Spatial-temporal Analysis Research Articles

Multi-scenario Simulation and Spatial-temporal Analysis of LUCC in China’s Coastal Zone Based on Coupled SD-FLUS Model

Multi-scenario Simulation and Spatial-temporal Analysis of LUCC in China’s Coastal Zone Based on Coupled SD-FLUS Model

Digital Image Correlation and Ultrasonic Lamb Waves for the Detection and Prediction of Crack-Type Damage in Fiber-Reinforced Polymer Composite Laminates.

The possibility of using the Digital Image Correlation (DIC) technique, along with Lamb wave analysis, was investigated in this study for damage detection and characterization of polymer carbon fiber (CFRP) composites with the help of numerical modeling. The finite element model (FEM) of the composite specimen with artificial damage was developed in ANSYS and validated by the results of full-field DIC strain measurements. A quantitative analysis of the damage detection capabilities of DIC structure surface strain measurements in the context of different defect sizes, depths, and orientation angles relative to the loading direction was conducted. For Lamb wave analysis, a 2D spatial-temporal spectrum analysis and FEM using ABAQUS software were conducted to investigate the interaction of Lamb waves with the different defects. It was demonstrated that the FEM updating procedure could be used to characterize damage shape and size from the composite structure surface strain field from DIC. DIC defect detection capabilities for different loadings are demonstrated for the CFRP composite. For the identification of any composite defect, its characterization, and possible further monitoring, a methodology based on initial Lamb wave analysis followed by DIC testing is proposed.

The impact of China's urbanization on ecosystem service value from the perspective of gross ecosystem product: a case study of Beijing-Tianjin-Hebei region

Gross ecosystem product (GEP) is an aggregate measure of the monetary value of final ecosystem services, or the direct benefits that people derive from nature. GEP can provide decision makers with clear and competing evidence of the monetary value of ecosystem services. However, the relationship between GEP and urbanization has not been clarified which is not conducive to the decision-making role of GEP in the process of urban sustainable development. This work focused on the ‘Beijing-Tianjin-Hebei’ (BTH) urban agglomeration as a case study of the dynamics of ecological production amidst rapid economic and urban development, and coupled a spatial–temporal analysis of regional ecological change based on GIS (Geographic Information System) with economic valuation methods using official statistics and survey data. Results showed that from 2000 to 2020, the GEP increased from 1.55 trillion to 2.36 trillion, the value of provisioning services and cultural services increased from 0.51 to 0.71 trillion to 0.10–0.65 trillion. The value of regulation services showed an upward and downward trend (0.94–1.03–0.98) due to the rapid economic development in the Beijing-Tianjin-Hebei region. There were obvious spatial differences in the distribution of the GEP, in which Beijing, Tianjin, Tangshan, Cangzhou GEP accounted for 15%, 14%, 16% and 11%, respectively. During 2000–2020, there is a significant correlation between urbanization index (UI) and GEP in BTH, but the correlation between GEP and UI shows a trend of first increasing and then decreasing. The correlation between UI and EPS and ERS gradually decreases, and the impact of UI on ETS shows a significant positive correlation. In the future, it can be foreseen that urbanization will suppress the increase of GEP.

Spatial-temporal patterns of ecological-environmental attributes within different geological-topographical zones: a case from Hailun District, Heilongjiang Province, China

The climate change and extension of human activities are shedding more stresses on ecosystems. Ecological zoning could help manage the ecosystem and deal with environmental problems more effectively. Geology and topography could affect the ecology primarily and are vital perspectives on ecological zoning. It is worth preliminarily understanding the spatial-temporal patterns of ecological-environmental attributes within various geological-topographical ecological zones (GTEZs). The objective of this study was to delineate GTEZs and present a spatial-temporal analysis on soil and land surface parameters within GTEZs. Firstly, Landsat imageries, high resolution satellite imagery products, digital elevation model, regional geological map, black soil thickness, soil bulk density, meteorological data, and ground survey were collected and conducted. Secondly, GTEZs in Hailun District were delineated according to geological and topographical background. Thirdly, soil composition, and monthly land surface temperature (LST), enhanced vegetation index (EVI), net primary productivity (NPP) were derived from ground survey and Landsat imageries. Finally, spatial-temporal patterns of various ecological-environmental attributes within different GTEZs were preliminarily revealed and analyzed. Results show that sand alluvial plain zone and silt-clay undulating plain zone mainly possess thick soil with fine-medium granule and higher bulk density, and are mainly covered by crops and grass, vegetation flourish the most in August with the highest monthly EVI and NPP. While the sand-conglomerate hill zone, sandstone hill zone, and granite hill zone possess relatively thin soil with medium-coarse granule and lower bulk density, and are mainly covered by forest, vegetation flourish the most in June and July, and has the highest yearly total NPP. With thinner soil thickness and higher NPP, hill zones tend to have more vulnerability to disturbance and more contribution to carbon neutrality target.

High spatial resolution inversion of chromophoric dissolved organic matter (CDOM) concentrations in Ebinur Lake of arid Xinjiang, China: Implications for surface water quality monitoring

Utilizing satellite remote sensing for the assessment and temporal-spatial analysis of Chromophoric Dissolved Organic Matter (CDOM) is vital for overseeing lake water health and devising management plans. This study focused on the saline, turbid and arid Ebinur Lake, located in China’s northwestern region. The Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) model algorithms were compared to select the one with the highest accuracy. It combined Sentinel-2 remote sensing data and in situ measurement data for the quantitative inversion of CDOM. Monthly CDOM distribution maps were generated with a 10 m resolution for the non-frozen months of May to October from 2018 to 2022, followed by a comprehensive analysis of temporal trends. The primary conclusions are: (1) The XGBoost model yielded highly accurate CDOM estimates, with a training set coefficient of determination (R2) of 0.94, a Root Mean Square Error (RMSE) of 0.06 mg/L, Mean Absolute Percentage Error (MAPE) of 6.05 %, Relative Percent Difference (RPD) of 4.07; the test set demonstrated an R2 of 0.41 with an RMSE of 0.22 mg/L, MAPE of 22.74 %, RPD of 1.35; (2) Throughout the study period, the main lake portion displayed variable CDOM spatial patterns and trends. The inversion indicated higher CDOM concentrations in the central part than nearshore areas and decreasing CDOM in tandem with seasonable water-surface shrinkage. The findings offer hints for an accurate evaluation of water color parameters of Ebinur Lake and practical references for monitoring arid-region lake water quality via remote sensing.

Epidemiological Characteristics and Spatiotemporal Patterns of Visceral Leishmaniasis in Xinjiang, China, during 2004-2021.

The spread of visceral leishmaniasis (VL), a serious global zoonotic parasitic disease, is mostly under control; however, several cases have been reported in recent decades in Xinjiang, China. This study aimed to analyze the epidemiological status and spatiotemporal clustering characteristics of VL in Xinjiang, China, between 2004 and 2021 to provide a basis for the development and implementation of surveillance and response measures. Data on VL incidence during 2004-2021 were collected from the National Diseases Reporting Information System of China. Global spatial autocorrelation analysis, identification of local indicators of spatial association, and spatial-temporal clustering analysis were conducted to identify the distribution and high-risk areas. A total of 2034 VL cases were reported, with a mean annual incidence of 0.50 per 100,000. There was a general decreasing trend in the incidence of VL during our study period. The majority of the cases were reported from October to February of the following year, and fewer cases were reported from April to July. Spatial autocorrelation analysis revealed that the incidence of VL was spatially clustered within a few counties. Significant differences were observed during the study period (Moran's I = 0.74, Z = 4.900, p < 0.05). The male-to-female ratio was 1.37:1, and most patients were in the age group 0-3 years. Cases were primarily distributed in seven regions and two autonomous prefectures, and Kashgar reported the highest number of cases (1688, 82.98%). Spatial analysis revealed that the aggregation of VL was predominantly observed in southwest Xinjiang. This was in alignment with the high-risk areas identified by spatiotemporal clustering analysis. The H-H clustering region was primarily observed in Gashi, Atushi, Shufu, Injisha, Kashgar, Yepuhu, and Bachu. These findings indicate that integrated control measures must be taken in different endemic areas to strengthen the VL control program in Xinjiang, China.

