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In rapidly developing urban areas, land use changes frequently occur in conjunction with population growth. The determination of the existing land use structure via traditional methods, including field studies and remote sensing techniques, is an exceedingly time-consuming, costly, and labor-intensive process. To address this issue, this paper proposes an innovative model based on Point of Interest (POI) data, Thiessen polygons and Geographic Information Systems (GIS) in the process of identifying land use functions. The model’s workflow comprises data collection, spatial geodatabase design, data pre-processing, the construction of geoprocessing workflows with ModelBuilder, analysis, urban function identification, and model verification steps. This study employs Thiessen polygons to analyze the topological and spatial relationships among 127,265 geotagged POIs in Ankara, the capital of Türkiye. The model achieved a Kappa value of 0.82 and an overall accuracy of 84.5%, demonstrating a high level of reliability. Following this analysis, functional density ratios were calculated based on the distribution of POIs to identify areas characterized by either dominant or mixed land use. The proposed methodology is expected to contribute significantly to urban planning efforts by providing insights into the utilization patterns of urban land. Furthermore, this model has the potential to function as a decision support system, aiding city planners in the effective management and development of urban spaces by delineating existing functional areas.

The recent advances in location tracking technologies and the widespread use of location-aware applications have resulted in big datasets of moving object trajectories. While there exists a couple of research prototypes for moving object databases, there is a lack of systems that can process big spatiotemporal data. This work proposes HadoopTrajectory, a Hadoop extension for spatiotemporal data processing. The extension adds spatiotemporal types and operators to the Hadoop core. These types and operators can be directly used in MapReduce programs, which gives the Hadoop user the possibility to write spatiotemporal data analytics programs. The storage layer of Hadoop, the HDFS, is extended by types to represent trajectory data and their corresponding input and output functions. It is also extended by file splitters and record readers. This enables Hadoop to read big files of moving object trajectories such as vehicle GPS tracks and split them over worker nodes for distributed processing. The storage layer is also extended by spatiotemporal indexes that help filtering the data before splitting it over the worker nodes. Several data access functions are provided so that the MapReduce layer can deal with this data. The MapReduce layer is extended with trajectory processing operators, to compute for instance the length of a trajectory in meters. This paper describes the extension and evaluates it using a synthetic dataset and a real dataset. Comparisons with non-Hadoop systems and with standard Hadoop are given. The extension accounts for about 11,601 lines of Java code.

The development of exploratory spatial data analysis methods is an active research domain in the field of geographic information science (GIS). At the same time, the coupled space-time attributes of economic phenomena are difficult to be represented and examined. Both GIS and economic geography are faced with the challenges of dealing with the temporal dynamics of geographic processes and spatial dynamics of economic development across scales and dimensions. This paper thus suggests a novel way to generalize the characteristics and the structure of space-time data sets, using regional economic data as the example. Accordingly, a reasonable number of general questions (data analysis tasks) can be abstracted. Then, tools (methods) may be suggested on that basis. The cross-fertilization between exploratory spatial data analysis (ESDA) and spatial economics is also identified and illustrated by the capabilities of these components, which have uncovered some interesting patterns and trends in the spatial income data of China and the United States. Through exploratory analysis of economic data, the detection of rich details of underlying geographical and temporal processes would be the first step toward such cross-fertilization. In addition, this exploratory analytical framework can be applied to other data sets that are also measured for areal units at multiple points in time.

