Spatial Index Research Articles

Spatial Index Structures for Modern Storage Devices: A Survey

To optimize the processing of spatial queries, there is an increasing interest in combining <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial index structures</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">modern storage devices</i> like flash-based Solid State Drives, PCM, and 3D Xpoint. These devices have several advantages compared to classical Hard Disk Drives, such as lower power consumption, and faster reads and writes. However, modern storage devices have changed the paradigm of data management because of their intrinsic characteristics, such as asymmetric read and write costs. Intending to exploit the benefits of modern storage devices, the development of spatial index structures for these devices has been an emerging research topic with recent and constant advances in the literature. This includes the adaptation of existing spatial index structures like the R-tree, or even the design of innovative structures. In this article, we present a comprehensive survey that highlights the key ideas, compares the main characteristics, and discusses the advantages and disadvantages of spatial index structures for modern storage devices. Further, we study how experimental evaluations have been conducted to empirically compare these structures. Finally, we discuss challenges and identify potential future trends when indexing spatial data in this era of modern storage devices.

Anomaly Detection in Social-Aware IoT Networks

Anomaly event coverage is usually related to several attributes, among which the primary attribute dominates at the time of improving detection efficiency. In the case of Internet of Things (IoT) devices with complex social-aware relationships, IoT nodes with primary attributes should cooperate with each other through their social-aware interactions, to detect potential event anomalies and further determine the coverage of such anomalies. Existing research has put a lot of effort into designing IoT detection frameworks to discover anomalous sensor data, rarely caring about the social-aware interactions. This paper targets this important efficiency problem, and develops a novel anomaly detection mechanism in collaborative social-edge-cloud architecture. The focus of it is to first construct a vector space based Aggregation Behavior Comparison Detection Model, and quantify the change of monitoring behavior by defining the clustering threshold of vector space. This can quickly judge whether a local social network is abnormal and speed up the abnormal detection rate. If it is, a Social Behavior Correlation Detection Model is further designed based on the correlation of primary attributes derived from the dominating social-aware interaction behavior captured by (primary) edge nodes. This strategy can help detect specific “abnormal" areas managed by one or more edge devices with higher accuracy. In the process of anomaly detection, we also propose a spatial index tree to store the information of IoT nodes, so as to effectively collect and route the perceived data of IoT nodes for anomaly analysis. Experimental results demonstrate that our anomaly detection method promotes the detection efficiency and accuracy in comparison with the state of art’s techniques.

Research on the Correlations between Spatial Morphological Indices and Carbon Emission during the Operational Stage of Built Environments for Old Communities in Cold Regions

The escalation of the urban population and energy demands has exacerbated the carbon emission intensity at the operational stage of urban old communities. The spatial elements of the built environments comprising building groups, roads and landscape, and the spatial morphology of these elements, are endowed not only with human activities but also impact local microclimates and overall carbon emissions. Nonetheless, little attention has been paid to the correlation mechanism between the spatial morphology of the urban built environments and carbon emissions. In this paper, the aim is to combine carbon emissions simulation and statistical analysis to find the correlation between the spatial morphological indices and carbon emissions and to bridge the gaps. Thus, guided by the principles of urban energy modeling, this research adopts a parametric process of “information model construction–carbon emission simulation–statistical analysis”. First, taking 60 typical samples of an old community in Jinan, China, as objects, morphological indices such as density, texture and layout are analyzed through regression analysis to highlight their impacts on carbon emissions. Then, a carbon emission prediction model based on spatial morphological indices is established and verified. The results show that the floor area ratio (FAR), building coverage ratio (BCR), enclosure degree (ED), shape factor (SF) and average road aspect ratio (AS) have significant impacts on carbon emissions during the operational stage. Among these indices, the FAR and the ED are identified as the pivotal influencers. The findings confirm the important role of spatial morphological design of old communities in cold regions in improving urban carbon reduction potential, and they provide theoretical underpinnings and empirical data as references for urban morphology design formulated within the context of low-carbon objectives.

The synergy between temporal and spatial effects of human activities on CO2 emissions in Chinese cities

Based on the IPAT (I = human impact, P = population, A = affluence, and T = technology) framework, we used the temporal and spatial Logarithmic Mean Divisia Index decomposition approach to investigate whether the temporal and spatial impacts of human activities on carbon emissions are synergistic in 330 prefecture-level cities in China from 2003 to 2017. The results showed that carbon emissions in Chinese cities increased to different degrees during the study period, and a spatial distribution pattern could be recognized, with high levels in the East and South, and low levels in the West and North. Carbon emissions in cities in North China and in the eastern coastal areas displayed a cluster pattern higher than the national average level, although in most cities were lower than the national average. With respect to temporal effects, we found that although the decrease in carbon intensity played an important role in reducing carbon emissions in cities, it was not sufficient to offset the incremental gains associated with economic growth. The effect of population change was not apparent. With respect to spatial effects, we found that the effect of carbon intensity in most cities enhanced the difference with the national average carbon emission, whereas the effect of economic development reduced it. The effect of population size on spatial differences in carbon emission was bound by the Heihe–Tengchong line, forming a distribution pattern in which the difference in emissions was enhanced in the Southeast and reduced in the Northwest. The temporal and spatial impacts of human activities on carbon emissions in most cities were not synergistic. These findings can provide a reference for formulating differentiated carbon emission reduction policies among cities.

Constructing an urban cooling network based on PLUS model: Implications for future urban planning

Many studies on mitigation of the surface urban heat island (SUHI) are conducted from the perspective of landscape patch allocation, and few studies have investigated the overall impact of network connectivity on SUHI. In this study, an ecological network framework is proposed to identify the key patches and cooling corridors. Based on surface temperature inversion data, morphological spatial pattern analysis (MSPA) and landscape connectivity indexes are used to identify heat island and cold island patches with high ecological value on which to construct the network. The innovative patch-generating land use simulation (PLUS) model is introduced to construct a resistance surface, and Circuit theory (CT) is used to connect cold islands and heat islands in cooling corridors. The results show that from 1999 to 2019, land surface temperature (LST) increased significantly in the city of Haikou, the extreme LST became more and more significant, and the area with the highest temperatures showed a southward trend. The connectivity between cold and heat island patches declined, and the Loop, Bridge and Branch areas in heat islands increased. The latter scenario is conducive to the construction of cooling corridors. The land cover index had a great influence on SUHI, and the resistance surface presented a distribution pattern with high values in the northwest and low values in the southeast, which is consistent with the distribution of LST. Finally, we extracted the resultant cold-heat island cooling network, which was mainly concentrated along the southern edge of the study area, and found that corridor construction faced fewer obstacles in 2019 than in 1999. This study can guide urban planners to reinforce the parts of ecological networks that will mitigate the urban heat island effect.

Disentangling relations between dynamic urban structure and its efficiency in 287 cities across China

Conventionally urban structures are mainly inferred from the spatial arranges of related urban elements such as land use and population density. China's unprecedented urbanization process has significantly changed urban spatial structure and it is not clear how urban structure impacts the sustainability and livability of cities across a large geographic space. Furthermore, urban structure always shows dynamical variations during the day and over the night, which are a challenge to be captured by the conventional research methods and data. In this paper, we evaluated and compared the characteristics of the urban structure of the 287 cities in China by examining the dynamic changes of urban human activities distribution inferred from location requests collected by Tencent's platform. We developed two new indexes, the Dispersion Variation Index (DVI) and Spatial Dispersion Variation Index (SDVI), using the Lorenz curve, Gini coefficient, and adjusted Ripley's K function to describe the overall agglomeration intensity and the temporal variability of human activities. We also applied the urban scaling law to create two further indexes, the Scale-Adjusted Metropolitan Indicator DVI (SAMI_DVI) and the Scale-Adjusted Metropolitan Indicator SDVI (SAMI_SDVI), which enable us to evaluate the efficiency of urban structures. Our results show that the daily fluctuations of the urban structure are related to urban population, and also impacted by urban planning, topography, and other factors. The 1st-tier cities have the highest DVI (median: 0.020) and SDVI (0.052) values, which suggest the most volatile urban structure during the day. The 2nd-tier cities have the lowest urban structure efficiency, as suggested by higher SAMI_DVI (0.17) and SAMI_SDVI (0.28) values. Also, 69.2% and 76.9% of the 2nd-tier cities have DVI and SDVI values over the predicted values based on their population sizes. We also demonstrate that cities with high unstable structural characteristics tend to exhibit lower commuting efficiency and higher carbon emission intensity. Our framework outperforms previous metrics, is highly scalable, and can be implemented at little cost, allowing studies to be conducted even in small and medium-sized cities with little traditional data.

Spatial indexing and searching using parallel wavelet tree

Spatial indexing and searching using parallel wavelet tree

Segregated by design? Street network topological structure and the measurement of urban segregation

Racial residential segregation is a longstanding topic of focus across the disciplines of urban social science. Classically, segregation indices are calculated based on areal groupings (e.g., counties or census tracts), with more recent research exploring ways that spatial relationships can enter the equation. Spatial segregation measures embody the notion that proximity to one’s neighbors is a better specification of residential segregation than simply who resides together inside the same arbitrarily drawn polygon. Thus, they expand the notion of “who is nearby” to include those who are geographically close to each polygon rather than a binary inside/outside distinction. Yet spatial segregation indices often resort to crude measurements of proximity, such as the Euclidean distance between observations, given the complexity and data requirements of calculating more theoretically appropriate measures, such as distance along the pedestrian travel network. In this paper, we examine the ramifications of such decisions. For each metropolitan region in the U.S., we compute both Euclidean and network-based spatial segregation indices. We use a novel inferential framework to examine the statistical significance of the difference between the two measures and following, we use features of the network topology (e.g., connectivity, circuity, throughput) to explain this difference using a series of regression models. We show that there is often a large difference between segregation indices when measured by these two strategies (which is frequently significant). Further, we explain which topology measures reduce the observed gap and discuss implications for urban planning and design paradigms.