Efficiency assessment and scenario simulation of the water-energy-food system in the Yellow river basin, China

Comprehending the efficiency of the Water-Energy-Food system within basins is crucial to enhance their resource utilization and facilitate high-quality development. This research evaluates the efficiency of the Water-Energy-Food (WEF) system in the Yellow River Basin between 2005 and 2021 using the super efficiency network Data Envelopment Analysis (DEA) model, addressing the limitation of traditional DEA models by considering both input and output within system. A multi-scenario combination is established under different development modalities, and an innovative attempt is made to introduce machine learning into efficiency prediction. The GA-LSTM (Genetic Algorithm-Long Short-Term Memory) model is constructed to forecast the efficiency values across multiple scenarios and identify the optimal scenario namely the scenario with the highest efficiency value, to carry out the spatial-temporal analysis. The results indicate that: (1) The WEF system efficiency in the Yellow River Basin exhibited a variable rising trajectory from 2005 to 2021, with the economic scale playing a significant role as an influencing element; (2) The trained GA-LSTM model can accurately predict the efficiency value and change trend of WEF system in the Yellow River Basin; (3) Under scenario 4 when water and food subsystems are maintained in the baseline scenario, while the energy subsystem is regulated in the general saving scenario, the overall average efficiency value of WEF system in the Yellow River Basin in 2022–2035 is the highest, reaching 1.28, which is the optimal enhancement path; (4) Under the optimal scenario, the WEF system efficiency in the Yellow River Basin increases by 6.89 % annually, with Shandong having the greatest efficiency level and Qinghai the lowest.

Analysis of green infrastructure network (GIN) based on morphological spatial-temporal pattern analysis (MSTPA): A case study of Shijingshan district in Beijing

Abstract Rapid urbanization results in habitat fragmentation. GIN analysis provides an objective basis for the complete construction of ecological networks. Although there is currently no uniform standard for GIN analysis, this study found that the core region and the connectivity between the core became two key elements of GIN analysis. By analyzing the morphological recognition of GIN, Morphological Spatial Pattern Analysis (MSPA) is targeted in the core area and connectivity analysis, so it has become the mainstream of GIN analysis methods. At the same time, this study finds that there are commonalities in MSPA analysis methods of different scholars. However, it should be noted that GIN analysis by MSPA method in China has two main research gaps: First, it lacks attention to the space scale of urban administrative district level; Second, there is a lack of research on GIN that considers time as a factor. In order to fill these two research gaps, the Shijingshan District of Beijing was selected as the research site to conduct GIN analysis based on Morphological Spatial-Temporal Pattern Analysis (MSTPA). At the same time, the analysis method and data of this study can provide a reference for future scholars to carry out the same type of research.

Utilizing IOT and Geospatial Analytics for Sustainable Fisheries Management

This study developed a software application that integrates Internet of Things (IoT) devices and weather data to visualize prime fishing locations using advanced spatial data techniques. The application features a dashboard that processes and displays real-time data, providing insights into fishing trends, fisherman activities, boat locations, and environmental conditions. The application uses the Graham scan method to generate a GIS grid heatmap for visualizing fish populations and trends, enhancing fisheries management capabilities. Comprehensive testing and refinement ensured the application's usability and adaptability. The results demonstrated high user satisfaction, with a 91% rating in usability and accuracy. The Graham Scan method successfully mapped fishing zones, achieving a 97.96% overlap in spatial-temporal data analysis, proving essential for data-driven decision-making in sustainable fisheries management.

Knowledge Maps and Emerging Trends in Cell-Cultured Meat since the 21st Century Research: Based on Different National Perspectives of Spatial-Temporal Analysis.

Cell-cultured meat holds significant environmental value as an alternative protein source. Throughout the 21st century, cell-cultured meat has progressively penetrated commercial markets. However, a systematic review encompassing the entire field needs improvement. Employing Citespace, Vosviewer, and R-Bibliometrix software, a bibliometric analysis was used to present the research progress and general development trends of 484 articles on cell-cultured meat from 2000 to 2022 based on countries, authors, institutions, and keywords. This analysis provides ideas for the future development of cell-cultured meat in different countries or regions worldwide. Research on cell-cultured meat from 2000 to 2022 has undergone two phases: fluctuating growth (2000-2013) and rapid growth (2013-2022). Noteworthy contributions to cell-cultured meat studies emerge from author groups in the United States of America, the United Kingdom, and China, with influential institutions like the University of Bath significantly impacting pertinent research. Furthermore, over the past two decades, research has leaned towards exploring topics such as "biomaterials", "cultured", "land use", "public opinion", "animal welfare", and "food safety". Furthermore, this study reveals differences in nomenclature between regions and institutions. "Cultured meat" is more popular in some countries than in other forms. Institutions in Asia use "cultured meat" more frequently; however, institutions in the Americas adopt "cultivated meat" and rarely adopt "in vitro meat", and institutions in the European region have no particularly prominent tendency towards a specific nomenclature. Future research should emphasize aligning the labeling of cell-cultured meat with effective management strategies and referencing regulatory policies across various countries. For the first time, we use three different bibliometric methods to analyze temporal and spatial variation in research on cellular meat. The results of this study have a multiplier effect. We provide a theoretical basis and a practical reference for the identification of alternatives in the dual context of "food crisis and food security" and "climate crisis". At the same time, we also provide a reference for the sustainable development of the food system.

Cetacean Stranding Response Program and Spatial-Temporal Analysis in Taiwan, 1994-2018.

A national cetacean stranding response program in Taiwan has evolved significantly in the past three decades. Initially co-ordinated by National Taiwan University from 1994, the program transitioned to the Taiwan Cetacean Society in 1999, and local governments took on a more prominent role after 2009. A comprehensive stranding database (1994-2018) has been maintained, which documented 1320 stranding events involving 1698 animals from at least 27 species. The most commonly stranded species include finless porpoises, bottlenose dolphins, Kogia spp., and Risso's dolphins. The stranding rates varied annually and seasonally, with increases noted from an average of 16 events per year for the first 3 years to 44-58 events per year between 1997 and 2015, and a sharp rise to over 90 events per year for the period of the last three years. Seasonal variations were also significant, with higher stranding rates during the northeastern monsoon (NEM, October to next April) than that during southwestern monsoon (SWM, May to September). From the aspect of distribution, more frequent and even strandings occurred along the coast of northern Taiwan, while mass strandings were concentrated in the southwestern counties during NEM. Among all strandings, 390 events (29.5%) and 660 animals (38.9%) were live ones. Under great effort in rescuing and rehabilitating 52 cases, 15 cetacean individuals have been released since 2000. Additionally, there have been 56 mass strandings involving at least 11 species since 1994, predominated by pygmy killer whales, particularly during the NEM season along the southwest coast. This study not only contributes to our understanding of the stranding patterns and diversity of the cetaceans in Taiwan, but also provides valuable insights for future conservation strategies on cetaceans in the western Pacific.

Pulmonary Tuberculosis Notification Rate Within Shenzhen, China, 2010-2019: Spatial-Temporal Analysis.

Pulmonary tuberculosis (PTB) is a chronic communicable disease of major public health and social concern. Although spatial-temporal analysis has been widely used to describe distribution characteristics and transmission patterns, few studies have revealed the changes in the small-scale clustering of PTB at the street level. The aim of this study was to analyze the temporal and spatial distribution characteristics and clusters of PTB at the street level in the Shenzhen municipality of China to provide a reference for PTB prevention and control. Data of reported PTB cases in Shenzhen from January 2010 to December 2019 were extracted from the China Information System for Disease Control and Prevention to describe the epidemiological characteristics. Time-series, spatial-autocorrelation, and spatial-temporal scanning analyses were performed to identify the spatial and temporal patterns and high-risk areas at the street level. A total of 58,122 PTB cases from 2010 to 2019 were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100,000 population in 2010 to 43.43 per 100,000 population in 2019. PTB cases exhibited seasonal variations with peaks in late spring and summer each year. The PTB notification rate was nonrandomly distributed and spatially clustered with a Moran I value of 0.134 (P=.02). One most-likely cluster and 10 secondary clusters were detected, and the most-likely clustering area was centered at Nanshan Street of Nanshan District covering 6 streets, with the clustering time spanning from January 2010 to November 2012. This study identified seasonal patterns and spatial-temporal clusters of PTB cases at the street level in the Shenzhen municipality of China. Resources should be prioritized to the identified high-risk areas for PTB prevention and control.

The WUSCHEL-related homeobox transcription factor CsWOX3 negatively regulates fruit spine morphogenesis in cucumber (Cucumis sativus L.).