The issue of property evaluation and appraisal has been of high interest for private and public agents involved in the housing industry for the purposes of trade, insurance and tax. This paper aims to investigate how different factors related to the location of a property affect its price over time. The predictive models applied in this research are driven by real estate transactions data of Tehran Metropolitan Area, captured from open data available to the public. The parameters of the functions that describe the behavior of the housing market are estimated through applying different types of statistical models, including ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR). This suite of models has been run in order to compare their efficiency and accuracy in predicting the variations in housing price. The GTWR model showed significantly better performance than OLS and GWR models, as the goodness of fit index (adjusted R²) improved by 22 percent. Therefore, spatio-temporal non-stationary modelling is significant in the explanation of the variations in housing value and the GTWR coefficients were found more reliable. Three internal factors (size of building; building age; building quality), and eight external factors (topography; landuse mix; population density; distance to city center; distance to subway station; distance to regional parks; distance to highway; distance to airport) influence the property price, either positively or negatively. Moreover, using significant variables that extracted from regression models, the optimum number of housing value clusters is generated using the spatial 'k' luster analysis by tree edge removal (SKATER) method. Five clusters of housing patterns were recognized. The policy implication of this paper is grouping of Metropolitan Tehran housing value data into five clusters with different characteristics. The varying factors influencing housing value in each cluster are different, making this data analysis technique useful for policy-makers in the housing sector.

Stimulants are considered the first-line treatment for Attention Deficit Hyperactivity Disorder (ADHD) in the US and they are used in other indications. Stimulants are also diverted for non-medical purposes. Ethnic and regional differences in ADHD diagnosis and in stimulant use have been identified in earlier research. The objectives of this report were to examine the pharmacoepidemiological pattern of these controlled substances over the past decade and to conduct a regional analysis. Data (drug weights) reported to the US Drug Enforcement Administration's Automation of Reports and Consolidated Orders System for four stimulants (amphetamine, methylphenidate, lisdexamfetamine, and methamphetamine) were obtained from 2006 to 2016 for Unites States/Territories. Correlations between state level use (mg/person) and Hispanic population were completed. Amphetamine use increased 2.5 fold from 2006 to 2016 (7.9 to 20.0 tons). Methylphenidate use, at 16.5 tons in 2006, peaked in 2012 (19.4 tons) and subsequently showed a modest decline (18.6 tons in 2016). The consumption per municipality significantly increased 7.6% for amphetamine and 5.5% for lisdexamfetamine but decreased 2.7% for methylphenidate (all p < .0005) from 2015 to 2016. Pronounced regional differences were also observed. Lisdexamfetamine use in 2016 was over thirty-fold higher in the Southern US (43.8 mg/person) versus the Territories (1.4 mg/person). Amphetamine use was about one-third lower in the West (48.1 mg/person) relative to the Northeastern (75.4 mg/person, p < .05) or the Midwestern (69.9 mg/person, p ≤ .005) states. States with larger Hispanic populations had significantly lower methylphenidate (r(49) = -0.63), lisdexamfetamine (B, r(49) = -0.49), and amphetamine (r(49) = -0.43) use. Total stimulant usage doubled in the last decade. There were dynamic changes but also regional disparities in the use of stimulant medications. Future research is needed to better understand the reasons for the sizable regional and ethnic variations in use of these controlled substances.

The accurate delineation of urban agglomeration boundary is conductive to not only the better understanding of the development relationship between cities in urban agglomeration but also to the guidance of regional functions as well as the formulation of regional management policies. At the same time, the fusion of land relations and urban internal relations can greatly improve the accuracy of the delineation of urban agglomeration boundary. Still, for all that, previous studies delineated the boundary only from the perspective of land relations. In this study, firstly, wavelet transform is used to fuse Night-time Light data (NTL), POI (Point of Interest) data and Tencent Migration data, respectively. Then, the image is segmented by multiresolution segmentation to delineate the urban agglomeration boundary of GBA. Finally, the results are verified. The results show that the accuracy of urban agglomeration boundary delineated by NTL data is 85.57%, with the Kappa value as 0.6256, respectively. While, after fusing POI data, the accuracy is 88.97%, with the Kappa value as 0.7011, respectively. What is more, the accuracy of delineating urban agglomeration boundary by continuous fusion of population movement data reaches 93.60%, and that of Kappa value as 0.8155. Therefore, it can be concluded that compared with delineating the boundary of urban agglomeration only based on land relations, the fusion of population movement data of urban agglomerations by wavelet transform strengthens the interconnection between cities in urban agglomeration and contributes to the accurate division of urban agglomeration boundaries. What is more, such accurate delineation not only has important practical value for optimizing the spatial structure of urban agglomerations, but also assists in the formulation of regional management and development planning policies.

