Spatial Database Research Articles

Crypto-Spatial: A New Direction in Geospatial Data

Abstract. Geospatial technology involves the use of maps, satellite imagery, and spatial databases to collect, analyze, and visualize geographic data. Understanding the link between blockchain and geospatial technology is crucial as it opens the door to innovation and progress in both fields. “The use of Blockchain as a system for managing and verifying geospatial data necessitates accuracy, integrity, and trustworthiness”(Pengxiang et al 2022). Improving data integrity, promoting collaboration, and revolutionizing applications in the geospatial domain are all possible through the combination of these two technologies. The World Wide Web (WWW) has expanded connections worldwide, but it also makes networks susceptible to multiple attacks from various anonymous sources. Packets of fixed or variable sizes are used for transmitting data across nodes. Data is encrypted using secure algorithms at the application level, then packetized and sent at lower levels in the OSI architecture. The encrypted data can be obtained by an intruder by organizing the data contents of each packet once they have access to all the packets. Intruders may also attempt to break the secured algorithm used by the sender. This sophisticated approach to Data Security is the content and order of data, ensuring that unauthorized individuals are unable to discern sensitive information during data transmission. The primary objective of this paper is to introduce a novel spatial encryption technique designed to enhance the security of data transfers within networks. This paper, founded on a comprehensive literature review, endeavors to propose a research project focused on exploring futuristic solutions, particularly within the post-quantum era.

Uncovering Patterns: Data Mining the Deforestation Frontier in Brazilian Cerrado

Abstract. The Brazilian Cerrado, recognized as the world's most diverse tropical savanna, has undergone significant land clearance for agriculture, cattle ranching, and charcoal production, leading to considerable loss of its natural vegetation. This deforestation disrupts major aquifers, affecting ecosystems and human populations. The Cerrado Deforestation Monitoring Project (PRODES), active since 2017, systematically maps new deforestation using satellite imagery. This study used GeoDMA, a geospatial data mining tool for analyzing complex spatial databases such as those provided by the PRODES. The study area is the Alto Parnaíba region within the MATOPIBA agricultural frontier, characterized by high rates of crop expansion and burned areas. Using PRODES deforestation data from 2000 to 2023, the study categorizes deforestation patterns into geometric, multidirectional, and diffuse types, associated with large-scale agriculture, smallholder farming, and subsistence agriculture, respectively. The analysis reveals that approximately 50% of land use trajectories are small-scale subsistence agriculture, predominantly in northern Alto Parnaíba. Unchanged large-scale farms are concentrated in the southern part of the study area, linked to annual crops. The transition from small to large-scale farms is significant in the western region. Protected areas, especially strictly protected conservation units, show minimal changes in native vegetation. This study highlights the expansion of large-scale agriculture in MATOPIBA, emphasizing the need for targeted interventions to manage land use changes and prevent further deforestation. Future research should extend this approach to other Cerrado ecoregions to improve the detection and analysis of deforestation processes.

A study on the spatial distribution and optimization strategies of day care centers for the elderly

Background: At present, the degree of ageing in China is becoming severe, especially in Shanghai, which is the largest city with the highest degree of aging in China. How to adjust the community to effectively meet the needs of the elderly groups has become the focus of social attention. Objectives: In this study, spatial analysis was used to explore the matching degree of the elderly population with the distribution of day care centers in Xuhui District, Shanghai, China, to explore the problems, and to propose optimization strategies with a view to providing feasible suggestions for the construction of old-age daycare. Methods: This research adopts the methods of literature study, map analysis, questionnaire survey, especially tries to use the spatial analysis software of Arc GIS in order to set up a spatial database of Xuhui District's elderly population based on street administrative territorial entity. Results: The results show that the quantity and quality of the day care centers in Xuhui District can not meet the actual needs of the elderly. Secondly, the services provided in daycare centers deviate from the actual needs of older population. Thirdly, the day care service content lists are quite homogeneous, which neglects the diversified and personalized needs . Main Contribution to Evidence-Based Practice: From the perspective of spatial distribution study, this article enriches the theory of the construction and management of the elderly care facilities in urban areas, and provides reference for the formulation of aging policy. Meanwhile, it gives some suggestions on optimizing the spatial distribution of community facilities to fulfil the actual needs for optimal matching of the elderly care resources.