Cucumber (Cucumis sativus L.) is a widely cultivated crop with rich germplasm resources, holding significant nutritional value. It also serves as an important model for studying epidermal cell fate and sex determination. Cucumbers are covered with multicellular and unbranched trichomes, including a specific type called spines found on the surface of the fruit. The presence and density of these fruit spines determine the visual quality of cucumber fruits. However, the key regulatory genes and mechanisms underlying cucumber fruit spine development remain poorly understood. In this study, we identified a WUSCHEL-related homeobox (WOX) family gene CsWOX3, which functioned as a typical transcriptional repressor and played a negative role in fruit spine development. Spatial-temporal expression analysis revealed that CsWOX3 exhibited a relatively high expression level in the cucumber female floral organs, particularly in the fruit exocarp. Knockout of CsWOX3 using CRISPR/Cas9 resulted in a significant 2-to-3-fold increase in the diameter of fruit spines base, while overexpression led to a 17% decrease in the diameter compared to the wild-type. A SQUAMOSA PROMOTER BINDING PROTEIN-LIKE transcription factor CsSPL15 could directly bind and activate the expression of CsWOX3, thereby suppressing the expression of downstream auxin-related genes, such as CsARF18. Additionally, the RING-finger type E3 ubiquitin ligase CsMIEL1-like interacted with the HD domain of CsWOX3, which might result in the ubiquitination and subsequent alteration in protein stability of CsWOX3. Collectively, our study uncovered a WOX transcription factor CsWOX3 and elucidated its expression pattern and biological function. This discovery enhances our comprehension of the molecular mechanism governing cucumber fruit spine morphogenesis.

Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as Deep Learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model’s flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood extent mapping model has a broad range of applicability, including, but not limited to, the continuous monitoring of flood events and their potential impacts on civil infrastructure assets (e.g., construction, operation, and maintenance of roads and bridges), early warning systems for timely evacuation and preparedness measures, and insurance risk assessment.

Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China

Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are significant for water system management. In this study, spatial-temporal analysis and correlation analysis were applied to reveal Chla concentration pattern in the Fuchun River, China. Then four exogenous variables (wind speed, water temperature, dissolved oxygen and turbidity) were used for predicting Chla concentrations by six models (3 traditional machine learning models and 3 deep learning models) and compare the performance in a river with different hydrology characteristics. Statistical analysis shown that the Chla concentration in the reservoir river segment was higher than in the natural river segment during August and September, while the dominant algae gradually changed from Cyanophyta to Cryptophyta. Moreover, air temperature, water temperature and dissolved oxygen had high correlations with Chla concentrations among environment factors. The results of the prediction models demonstrate that extreme gradient boosting (XGBoost) and long short-term memory neural network (LSTM) were the best performance model in the reservoir river segment (NSE = 0.93; RMSE = 4.67) and natural river segment (NSE = 0.94; RMSE = 1.84), respectively. This study provides a reference for further understanding eutrophication and early warning of algal blooms in different type of rivers.

STPDA: Leveraging spatial-temporal patterns for downstream analysis in spatial transcriptomic data

Spatial transcriptomics, a groundbreaking field in cellular biology, faces the challenge of effectively deciphering complex spatial-temporal gene expression patterns. Traditional data analysis methods often fail to capture the intricate nuances of this data, limiting the depth of understanding in spatial distribution and gene interactions. In response, we present Spatial-Temporal Patterns for Downstream Analysis (STPDA), a sophisticated computational framework tailored for spatial transcriptomic data analysis. STPDA leverages high-resolution mapping to bridge the gap between genomics and histopathology, offering a comprehensive perspective on the spatial dynamics of gene expression within tissues. This approach enables a view of cellular function and organization, marking a paradigm shift in our comprehension of biological systems. By employing Autoregressive Moving Average (ARMA) and Long Short-Term Memory (LSTM) models, STPDA effectively deciphers both global and local spatio-temporal dynamics in cellular environments. This integration of spatial-temporal patterns for downstream analysis offers a transformative approach to spatial transcriptomics data analysis. STPDA excels in various single-cell analytical tasks, including the identification of ligand-receptor interactions and cell type classification. Its ability to harness spatial-temporal patterns not only matches but frequently surpasses the performance of existing state-of-the-art methods. To ensure widespread usability and impact, we have encapsulated STPDA in a scalable and accessible Python package, addressing single-cell tasks through advanced spatial-temporal pattern analysis. This development promises to enhance our understanding of cellular biology, offering novel insights and therapeutic strategies, and represents a substantial advancement in the field of spatial transcriptomics.

