest correlation was with SFI2. The SFI2 data agreed with the national forest inventory (NFI) 2018 data. The SFI2 of the forest area was set at 1.2, which was confirmed with 90.4% confidence. Overall, SFI2 is suitable for land cover/land use changes and forest classification, monitoring, and management in Mongolia and could be crucial for estimating the boundary of forested areas depending on the forest cover and species in the region.

Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operation

ABSTRACT As the human’s role in the operation of maritime autonomous surface ships (MASSs) is concentrated on less manpower, several issues have been raised regarding the capacity of single manpower. This indicates the necessity of developing monitoring technology for abnormal navigational situations to prevent maritime accidents. Since boating under the influence (BUI) of alcohol is one of the major causes of maritime accidents in Korea, this study focused on BUI of alcohol as abnormal navigation to be monitored. The model suggests a methodology for detecting BUI ships based on their trajectory and behavior. The trajectory and behavior-related features are extracted using AIS and geographic information system datasets and clustered to the anomaly and normal navigation patterns. The proposed model can aid the decision-making of humans monitoring the MASS in detecting abnormal ships in the vicinity of MASSs.

An Integrated Terrain and Clutter Propagation Model for 1.7 GHz and 3.5 GHz Spectrum Sharing

Over the past decade, spectrum sharing has evolved into viable commercial systems poised to meet the ever-increasing spectrum demand from service providers and end users. The key to the success of these systems is accurate and reliable propagation models that will simultaneously maximize the number of users able to access the spectrum and minimize the interference to incumbent or protected users. The classical propagation models currently utilized by these systems—the irregular terrain model (ITM) and extended hata (eHata)—do not account for features, such as foliage or endpoint clutter that can have a large impact on propagation loss. This article presents a measurement-based framework for updating these classical models and proposes a new integrated terrain and clutter model using publicly available geographic information system datasets. Over 400 000 path loss measurements were recorded in nine diverse locations across the United States at 1.7 and 3.5 GHz. Our updates improved the rms difference between measurements and model by 3–7 dB for ITM and 3–14 dB for eHata. In addition, we demonstrate several integrated terrain and clutter models that have rms differences ranging from 9.5 to 17.8 dB. Finally, cross validation was used to demonstrate the generalizability of our models to a wide variety of propagation environments.

Geospatial modelling of overlapping habitats for identification of tiger corridor networks in the Terai Arc landscape of India

Wildlife corridors in a landscape include local vegetation, topography, prey base, water and are associated with isolated wildlife habitat patches. They facilitate maintenance of ecological structure and function as well as provide connectivity to faunal populations supporting genetic transfers, and are elements critical to wildlife management. In this work, habitat patches for tiger, both inside as well as outside of Protected Areas have been identified by developing a Habitat Suitability Index model utilizing Remote Sensing and Geographical Information System datasets for the Terai Arc landscape, India. By using a computational approach based on the framework of theory of complex networks, for exclusively pairwise interactions between the habitat patches, a potential tiger corridor network has been structurally identified and studied in this landscape. The interactions between these habitat patches on a spatial scale has been analyzed as a clique of the corridor network. Further, the Clique Percolation Method has been applied to detect overlapping communities of habitat patches in the landscape. The Cliques required for maintaining contiguity between the habitat patches in order to support tiger movement are validated using field observations of tiger communities within the landscape matrix. The model developed for identification of tiger corridors in this study could potentially be of a vital importance for wildlife stakeholders to better understand and manage tiger populations both within and outside of protected areas. The study also highlights Critical Habitat Patches and their importance in maintaining landscape connectivity for tiger dispersal in the landscape. Using a report published by the Government of India as a benchmark, the model presented in the work is found to have an accuracy of 90.73% in predicting tiger carrying patches and the corridor network in the focal landscape.

Spatially calibrating polycyclic aromatic hydrocarbons (PAHs) as proxies of area burned by vegetation fires: Insights from comparisons of historical data and sedimentary PAH fluxes

Many regions worldwide have experienced increasing wildfire activity in recent years and climate changes are predicted to result in more frequent and severe fires. Reconstruction of past fire activity offers paleoenvironmental context for modern and future burning. Pyrogenic polycyclic aromatic hydrocarbons (PAHs) have been increasingly used as a molecular biomarker for fire occurrence in the paleorecord and offer opportunity for nuanced reconstructions of fire characteristics. A suite of PAHs is produced during combustion, and the emission amount and assemblage is influenced by many variables including fuel type, fire temperature, and oxygen availability. Despite recent advances in understanding the controls and taphonomy of these biomass burning markers, the spatial scale of this proxy is unknown. In this paper, measurements of PAH fluxes preserved in a lake sediment archive from the Sierra Nevada, California were compared with a historical geographic information system (GIS) dataset of area burned up to 150 km distance from the lake to determine the spatial scales for which these biomarkers are reliable proxies of biomass burning. The PAH fluxes in the Swamp Lake sediments record a change in the relative anthropogenic and pyrogenic sourcing of PAHs. Anthropogenic pollution sources could explain why some PAHs (fluoranthene (Fl), pyrene (Py), benz[a]anthracene (BaA), retene (Ret), benzo[a]pyrene (BaP), dibenzo[a,h]anthracene (DA) and ideno[1,2,3-cd]pyrene (IP)) did not correlate with area burned within 150 km. This indicates that individual PAHs may have different efficacies in recording area burned and be more susceptible to masking of fire signals by pollution sources. Despite these complications, we find that the PAHs naphthalene (Na), acenaphthene (Ace), fluorene (F), and anthracene (An) are reliable local proxies of area burned (within 40 km), whereas the PAHs phenanthrene (Phe), chrysene (Ch), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), and benzo[g,h,i]perylene (Bghi) are both reliable local (within 36 km) as well as more regional (as much as 75 km for phenanthrene (Phe), chrysene (Ch), and benzo[g,h,i]perylene (Bghi) or 150 km for benzo[b]fluoranthene (BbF) and benzo[k]fluoranthene (BkF)) area burned proxies. Comparisons of PAH fluxes with charcoal accumulation rates in the same sediments suggest that pyrogenic particulate transport modulates low to mid-molecular weight PAHs via adsorption. Overall, the results show that PAH records integrate a combination of spatial signals of area burned and measurement of individual PAHs may enable cross-scale paleofire reconstructions.

Investigating the suitability of GIS and remotely-sensed datasets for photovoltaic modelling on building rooftops

Rising energy demands and net-zero targets have led to development and implementation of renewable technologies. Rooftop photovoltaic (PV) solar panels offer a viable solution while minimising complex construction or excessive infrastructure development. However, critical physical factors influence building suitability to maximise cost-benefit requirements. This research adopted a Geographic Information System (GIS) approach to identify building suitability by analysing and comparing Digital Surface Models (DSM) derived from Light Detection and Ranging (LiDAR) and orthophotography data using UK standardised PV formulas. Geospatial workflows processed rooftop features and modelled outputs for solar irradiation, panel type, kWh, CO2, payback and costs while 3D models and solar web applications were used to validate results. Both models suggested a range of between 14.2 and 15.2 GWh potential for an installed capacity of between 21.1 and 22.3 MW. Residential models met between 62.1% (LiDAR) and 66.6% (orthophotography) of average consumer demand with PV potential exceeding 94% of residential dwellings. LiDAR and orthophotography models had strong agreement with existing PV installations. The methodology can be scaled to a regional level and expanded for larger PV capacity. Moreover, the process can assist policymakers with informed decisions on renewable technologies alongside developments such as Peer to Peer (P2P) solar trading.