Leveraging SAR and Optical Remote Sensing for Enhanced Biomass Estimation in the Amazon with Random Forest and XGBoost Models

Abstract. This study addresses the challenge of estimating above-ground biomass (AGB) in the Amazon rainforest by developing a reference geographical database, which provides the ground truth, and comparing the relative importance of using Synthetic Aperture Radar (SAR) and optical remote sensing data to automatically infer AGB. In the experiments reported in this article, we assessed how those two remote sensing data sources impact the accuracy of AGB estimates produced by regression models built with Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The research involved compiling a comprehensive database from many available forest inventories, integrating parcel- and tree-level data to enable precise biomass estimation. The methodology included setting up a spatial data analysis environment, standardizing data, and implementing an experimental protocol with feature selection and leave-one-out cross-validation. The results demonstrate that both kinds of data, i.e., SAR and optical, and their combination can be used for estimating AGB, providing valuable insights for forest management and climate change mitigation efforts. The reference database is available upon request to the corresponding authors.

An Updated Review on the Spatial Distribution of Borrelia burgdorferi Sensu Lato Across Ticks, Animals and Humans in Northeastern China and Adjacent Regions.

Lyme disease is a tick-borne zoonotic disease caused by Borrelia burgdorferi sensu lato and is prevalent in northeastern Asia, particularly in the forested area of Northeastern China. However, a lack of systematic data on the spatial distribution of B. burgdorferi in this region hinders the prediction of its transmission risk across the landscape. To provide an updated overview and establish a comprehensive spatial distribution database, we conducted a systematic review of literature published between 2000 and 2022. We collected and compiled relevant data on B. burgdorferi in Northeastern China and its neighbouring regions, outlining its distribution in ticks, wild animals, livestock and humans. Spatial analysis was performed to identify spatial clusters of tick positivity and host infection rates. From a total of 1823 literature, we selected 110 references to compile 626 detection records of B. burgdorferi, including 288 in ticks, 109 in wildlife, 111 in livestock and domestic animals and 100 in humans. The average detection rate of B. burgdorferi in ticks was approximately 20%, with wildlife, livestock and domestic animal host positivity rates below 50% and human seroprevalence rates varying from 0.94% to 44.18%. The study identified the presence of 17 tick species and ten genotypes of B. burgdorferi in the region, indicating a broad distribution. Notably, B. burgdorferi exhibited notable clustering, particularly in the central and eastern areas of Jilin Province, warranting further investigation.

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities.Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

SpatialRef: a reference of spatial omics with known spot annotation.

Spatial omics technologies have enabled the creation of intricate spatial maps that capture molecular features and tissue morphology, providing valuable insights into the spatial associations and functional organization of tissues. Accurate annotation of spot or domain types is essential for downstream spatial omics analyses, but this remains challenging. Therefore, this study aimed to develop a manually curated spatial omics database (SpatialRef, https://bio.liclab.net/spatialref/), to provide comprehensive and high-quality spatial omics data with known spot labels across multiple species. The current version of SpatialRef aggregates >9 million manually annotated spots across 17 Human, Mouseand Drosophila tissue types through extensive review and strict quality control, covering multiple spatial sequencing technologies and>400 spot/domain types from original studies. Furthermore, SpatialRef supports various spatial omics analyses about known spot types, including differentially expressed genes, spatially variable genes, Gene Ontology (GO)/KEGG annotation, spatial communication and spatial trajectories. With a user-friendly interface, SpatialRef facilitates querying, browsing and visualizing, thereby aiding in elucidating the functional relevance of spatial domains within the tissue and uncovering potential biological effects.

Efficient Distance Queries on Non-point Data

Distance queries, including distance-range queries, k -nearest neighbors search, and distance joins, are very popular in spatial databases. However, they have been studied mainly for point data. Inspired by a recent approach on indexing non-point objects for rectangular range queries, we propose a secondary partitioning approach for space-partitioning indices, which is appropriate for distance queries. Our approach classifies the contents of each primary partition into 16 secondary partitions, taking into consideration the begin and end values of objects with respect to the spatial extent of the primary partition. Based on this, we define algorithms for three popular spatial query types, that avoid duplicate results and skip unnecessary computations. We compare our approach to the previous secondary partitioning method and to state-of-the-art data-partitioning indexing and find that it has a significant performance advantage.

A Comprehensive Spatially Resolved Metabolomics Dataset for Lampreys

As one of the two most ancient groups of extant vertebrates, lamprey has become an important model organism in various fields of biology. In this paper, we present a comprehensive tissue-wide spatial metabolomics dataset for lampreys, where 14 distinct tissues were analyzed using liquid chromatography-mass spectrometry (LC-MS) in both positive and negative ion modes. The dataset has been fully validated using internal standard and pooled quality control samples and is readily accessible at the UCSD Metabolomics Workbench. This dataset serves as a valuable resource for researchers using lampreys as a model organism. Additionally, it acts as a benchmark metabolomics dataset for evaluating new algorithms and software tools and comparing them with previously published results. A lamprey spatial metabolomics database is also provided to support studies utilizing lampreys as an animal model, and to complement and validate other spatial metabolomics studies on lampreys conducted with mass spectrometry imaging or other techniques.

Development of a georeferenced atlas of energy renovation in residential buildings using GIS tool

Abstract This paper presents a method for modelling the energy needs for heating the residential building stock on a macro scale. The method is based on the use of Geographic Information Systems (GIS) tools to manage various existing geolocalised databases and the collection of field data. It combines the calculation of energy demand with the estimation of solar gains received by the building envelope. In order to estimate the heat energy balance, a description of the building geometry is necessary. The geometrical characteristics of the envelope are extracted from the building entities information available in the spatial database. These parameters are then enriched and cross-referenced with other data gathered from the government’s complex taxation files. The TABULA buildings typology was chosen because it is an exhaustive European classification adapted for use in GIS utilities. The objective of this work is to produce a georeferenced atlas that can identify potential energy savings on large zones, such as urban areas. An understanding of energy consumption at this scale is crucial for the effective management of massively scaled energy-saving solutions. The atlas will promote energy efficiency in the residential sector and guide environmental policy measures by identifying priority areas for renovation.

Amazonian amphibians: diversity, spatial distribution patterns, conservation and sampling deficits.

The Amazon biome is home to the largest tropical forest on the planet and has the greatest global biodiversity on Earth. Despite this, several less charismatic taxonomic groups, such as amphibians, lack comprehensive studies on their species richness and spatial distribution in the Amazon Region. In this study, we investigated: i) patterns of richness and endemism of Amazonian amphibians across geopolitical and biogeographic divisions, ii) similarities between different Amazonian bioregions, iii) temporal trends in amphibian sampling, iv) conservation status of amphibians according to assessments of the IUCN and v) the importance of diverse data sources in building a robust database of amphibian occurrences. We aggregated data from four different sources: publicly accessible platforms, peer-reviewed articles, grey literature and fieldwork inventories spanning 15 years (2007-2021), ultimately compiling 160,643 records of 947 species across 7,418 sampled sites. The greatest diversity of species was found in Peru, Brazil and Ecuador, with notable amphibian diversity and endemism in regions such as the western basins and the Tapajós River Basin in the central-southern Amazon. Geographical analysis of species diversity revealed four distinct groups defined by latitudinal (the Amazon River) and longitudinal (the Juruá, Madeira and Tapajós Rivers) gradients, with low species similarity (< 40%), particularly in the basins of north-western Amazonia. Amphibian sampling in the Amazon has intensified since the 1950s with the establishment of important research centres such as INPA and the GOELD Museum in the Brazilian Amazon. Approximately 18% of Amazonian amphibian species face extinction risk, according to IUCN assessments, highlighting the need for comprehensive data sources to understand and conserve species in this megadiverse region. Our findings suggest that river systems likely influence Amazonian amphibian species composition due to biogeographic history, emphasising the need for robust taxonomic and spatial databases. This study, therefore, contributes a valuable large-scale dataset for Amazonian amphibians, guiding future research and strategies for amphibian conservation.