Assessing efficiency in sustainable allocation of agricultural scientific and technological talent: a spatial-temporal analysis in China

Efficient allocation of agricultural scientific and technological talents (ASTTs) is crucial for agricultural innovation and economic development. This study aims to systematically evaluate ASTTs’ allocation efficiency in provincial agricultural research institutions in China, aiding decision-making for local governments and research bodies. Utilizing data from 2009 to 2019 across 31 provinces, an output-oriented data envelopment analysis model measures ASTTs’ allocation efficiency and analyzes its trends, regional differences, and spatial characteristics. Results show: (1) Provincial ASTTs’ mean comprehensive technical efficiency (CTE) in China was 0.786, with room for improvement. (2) Enhanced CTE was driven by scale efficiency improvements, while pure technical efficiency declined, indicating a need for better management systems and technology applications. (3) Disparities in ASTTs’ allocation efficiency among provinces decreased, with higher efficiencies in the East and Central-Southern China regions. At the provincial level, areas like Jiangsu, Shandong, Henan, and Sichuan demonstrated relatively high ASTTs allocation efficiencies. (4) Spatial agglomeration of ASTTs’ allocation efficiency was localized in a few major agricultural provinces without a significant overall effect. These findings advocate for further optimization of ASTTs’ regional layout and management mechanisms in China.

Spatial Analysis of LULC Changes and Suitability of Ngawi Regency on Spatial Regional Planning 2011-2031 Using Geographic Information System

Abstract This study analyzes land use change in Ngawi Regency, East Java, in 2011 and 2021 and examines the suitability of land use, Regional Spatial Plan, and Regional Medium-Term Development Plan. Qualitative descriptive analysis methods and spatial-temporal analysis are used to identify land-use change, land-use change, and land-use-related problems. The results showed a discrepancy between actual land use and RTRW in several sub-districts, such as Padas, Pangkur, Ngawi, Pitu, Kedunggalar, Ngrambe, Sine, Jogorogo, Kendal. This discrepancy includes changes in land functions from settlements to industries, tourism areas to settlements, industries to settlements, and vice versa. The total area of nonconformity reached 40,361 hectares or 28.93 % of the Ngawi area. GIS and map overlays provide an in-depth understanding of land change patterns, with settlements and industrial sectors being key focuses. Although the Regional Spatial Plan has been established to regulate land use, its implementation still needs to be improved in 2021. The development of tourism, industrial, agricultural, and residential areas sometimes goes differently than planned. This research highlights the need for coordination between Regional Medium-Term Development Plans and Regional Spatial Plans to achieve sustainable regional development.

Designing a Motion-Onset Visual-Evoked Potential-Based Brain–Computer Interface to Control a Computer Game

Brain-computer interface (BCI) establishes an alternative direct communication channel between the brain and computer or other external devices. Motion-onset visual evoked potential (mVEP) related to the individual perception of movement is among synchronous BCI system that utilizes external stimulus. mVEPs stimulation paradigm is like an oddball stimulation paradigm. Researchers have recently been investigating mVEPs to control a computer game. To control a computer game, they used translation stimulus to evoke these potentials in EEG brain signals. In this study, we examine designing a computer game with four different types of stimuli, including translation, contraction, expansion, and rotation. We also used time-domain and wavelet coefficients for the feature extraction. Also, we used different types of linear discriminant analysis (LDA) classification methods, including least absolute shrinkage and selection operator (LASSO), stepwise LDA (SWLDA); moreover, and spatial-temporal discriminant analysis (STDA) for detecting the target stimulus. Our results demonstrate that contraction, expansion, and rotation stimuli, as well as translation, could be employed to control a BCI game. Our results showed that subjects got higher scores, game accuracy control, and information transfer rate using the rotation stimulus with SWLDA/STDA classification methods.

Spatial-temporal analysis of heat islands applied to the coastal city of San Francisco de Campeche, Mexico

The urbanization of the city of San Francisco de Campeche influences the formation of urban heat islands due to construction materials, buildings and structures, human activities, lack of vegetation, and transportation infrastructure. Heat islands have negative consequences such as increased energy consumption and heat stress for the population, contributing to climate change due to increased greenhouse gas emissions caused by additional energy demand. Cities such as Sydney, Beijing, Nanjing, Moscow, and Hong Kong are implementing urban planning strategies that promote urban vegetation, the use of reflective building materials, the improvement of public transport, and the promotion of energy efficiency in buildings. Landsat satellite images were used to analyze population growth and urban sprawl to identify heat islands, and a vegetation index analysis was also made. Regarding the analyses, it was recognized that the temperature increased by approximately 6°C between 1990 and 2022. There has also been a decrease in vegetation due to the urban sprawl and housing growth, quadrupling the Normalized Difference Vegetation Index (NDVI) in the 0-0.25 class for the same period. Finally, mitigation measures are proposed to counteract the effects caused by heat islands in the city.