Rebuilding Cultural Ethos in Urban Storefronts: Two Arts Districts in Dallas-Fort Worth, Texas

Dallas-Fort Worth, the fastest growing region in Texas, has been criticized for decades for auto-centric and corporate development, which has restricted the sustainable design of cultural venues in need of permeable access to the community. Meanwhile, the city's arts districts – Bishop Arts District and Deep Ellum, officially designated as Cultural Districts in the Dallas Cultural Plan – are known for their clusters of murals, live music venues, and independent businesses, which exemplify the transformation of the forgo en historic streetcar corridors into bohemian cultural destinations. This paper examines the revitalization of Deep Ellum and the Bishop Arts District that have both become successful local destinations with walkable commercial streets. First, we review the literature on cultural placemaking in a post-industrial urban context. Next, using US Census data, we explore the socio-demographic environment of the two neighbourhoods. Then, synthesizing a Geographic Information System (GIS) data set and Google Street imagery, we analyse storefronts that have been modi fied for cultural use. Urban development of cultural districts shows that reusing storefronts can and should be contextually adapted to the existing built landscape and socio-cultural environment to satisfy both communities' needs and to enhance local economic growth. In view of cultural placemaking, the city should develop a policy that increases pedestrian and transit access to cultural districts, aff ordable housing, and spaces for artists and residents, and spurs innovative, often post-retail, cultural storefront repurposing through community engagement in order to build incremental, long-term resilience.

Landslide variation with morphometric factors using the GIS techniques: The case of Shaqlawa Forest

This study deals with the application of geographical information system (GIS) datasets and methods to assess the landslide susceptibility in Wadi Hujran. The area has a rocky terrain and belongs to the Shaqlawa district of the Kurdistan Region of Iraq. The region is placed towards the Northeast side of Erbil city. The region covers an area of 18.56 Km2 (1856.1 ha) and consists of rough broken and stones. The watershed area is surrounded by North latitudes 36° 21' 53.514" to 36° 17' 49.7796" and East longitudes 44° 17' 5.658" to 44° 20' 9.06". Three factors, namely the morphometric, geological, and environmental, were used to prepare the landslide susceptibility index. The study made use of AHP method and prepared a landslide susceptibility map. Data related to geology, topography, hydrology, rainfall, and land use were used to prepare the map. Physical and statistical methods were used to validate the map. A heuristic approach was incorporated to produce the final susceptibility map. ArcGIS software was used to generate the landslide zones. A total of five landslide zones were generated, which varied from very low landslide zones (80.5) to very high landslide zone (84.5). The zones also included low landslide zone (1262.2), moderate landslide zone (1505.9), and high landslide zone (566.8), and the ratio of consistency in the present study was 0.06 AHP less than 1, and all the five zones in the study were compiled landslide zonation estimated.

Associations between Children's Physical Activity and Neighborhood Environments Using GIS: A Secondary Analysis from a Systematic Scoping Review.

Regular participation in physical activity is essential for children’s physical, mental, and cognitive health. Neighborhood environments may be especially important for children who are more likely to spend time in the environment proximal to home. This article provides an update of evidence for associations between children’s physical activity behaviors and objectively assessed environmental characteristics derived using geographical information system (GIS)-based approaches. A systematic scoping review yielded 36 relevant articles of varying study quality. Most studies were conducted in the USA. Findings highlight the need for neighborhoods that are well connected, have higher population densities, and have a variety of destinations in the proximal neighborhood to support children’s physical activity behaviors. A shorter distance to school and safe traffic environments were significant factors in supporting children’s active travel behaviors. Areas for improvement in the field include the consideration of neighborhood self-selection bias, including more diverse population groups, ground-truthing GIS databases, utilising data-driven approaches to derive environmental indices, and improving the temporal alignment of GIS datasets with behavioral outcomes.

An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet

Predictions and estimations are very important for agriculture applications. The estimation results on crop production may have a huge impact in the economy of a country by changing their export and import data. The estimation of crop production was started by collecting information manually from the fields and analyzing it using a computer. However, the accuracy was not up to the mark due to the error caused by manual collection of data. The Geographic Information System (GIS) applications are developed to store the information observed from the satellite images on change detection in town planning, disaster management, business development and vegetation management. The proposed work estimates the crop production of Indian states from a GIS dataset with a SqueezeNet algorithm. The performance of the SqueezeNet algorithm is compared with the traditional Inception and ResNet algorithms.

A bit closer to the past: the three-dimensionalization of an Inca ceremony in the north Calchaquí Valley (Salta, Argentina)

<p>Like other expansive polities, the expansion of the Inca empire across the highlands and lowlands of South America is not only a history of trade and warfare, but also of mesmerizing public performances that yielded new and memorable experiences. During highly ritualized public celebratory events, the local polities gained first-hand access to the imperial liturgy, which was vital to promote and legitimate the Inca cosmology across the newly acquired lands. Especially in the last 20 years, new technologies, an ever-growing corpus of archaeological data, as well as increasing hardware capacity and software development, make it possible to emulate the scenes that people got to witness during the Inca public events, at a home computer scale and without complex and expensive equipment. Furthermore, it prompts us to test and apply new tools and academic dissemination techniques, perhaps more suitable to current technologies and means of knowledge storage and circulation. This article presents the process of building a three-dimensional (3D) model that, on the one hand, combines historical, ethnographic, and archaeological data with Geographic Information System (GIS) datasets; on the other hand, it uses detailed architectural analysis and astronomical measurements. The objective is to yield renders that accurately display the atmospheric and lighting conditions prevailing when the site was inhabited. We will offer a detailed description of all methods, techniques, equipment, and software used to create the model and the parameters for rendering the images. The authors intend to exemplify how 3D modelling goes well beyond the 3D model as a product in itself; it becomes a fundamental tool that encouraged us to test new variables and discuss new interpretations about this settlement. Results indicate that its builders designed these settlement's Inca compounds to show off the imperial capabilities and constructive proficiency, to convey exceptional, memorable experiences to its residents and visitors, and to stage explicit links between the imperial representatives and some fundamental procreative components of the Andean cosmos. In doing so, Guitián's plaza served to stage and communicate the privileged role the imperial representatives claimed to have in a broader cosmological scheme.</p><p>Highlights:</p><ul><li><p>Inca public performances were finely choreographed so that objects, places, people, landscape, and skyscape features interacted according to the main principles of imperial cosmology.</p></li><li><p>Current mid-range hardware and specialized yet reasonably user-friendly software are suitable to create accurate three-dimensional (3D) models combining historical, archaeological, and astronomical data.</p></li><li><p>Creating such a detailed 3D model contributes to cultural heritage and academic dissemination and prompted us to revise and broaden our interpretations.</p></li></ul>

Landslide variation with morphometric factors using the GIS techniques: The case of Shaqlawa Forest

This study deals with the application of geographical information system (GIS) datasets and methods to assess the landslide susceptibility in Wadi Hujran. The area has a rocky terrain and belongs to the Shaqlawa district of the Kurdistan Region of Iraq. The region is placed towards the Northeast side of Erbil city. The region covers an area of 18.56 Km2 (1856.1 ha) and consists of rough broken and stones. The watershed area is surrounded by North latitudes 36° 21' 53.514" to 36° 17' 49.7796" and East longitudes 44° 17' 5.658" to 44° 20' 9.06". Three factors, namely the morphometric, geological, and environmental, were used to prepare the landslide susceptibility index. The study made use of AHP method and prepared a landslide susceptibility map. Data related to geology, topography, hydrology, rainfall, and land use were used to prepare the map. Physical and statistical methods were used to validate the map. A heuristic approach was incorporated to produce the final susceptibility map. ArcGIS software was used to generate the landslide zones. A total of five landslide zones were generated, which varied from very low landslide zones (80.5) to very high landslide zone (84.5). The zones also included low landslide zone (1262.2), moderate landslide zone (1505.9), and high landslide zone (566.8), and the ratio of consistency in the present study was 0.06 AHP less than 1, and all the five zones in the study were compiled landslide zonation estimated.