In smart cities, ensuring road safety and optimizing transportation efficiency heavily relies on streamlined road condition monitoring. The application of Artificial Intelligence (AI) has notably enhanced the capability to detect road surfaces effectively. This study presents a novel approach to road condition monitoring in smart cities through the development of an acoustic data processing and analysis module. It focuses on four types of road conditions: smooth, slippery, grassy, and rough roads. To assess road conditions, a microphone integrated road surface detector unit is designed to collect audio signals, and an ultrasonic module is used to observe the road depth information. The whole hardware unit is installed in the wheel rim of the vehicles. The data collected from the road surfaces are then analyzed using machine learning algorithms, such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrate the effectiveness of the proposed method in accurately identifying different road conditions. From these results, it was observed that the MLP provides better accuracy of 98.98% in assessing road conditions. The study provides valuable insights into the development of a more efficient and reliable road condition monitoring system for delivering secure transportation services in smart cities.

This investigation was designed to analyze alterations in functional connectivity across brain networks associated with cognitive fatigue through electroencephalogram (EEG) data analysis. Through the application of both global and local graph-theoretical metrics to characterize the topology of brain networks, this study establishes a conceptual framework supporting enhanced detection of cognitive fatigue manifestations while facilitating examination of its neurophysiological substrates. The study cohort comprised neurologically intact individuals aged 20-35 years, recruited from Beijing Rehabilitation Hospital, Capital Medical University between February 6 and September 30, 2024 for participation in a cognitive fatigue induction task. Following acquisition of written informed consent, data before and after the task were obtained, including both subjective fatigue assessments using the Visual analog scale for fatigue (VAS-F) scores and EEG data. The preprocessed EEG signals were segmented into three frequency bands: θ (4-8 Hz),α (8-13 Hz), and β (13-30 Hz). To determine the frequency band exhibiting maximal sensitivity to cognitive fatigue, cross-band comparative power spectral density (PSD) was implemented. The selected frequency band subsequently served as the basis for weighted Phase Lag Index (wPLI) computation, yielding a functional connectivity matrix derived from wPLI measurements. Network topology was evaluated through application of five global graph theory metrics (global efficiency [Eg], local efficiency [Eloc], clustering coefficient [Cp], shortest path length [Lp], and small-world property [Sigma]) complemented by two local graph theory metrics (nodal efficiency [NE] and degree centrality [DC]). This analytical framework enabled systematic comparison of connectivity patterns and topological characteristics between before and after cognitive fatigue states. Statistical analysis revealed significant post-fatigue elevations in global average PSD across all examined frequency bands: α (p < 0.001), θ (p < 0.001), and β (p = 0.004). The α band demonstrated the most pronounced effect size (Cohen's d = 4.23, r = 0.90). Topological analysis of α-band wPLI networks showed enhanced Eg (p = 0.005), Eloc (p < 0.001), and Cp (p < 0.001), whereas Lp displayed significant reduction (p = 0.005). Regional analysis revealed preferential enhancement of NE, particularly in central and anterior cortical regions. The experimental data indicated that α-band activity exhibited the highest sensitivity to cognitive fatigue induced by the sustained Stroop task, establishing a framework for accurate identification of fatigue states. Cognitive fatigue compensatory mechanisms manifested as concurrent improvements in both local and global neural information processing efficiency. Although such adaptive reorganization may compromise overall network efficiency, these findings implied an inherent balance between adaptive network reconfiguration and system efficiency. These results elucidated novel neurophysiological mechanisms underlying cognitive fatigue, substantially advancing our understanding of brain network dynamics during prolonged cognitive demand.

Context Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications. Objectives The review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research. Methods We conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function. Results We summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software. Conclusion We observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics.