Space-time Cube Research Articles

Enhancing GeoHealth: A step-by-step procedure for spatiotemporal disease mapping.

Cartography, or geographical visualization of disease is an essential aspect of the field of GeoHealth, yet there is limited guidance on the visualization of spatiotemporal disease maps. In order to adequately contribute to understanding disease outbreaks, disease maps should be crafted carefully and according to relevant cartographic guidelines. This article aims to increase the understanding of space-time visualization techniques that are relevant to the field of GeoHealth, by providing a step-by-step framework for the creation of space-time disease visualizations. This study introduces a systematic approach to spatiotemporal disease mapping by integrating operations from the Generalized Space Time Cube (GSTC) Framework with established cartographic symbology guidelines. This resulted in an overview table that contains both the relevant GSTC operations and cartographic guidelines, as well as a step-by-step procedure that guides users through the process of creating informative spatiotemporal disease maps. The practical application of this step-by-step procedure is demonstrated with an example using Dutch COVID-19 data. By providing a clear, practical step by step procedure, this study enhances the capacity of public health professionals, policymakers, and researchers to monitor, understand, and respond to the spatial and temporal dynamics of diseases.

From urban heat islands to intra-urban heat islands: Role of urban fabric in redefining microclimates of tomorrow’s compact cities

A compact city is a urban area characterized by high density, mixed land use, and limited sprawl, designed to promote sustainable development, reduce urban sprawl, and enhance quality of life. This investigation focuses on the Intra Urban Heat Island (IUHI) effect, an intensified progression of the Urban Heat Island (UHI) phenomenon, specifically within a compact city transitioning from urban sprawl. The study employed advanced spatial analytics, which employed the concept of “space-time cube”, incorporating Getis Ord Gi*, and per pixel, Mann Kendall tests for the space–time pattern mining on a time series of high-resolution Remote Sensing data spanning from 1999 to 2023. The findings reveal distinctive spatial and temporal patterns in IUHI, identifying a total of 693,900 m2 of intensifying hot-spot areas in the selected compact city characterized by industrial, warehousing, and commercial developments. The detailed examination of urban fabric at a finer resolution (7.5 cm × 7.5 cm) identified rooftops with specific spectral characteristics (red/copper hue) as significant contributors to the IUHI phenomenon, inducing surface temperature increases above 5 °C compared to neighboring cells. Furthermore, high-rise developments emerge as land use forms that create cold spots in the urban fabric, improving the city’s thermal environment. The implications underscore the necessity for future urban planning to consider IUHI as a concentrated development of the UHI effect, urging a holistic understanding of the complex interconnections among various factors influencing microclimates in urban environments.

Quantitative Analysis about the Spatial Heterogeneity of Water Conservation Services Function Using a Space–Time Cube Constructed Based on Ecosystem and Soil Types

Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies.

Articulating environmental sustainability dynamics with space-time cube

Conceptually, environmental sustainability involves maintaining crucial environmental functions while considering both present and future development. However, existing methods for expressing environmental sustainability are mainly derived from a steady state with minimal spatial explicitness. Furthermore, the environmental impact of certain events may exhibit a lag, particularly in basins. Here, we propose a framework that employs a space-time cube to articulate environmental sustainability. This cube can visualize the environment's evolution over time, identify hot and cold spots in space, and concurrently determine underlying influencing factors via spatial regression analysis. Unlike traditional methods, the space-time cube incorporates not only spatial dimensions but also temporal dimensions. We applied this framework to China's upper Han River basin, using the Remote Sensing Ecological Index (RSEI) as an indicator of environmental sustainability. It enabled us to chart the basin's ecological trajectory with spatial and temporal explicitness from 1990 to 2020. Our findings reveal that climate change (represented by temperature and precipitation changes) and human activities (represented by nighttime light) were the main factors driving changes in environmental sustainability from 2000 to 2020 in the basin. Therefore, our proposed spatial-temporal integration framework proves to be an efficient tool in articulating environmental sustainability.