Leveraging Open-Source Geographic Databases to Enhance the Representation of Landscape Heterogeneity in Ecological Models.

Wildlife abundance and movement are strongly impacted by landscape heterogeneity, especially in cities which are among the world's most heterogeneous landscapes. Nonetheless, current global land cover maps, which are used as a basis for large-scale spatial ecological modeling, represent urban areas as a single, homogeneous, class. This often requires urban ecologists to rely on geographic resources from local governments, which are not comparable between cities and are not available in underserved countries, limiting the spatial scale at which urban conservation issues can be tackled. The recent expansion of community-based geographic databases, for example, OpenStreetMap (OSM), represents an opportunity for ecologists to generate large-scale maps geared toward their specific research needs. However, computational differences in language and format, and the high diversity of information within, limit the access to these data. We provide a framework, using R, to extract geographic features from the OSM database, classify, and integrate them into global land cover maps. The framework includes an exhaustive list of OSM features describing urban and peri-urban landscapes and is validated by quantifying the completeness of the OSM features characterized, and the accuracy of its final output in 34 cities in North America. We portray its application as the basis for generating landscape variables for ecological analysis by using the OSM-enhanced map to generate an urbanization index, and subsequently analyze the spatial occupancy of six mammals throughout Chicago, Illinois, USA. The OSM features characterized had high completeness values for impervious land cover classes (50%-100%). The final output, the OSM-enhance map, provided an 89% accurate representation of the landscape at 30m resolution. The OSM-derived urbanization index outperformed other global spatial data layers in the spatial occupancy analysis and concurred with previously seen local response trends, whereby lagomorphs and squirrels responded positively to urbanization, while skunks, raccoons, opossums, and deer responded negatively. This study provides a roadmap for ecologists to leverage the fine resolution of open-source geographic databases and apply it to spatial modeling by generating research-specific landscape variables. As our occupancy results show, using context-specific maps can improve modeling outputs and reduce uncertainty, especially when trying to understand anthropogenic impacts on wildlife populations.

Application of UAV airborne LiDAR technology in highway engineering construction

Abstract The airborne LiDAR technology of UAVs is an efficient and high-precision spatial information acquisition technology. This paper introduces the airborne LiDAR technology of UAV in detail and studies the application of airborne LiDAR technology of UAV in highway construction based on the sixth section of the Leibokahalo-Yongshan connection line of G4216 Yibin Xinshi to Panzhihua Section. Engineering application practice shows that this technology can solve the difficult problem of obtaining basic geographic information data, and verify the rationality of the scheme and the matching degree with the field environment. In addition, compared with traditional survey technology, the technology has obvious advantages in the construction of highways in complex terrain, which helps to improve the quality of survey and design.

Geospatial Intelligence for Landslide Susceptibility and Risk Analysis: Insights from NH31A and East Sikkim Himalaya Settlements

Slope instability is a serious concern in the Sikkim Himalayas. The town and numerous road segments along National Highway 31A were ravaged by multiple landslides that occurred in the nearby region. A bivariate statistical method known as frequency ratio (FR), information value (IV), and certainty factor (CF) analysis was employed in this work to examine landslide risk assessment (LRA) and landslide susceptibility zonation (LSZ) maps in the Rorachu watershed. This study represents the first comprehensive analysis of landslide risk in the populated areas of East Sikkim and along NH31A, offering a deeper understanding of the risks involved and contributing to the enhancement of local resilience against landslide hazards. A total of 153 different landslide locations were mapped using Google Earth and GIS software; 30% (46) of these locations were used to validate the models, and 70% of these (107) served as training data for the FR, IV, and CF models. The thirteen landslide causative factors (geology, soil, elevations, slope, curvature, drainage density (DD), road density (RD), rainfall, normalized difference vegetation index (NDVI), land use land cover (LULC), topographic position index (TPI), stream power index (SPI), and topographic wetness index (TWI)) were extracted from a spatial database for LSZ mapping. Landslides were most prevalent on slopes (35° to 50°), heights (2,500–4,100 m), and rainfall (2000–2,500 mm and 3,000–3,300 mm). The area under the curves (AUC) for the FR, IV, and CF models are 0.925 (92.50%), 0.846 (84.60%), and 0.868 (86.20%), respectively. The prediction rates are shown by the AUCs for the FR, IV, and CF models, which are 0.828 (82.8%), 0.750 (%), and 0.836 (83.60%), respectively. According to the landslide risk assessment (LRA), the FR (20.75%), IV (40.91%) and CF (18.78%) models showed high risk on Highway 31A, while the FR (9.05%), IV (38.59%) and CF (20.90%) models showed high risk in densely populated areas. These landslide risk and vulnerability maps can be used to develop land use planning strategies that can save lives and are useful for planners and mitigation measures. Special attention should be paid to urbanization, highway construction, and deforestation.

Graft Failure and Contralateral ACL Injuries After Primary ACL Reconstruction: An Analysis of Risk Factors Using Interpretable Machine Learning.

Anterior cruciate ligament (ACL) reconstruction (ACLR) can be successful in restoring knee stability. However, secondary ACL injury, either through graft failure or contralateral injury, is a known complication and can significantly impact the ability of a patient to return successfully to previous activities. To develop and internally validate an interpretable machine learning model to quantify the risk of graft failure and contralateral ACL injury in a longitudinal cohort treated with ACLR. Case-control study; Level of evidence, 3. An established geographic database of >600,000 patients was used to identify patients with a diagnosis of ACL rupture between 1990 and 2016 with a minimum 2-year follow-up. Medical records were reviewed for relevant patient information and 4 candidate machine learning algorithms were evaluated for prediction of graft failure and contralateral ACL injury in patients after ACLR as identified either on magnetic resonance imaging or via arthroscopy. Performance of the algorithms was assessed through discrimination, calibration, and decision curve analysis. Model interpretability was enhanced utilizing global variable importance plots and partial dependence curves. A total of 1497 patients met inclusion criteria. Among them, 140 (9.4%) had graft failure and 128 (8.6%) had a contralateral ACL injury after index surgery at a median follow-up of 140.7 months (interquartile range, 77.2-219.2 months). The best performing models achieved an area under the receiver operating characteristics curve of 0.70 for prediction of graft failure and 0.67 for prediction of contralateral ACL injury, outperforming a logistic regression fitted on the identical feature set. Notable predictors for increased risk of graft failure included younger age at injury, body mass index (BMI) <30, return to sports <13 months, initial time to surgery >75 days, utilization of allograft, femoral/tibial fixation with suspension/expansion devices, concomitant collateral ligament injury, and active or former smoking history. Predictors of contralateral ACL injury included greater preoperative pain, younger age at initial injury, BMI <30, active smoking history, initial time to surgery >75 days, history of contralateral knee arthroscopies, and involvement in contact sports. Less than 18% of all patients who undergo ACLR should be expected to sustain either a graft failure or contralateral ACL injury. Machine learning models outperformed logistic regression and identified greater preoperative pain, younger age, BMI <30, earlier return to higher activity, and time to surgical intervention >75 days as common risk factors for both graft failure as well as contralateral ACL injury after ACLR. Surgeon-modifiable risk factors for graft failure included allograft and femoral/tibial fixation with a suspension/expansion combination.