A user study of visualisations of spatio-temporal eye tracking data

AbstractEye movements have a spatial (where people look), but also a temporal (when people look) component. Various types of visualizations have been proposed that take this spatio-temporal nature of the data into account, but it is unclear how well each one can be interpreted and whether such interpretation depends on the question asked about the data or the nature of the dataset that is being visualised. In this study, four spatio-temporal visualization techniques for eye movements (chord diagram, scan path, scarf plot, space-time cube) were compared in a user study. Participants $$(N = 25)$$ ( N = 25 ) answered three questions (what region first, what region most, which regions most between) about each visualization, which was based on two types of datasets (eye movements towards adverts, eye movements towards pairs of gambles). Accuracy of the answers depended on a combination of the dataset, the question that needed to answered, and the type of visualization. For most questions, the scan path, which did not use area of interest (AOI) information, resulted in lower accuracy than the other graphs. This suggests that AOIs improve the information conveyed by graphs. No effects of experience with reading graphs (for work or not for work) or education on accuracy of the answer was found. The results therefore suggest that there is no single best visualisation of the spatio-temporal aspects of eye movements. When visualising eye movement data, a user study may therefore be beneficial to determine the optimal visualization of the dataset and research question at hand. Graphical abstract

Application of modern spatio-temporal analysis technologies to identify and visualize patterns of rabies emergence among different animal species in Kazakhstan.

During the period 2013-2023, 917 cases of rabies among animals were registered in the Republic of Kazakhstan. Out of these, the number of cases in farm animals amounted to 515, in wild animals to 50 and in pets to 352. Data on rabies cases were obtained from the Committee for Veterinary Control and Supervision of Kazakhstan, as well as during expeditionary trips. This research was carried out to demonstrate the use of modern information and communication technologies, geospatial analysis technologies in particular, to identify and visualize spatio-temporal patterns of rabies emergence among different animal species in Kazakhstan. We also aimed to predict an expected number of cases next year based on time series analysis. Applying the 'space-time cube' technique to a time series representingcases from the three categories of animals at the district-level demonstrated a decreasing trend of incidence in most of the country over the study period. We estimated the expected number of rabies cases for 2024 using a random forest model based on the space-time cube in Arc-GIS. This type of model imposes only a few assumptions on the data and is useful when dealing with time series including complicated trends. The forecast showed that in most districts of Kazakhstan, a total of no more than one case of rabies should beexpected, with the exception of certain areas in the North and the East of the country, where the number of cases could reach three. The results of this research may be useful to the veterinary service in mapping the current epidemiological situation and in planning targeted vaccination campaigns among different categories of animals.

Case study: analysing the progression of artificial lighting in Gauteng Province over a decade using spatio-temporal analysis

ABSTRACT Services related to generating and distributing electrical power are essential to economic growth and development. This study investigates the temporal patterns of night-time light (NTL) recorded by the Visible Infrared Imaging Radiometer Suite (VIIRS) on orbiting satellites. The time series data covers the years 2012 through 2022. The findings show that the year-on-year changes in lighting are quite small. Most major cities’ luminosity has diminished, whereas smaller cities have remained relatively constant. Using statistical approaches, this study located areas of hot and cold spots of NTL trends across Gauteng Province using a space–time cube. By using space–time cubes, it is possible to visualise how phenomena evolve over time and space. The most worrying is the diminishing hot spot located at the economic powerhouse of South Africa, Johannesburg. The study also used three-dimensional visualisation to comprehend better the underlying patterns represented by each hot spot. This dataset makes it possible to determine where urban sprawl occurs in the province, as they are characterised by new, consecutive, and sporadic hot spots. By looking at spatiotemporal dynamic changing patterns, it is easy to find complex patterns that change over time and space and provide valuable information for NTL.

Spatiotemporal dynamic relationships and simulation of urban spatial form changes and land surface temperature: a case study in Chengdu, China.

Exploring the spatiotemporal dynamic evolution of local climate zones (LCZ) associated with changes in land surface temperature (LST) can help urban planners deeply understand urban climate. Firstly, we monitored the evolution of 3D urban spatial form in Chengdu City, Sichuan Province, China from 2010 to 2020, used the ordinary least squares model to fit the dynamic correlation (DR) between the changes in urban spatial patterns and changes in LST, and revealed the changes of urban spatial patterns closely related to the rise in LST. Secondly, the spatiotemporal patterns of LST were examined by the integration of the Space-Time Cube model and emerging hotspot analysis. Finally, a prediction model based on curve fitting and random forest was integrated to simulate the LST of study area in 2025. Results show the following: the evolution of the urban spatial form consists of three stages: initial incremental expansion, midterm incremental expansion and stock renewal, and late stock renewal and ecological transformation. The influence of the built environment on the rise of LST is greater than that of the natural environment, and the building density has a greater effect than the building height. The overall LST shows a warming trend, and the seven identified LST spatiotemporal patterns are dominated by oscillating and new hotspots patterns, accounting for 51.99 and 11.44% of the study area, respectively. The DR between urban spatial form and LST varies across different time periods and built environment types, whereas the natural environment is always positively correlated with LST. The thermal environment of the city will warm up in the future, and the area affected by the heat island will shift to the central of the city.

Geographical disparities in access to surgical treatment and mortality rates from abdominal aortic aneurysms in Brazil: A retrospective longitudinal study.

Abdominal aortic aneurysm (AAA) is a growing public health problem, and not all patients have access to surgery when needed. This study aimed to analyze spatiotemporal variations in AAA mortality and surgical procedures in Brazilian intermediate geographic regions and explore the impact of different surgical techniques on operative mortality. A retrospective longitudinal study was conducted to evaluate AAA mortality from 2008 to 2020 using space-time cube (STC) analysis and the emerging hot spot analysis tool through the Getis-Ord Gi* method. There were 34,255 deaths due to AAA, 13,075 surgeries to repair AAA, and a surgical mortality of 14.92%. STC analysis revealed an increase in AAA mortality rates (trend statistic = +1.7693, p = 0.0769) and a significant reduction in AAA surgery rates (trend statistic = -3.8436, p = 0.0001). Analysis of emerging hotspots revealed high AAA mortality rates in the South, Southeast, and Central-West, with a reduction in procedures in São Paulo and Minas Gerais States (Southeast). In the Northeast, there were extensive areas of increasing mortality rates and decreasing procedure rates (cold spots). AAA mortality increased in several regions of the country while surgery rates decreased, demonstrating the need for implementing public health policies to increase the availability of surgical procedures, particularly in less developed regions with limited access to services.

Interpretation of Hot Spots in Wuhan New Town Development and Analysis of Influencing Factors Based on Spatio-Temporal Pattern Mining

The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan’s new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran’s index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities.

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

Renewable sources of energy production are some of the main targets today to protect the environment through reduced fossil fuel consumption and CO2 emissions. Alongside wind, solar, marine, biomass and nuclear sources, hydropower is among the oldest but still not fully explored renewable energy sources. Compared with other sources like wind and solar, hydropower is more stable and consistent, offering increased predictability. Even so, it should be analyzed considering water flow, dams capacity, climate change, irrigation, navigation, and so on. The aim of this study is to propose a forecast model of hydropower production capacity and identify long-term trends. The curve fit forecast parabolic model was applied to 33 European countries for time series data from 1990 to 2021. Space-time cube ArcGIS representation in 2D and 3D offers visualization of the prediction and model confidence rate. The quadratic trajectory fit the raw data for 14 countries, validated by visual check, and in 20 countries, validated by FMRSE 10% threshold from the maximal value. The quadratic model choice is good for forecasting future values of hydropower electric capacity in 22 countries, with accuracy confirmed by the VMRSE 10% threshold from the maximal value. Seven local outliers were identified, with only one validated as a global outlier based on the Generalized Extreme Studentized Deviate (GESD) test at a 5% maximal number of outliers and a 90% confidence level. This result achieves our objective of estimating a level with a high degree of occurrence and offering a reliable forecast of hydropower production capacity. All European countries show a growing trend in the short term, but the trends show a stagnation or decrease if policies do not consider intensive growth through new technology integration and digital adoption. Unfortunately, Europe does not have extensive growth potential compared with Asia–Pacific. Public policies must boost hybrid hydro–wind or hydro–solar systems and intensive technical solutions.

Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System

Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies.

A new perspective on water quality: Exploring spatial and temporal patterns of impaired waters

Managing surface water quality is a global challenge, and understanding spatial and temporal patterns of water quality is a key component to effective management. However, analysis of spatiotemporal patterns of impaired waters over broad areas is sparse due to disparate water quality data and variable water quality standards. Thus, here we leverage the Alabama 303(d) List of impaired waters to present a new perspective for investigating spatiotemporal water quality patterns. Every two years, each state in the United States is required to assess its surface water quality and compile a list of impaired waterbodies, meaning waters that do not meet water quality standards for their designated usage – referred to as the 303(d) List. The purpose of the 303(d) List is to identify impaired waters so that corrective action can be taken to reduce pollutant loads and, ultimately, improve water quality. Using GIS, a space time cube was created to analyze and visualize spatiotemporal patterns of the impaired rivers added to the Alabama 303(d) Lists from 1996 to 2022. For this analysis, the percentage of river length impaired out of the total river length, and number of times each impairment cause was listed, were summarized within Alabama sub-basins (Hydrologic Unit Code 8) (n = 51). Trend and hot spot analyses were conducted on the river impairment and causes. There was an up trend in river impairment for eight sub-basins across the state and a downtrend in one sub-basin. Over half of the sub-basins with an up trend in impairment also had an up trend in the number of times pathogens was listed as a cause of impairment. Additionally, coastal sub-basins were found to be a hot spot for river impairment. Interestingly, there was a down trend in the number of times nutrients, ammonia, and siltation were listed as a cause of impairment at the state and sub-basin scales of analysis. Altogether, these findings show the use of spatiotemporal pattern analysis of impaired waters and can indicate where, both spatially and by pollutant, management should prioritize water quality improvement efforts.

A Review on the 3D Cartographic and Spatiotemporal GIS Models for Safety of Accidents in Deep Underground Coal Mines

The aim of this study was the safety risk assessment of accidents that occur because of production dynamics in deep coal mining, one of the highly dangerous business class, with spatiotemporal GIS. In this study, accidents that occur at certain times during production in underground mining workplaces are discussed based on evaluations of spatial and temporal dimensions. Accordingly, various analyses have been handled using criminology science tactics and strategies such as 80–20, kernel density, space–time cube, and hot spot, which have been adapted to GIS tools. Implementations have been realized on 3D underground cartographic models covering accident data from deep coal mine workplaces in the Zonguldak-Kozlu basin in Turkey between 2019 and 2021, at an average elevation of – 400 m. Space–time cube, kernel density, and time series analyses suggest that the Annual Bonus Incentives, which are repeated every year and given before the new year, increase the psychosocial risks for employees. It has been determined that this situation can lead to problems such as poor concentration among miners and loss of focus in some shifts. Additionally, significant relationships were found between the number of accidents and monthly production progress rates as a temporal parameter. The results demonstrate that applying a reactive approach with spatiotemporal GIS to accidents in deep coal mines is becoming increasingly important.

MoReVis: A Visual Summary for Spatiotemporal Moving Regions.

Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.

Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada

Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop’s growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high spatial and temporal resolution remote sensing images. The spectral signatures for five growing stages were prepared. NIR reflectance increased during the growing stages and decreased at the senescence, indicating healthy vegetation. The space–time cube provided valuable insight into how canopy height changed over time. The effect of nitrogen treatments on wheat did not directly influence the plant count (spring/autumn), and height and volume at maturity. However, the green and dry weights were different at several treatments. Winter wheat yield was predicted using the XGBoost algorithm, and moderate results were obtained. The study explored different techniques for analyzing winter wheat growth dynamics and identified their usefulness in smart agriculture.

Exploiting Physical Referent Features as Input for Multidimensional Data Selection in Augmented Reality

Embedding data into the physical environment using augmented reality (AR) is a practical approach for data visualization as it offers a large and flexible display space on or around the physical referent, i.e. the physical object to which the data is related. Yet current interaction in such context is often performed using cumbersome dedicated devices, tiring mid-air gestures or awkward on-body input. In this paper, we investigate the use of the physical referent itself as a support for input interaction with an embedded space-time cube (STC) representation. Hence, we first identify the most promising mappings between the physical features of the referent (edges, faces, corners) and the STC dimensions. Then, we design three data selection techniques using the physical referent and compare them to mid-air gestures when performing selection tasks on the STC. Overall, our work demonstrates that using the physical referent to support input interaction with embedded data representations is an efficient and comfortable approach for data selection in standing and sitting situations.

Hot Spots of Gun Violence in the Era of Focused Deterrence: A Space-Time Analysis of Shootings in South Philadelphia

Gun and street group violence remains a serious problem in cities across the United States and the focused deterrence strategy has been a widely applied law enforcement intervention to reduce it. Although two meta-analytical studies concluded that the intervention had a significant effect on violence, questions remain about how violence changes across space and time during and after the intervention. This study applies novel geospatial analyses to assess spatiotemporal changes in gun violence before, during, and after the implementation of Philadelphia Focused Deterrence. Emerging hot spot analysis employing Space-Time cubes of ten annual time bins (2009–2018) at the Thiessen polygon level was used to detect and categorize patterns. The analyses revealed a non-significant decreasing trend across the ten-year period. Furthermore, there were ninety-three statistically significant hot spots categorized into four hot spot patterns: fourteen new hot spots; twenty-three consecutive; one persistent; and fifty-three sporadic. There was no evidence showing statistically significant hot spots for the “diminishing” pattern. Knowledge of these patterns that emerge across micro-locations can be used by law enforcement practitioners to complement data-driven problem solving and fine tune these strategies and other place-based programming. Policymakers can use findings to prioritize resources when developing complementary prevention and intervention efforts by tailoring those efforts to the different emergent patterns.

A Conceptual Framework for Modeling Spatiotemporal Dynamics of Diesel Attenuation Capacity: A Case Study across Namyangju, South Korea

The vadose zone acts as a natural buffer against groundwater contamination, and thus, its attenuation capacity (AC) directly affects groundwater vulnerability to pollutants. A regression model from the previous study predicting the overall AC of soils against diesel was further expanded to the GIS-based overlay-index model. Among the six physicochemical parameters used in the regression model, saturation degree (SD) is notably susceptible to climatological and meteorological events. To accommodate the lack of soil SD historical data, a series of infiltration simulations were separately conducted using Phydrus code with moving boundary conditions (i.e., rainfall records). The temporal variation of SD and the resulting AC under transient conditions are captured by building a space–time cube using a temporal raster across the study area within the designated time frame (1997–2022). The emerging hot spot analysis (EHSA) tool, based on the Getis–Ord Gi* and Mann–Kendall statistics, is applied to further identify any existing pattern associated with both SD and AC in both space and time simultaneously. Under stationary conditions, AC decreases along depth and is relatively lower near water bodies. Similarly, AC cold spot trends also show up near water bodies under transient conditions. The result captures not only the trends across time but also shows the exact location where the changes happen. The proposed framework provides an efficient tool to look for locations that have a persistently low or a gradually decreasing ability to attenuate diesel over time, indicating the need for stricter management regulations from a long-term perspective.

Space-time cube uncovers spatiotemporal patterns of basin ecological quality and their relationship with water eutrophication

Maintaining an optimal eco-environment is important for sustainable regional development. However, existing methods are inadequate for examining both spatial and temporal dimensions. Here, we propose a systematic procedure for spatiotemporal examination of the eco-environment using the space-time cube (STC) model and describe a preliminary investigation of the coupling relationships between basin ecological quality and water eutrophication in upstream of the Han River basin between 2000 and 2020. The STC model considers the temporal dimension as the third dimension in calculations. We first categorized the basin into three sub-watershed types: forest, cultivated land, and artificial surface. Subsequently, the ecological quality and driving factors were assessed and identified using the remote sensing ecological index (RSEI) and Geodetector method, respectively. The findings indicated that the forest basin and artificial surface basin had the highest and lowest ecological quality, respectively. The spatiotemporal cold spots of ecological quality during the past 20 years were mostly located in the vicinity of reservoirs, rivers, and artificial surface areas. Human activity, precipitation, and the percentage of cultivated land were other important driving factors in the artificial surface, forest, and cultivated land sub-watersheds, respectively, in addition to the dominant factors of elevation and temperature. The results also indicated that when the ecological quality degraded to a certain extent, water eutrophication was significantly coupled with the ecological quality of the catchments. The findings of this study are useful for ecological restoration and sustainable river basin development.