Long-term Outcomes of Complete Tears of the Distal Biceps Tendon: An Analysis of Surgical Management at a Median Follow-up of 14.7 Years.

Surgical repair of full thickness biceps tears has demonstrated adequate outcomes in short and mid-term studies. However, data on the long-term outcomes of full thickness distal biceps injuries and their treatment are currently lacking. The purpose of this study was to report on patient demographics, injury characteristics, and long-term outcomes for patients with full-thickness distal biceps tears. It was hypothesized that complete distal biceps tears managed operatively would demonstrate robust clinical success at long-term follow-up. Case series; Level of evidence, 4. Patients with magnetic resonance imaging-confirmed, complete distal biceps tendon rupture sustained between 1996 and 2016 were identified. Patients were cross-referenced with a regional geographic database. A total of 66 patients (3 female, 63 male) with a median age of 50.8 years (IQR, 41.5-60.4) and a median clinical follow-up of 14.7 years (IQR, 9.6-17.9 years) were included. Patients who sustained a full-thickness distal biceps tendon tear were likely to be in their early 50s, male, right-hand dominant, current/former smokers, and laborers with a history of traumatic injury during an intentional movement. Most of these patients had pain and supination weakness but no loss of range of motion. All included tears were treated operatively. At final follow-up, patients maintained a majority of near-normal range of motion (median total arc of flexion/extension 140° and supination/pronation 80°), excellent elbow flexion strength (91% of patients had full strength), and adequate elbow supination strength (76% of patients had full strength). The overall complication rate was 24%, with 16 out of 66 patients experiencing some type of complication between infection, rerupture, heterotopic ossification, reoperation, and nerve complications. Overall return to work was 98%, and 85% of those who returned to work did so without restrictions. Complete tears of the distal biceps were most common in patients 50 years of age, male sex, right-hand dominant, and current/former smokers. The most common profession was laborer, and injuries were primarily traumatic in nature during intentional activity. Patients managed operatively demonstrated high rates of success at long-term follow-up with respect to elbow function and clinical outcomes.

SPATIAL EVOLUTION OF SMART CITIES FOR SUSTAINABLE TOURISM: A CASE STUDY OF PHUKET PROVINCE, THAILAND

In this research, the objective is to study the Spatial Evolution of Smart Cities for Sustainable Tourism in Phuket Province, Thailand from 2005-2024, using the Geo-informatic Technique as information for decision-making in determining the direction of Smart City. The study results found that land use patterns such as Urban and built-up land, Waterbodies, and Forest land have had an increasing trend over the past 20 years. In 2005, Urban and built-up land was found to cover 132.19 km2 (24.10% of the total area). In 2024, it was found to have greatly increased, covering 165.91 km2 (30.25% of the total area), especially in Thalang District, which is north of Phuket. The area has a wide plain landscape, and beautiful wide beaches such as Bang Tao Beach. There has been a change from agricultural land to housing developments, residential areas, restaurants, hotels, resorts and homestays. The results of this study have been prepared as a spatial database for supporting decision-making in monitoring and planning the development of areas to support the expansion of sustainable tourism in the future to become a smart city.

Guardians of the aquifers: ehancing Rome’s groundwater monitoring network

This paper proposes a methodology for managing groundwater monitoring directly in the field through a specific data entry system installed on mobile devices and a relational geographic database that allows the visualization and querying of data via a specific web interface. The study area is the city of Rome, where a monitoring system of approximately 150 piezometers and wells, currently manually monitored twice a year. The proposed method uses the Enterprise cloud platform (ESRI, 2024) managed by ISPRA-SNPA, which guarantees the repository of the data collected in an online cloud system and the use of Web applications. The data of the time series of levels and chemical-physical parameters such as temperature, pH, conductivity are freely accessible through attribute tables, graphs, and viewer elements. The results highlight numerous possibilities for expanding the network of active wells, enabling the use of groundwater resources for adaptation measures to address ongoing climate change.