Influence of university campus spatial morphology on outdoor thermal environment: A case study from Eastern China

Outdoor thermal comfort is crucial for creating sustainable and livable urban spaces. Studying the distribution and impact factors of the thermal environment can provide theoretical support for improving the thermal environment and spatial planning. This study investigates the influence of well-defined boundary spaces on outdoor thermal comfort and proposes design measures to improve spatial thermal comfort. High-resolution data obtained from unmanned aerial vehicles (UAVs) is integrated into outdoor microclimate simulations to enhance the accuracy and precision of the models. The Physiological Equivalent Temperature (PET) index is employed as an evaluation indicator, considering the categorization of PET values into different comfort levels. Additionally, the axial evaluation method is introduced to assess thermal comfort more accurately, reflecting the perceived thermal comfort by individuals. Through a case study in a mixed-use and clear boundary area, the research identifies the variables that influence outdoor thermal comfort and provides design guidelines to enhance spatial thermal comfort. The correlations between the spatial morphology index and PET were analyzed by multiple regression. The findings contribute to the understanding of outdoor thermal comfort in complex urban environments and offer valuable insights for the design and planning of comfortable and sustainable outdoor spaces.

Glucose metabolism, hippocampal subfields and cognition in first-episode and never-treated schizophrenia

BackgroundPrevious studies have indicated that glucose metabolism and altered hippocampal structure and function play a pivotal role in cognitive deficits in schizophrenia (SZ). This study was designed to explore the inter-relationship between glucose metabolism, hippocampal subfield volume, and cognitive function in the antipsychotics-naive first episode (ANFE) SZ patients. MethodsWe chose the fasting insulin, glucose, and insulin resistance (HOMA-IR) index as biomarkers of glucose metabolism. Cognitive function was assessed by the MATRICS Consensus Cognitive Battery (MCCB). The hippocampal subfield volume, glucose metabolism biomarkers, and cognitive function were evaluated in 43 ANFE SZ and 29 healthy controls (HCs). ResultsCompared with HCs, SZ patients had higher fasting blood glucose and insulin levels and HOMA-IR (all p < 0.05). Correlation analysis revealed that category fluency performance was positively associated with fasting glucose level. Fasting insulin or HOMA-IR was positively associated with the hippocampal subfield volume in patients (all p<0.05). Moreover, the spatial span index score was associated with the volume of the right presubiculum, subiculum, and right hippocampal tail. In addition, multiple regression analysis found that the interaction effects of insulin × right fimbria or insulin × left fimbria were independent predictors of the MCCB total score. ConclusionsOur findings suggest that abnormal glucose metabolism and cognitive decline occur in the early stage of SZ. The interaction between abnormal glucose metabolism and hippocampal subfields was associated with cognitive functions in SZ.

Identification of Bird Habitat Restoration Priorities in a Central Area of a Megacity

Rapid global urbanization has caused habitat degradation and fragmentation, resulting in biodiversity loss and the homogenization of urban species. Birds play a crucial role as biodiversity indicators in urban environments, providing multiple ecosystem services and demonstrating sensitivity to changes in habitat. However, construction activities often disrupt urban bird habitats, leading to a decline in habitat quality. This paper proposes a framework for prioritizing habitat restoration by pinpointing bird hotspots that demand attention and considering the matching relationship between bird richness and habitat quality. Shanghai represents a typical example of the high-density megacities in China, posing a significant challenge for biodiversity conservation efforts. Utilizing the random forest (RF) model, bird richness patterns in central Shanghai were mapped, and bird hotspots were identified by calculating local spatial autocorrelation indices. From this, the habitat quality of hotspot areas was evaluated, and the restoration priority of bird habitats was determined by matching bird richness with habitat quality through z-score standardization. The results were as follows: (1) Outer-ring green spaces, large urban parks, and green areas along coasts or rivers were found to be the most important habitats for bird richness. Notably, forests emerged as a crucial habitat, with approximately 50.68% of the forested areas identified as hotspots. (2) Four habitat restoration types were identified. The high-bird-richness–low-habitat-quality area (HBR-LHQ), mainly consisting of grassland and urban construction land, was identified as a key priority for restoration due to its vulnerability to human activities. (3) The Landscape Shannon’s Diversity Index (SHDI) and Normalized Difference Vegetation Index (NDVI) are considered the most significant factors influencing the bird distribution. Our findings provide a scientifically effective framework for identifying habitat restoration priorities in high-density urban areas.

Quantification of the spatial resilience of the Spanish rural landscape to fire occurrence analysed using the SISPARES network of plots

ContextIn Spain, fire is a tool used for management in rural landscapes but also wildfires can play a disruptive socio-ecological role. Therefore, its spatial structure analysis and spatial resilience quantification to wildfire is useful for territorial planning and management.ObjectivesWe evaluate the relationship between the spatial structure of Spanish rural landscapes with the historical wildfire frequency. We develop and calculate indices of spatial resilience to wildfires and large wildfires.MethodsWe followed a stratified sampling based on the biogeoclimatic classes grouping by ecoregions. We calculated landscape metrics with Fragstats and collected wildfires occurrence from a national wildfire database. Spatial resilience was estimated with a self-elaborated index, using wildfires data and landscape structure subjected to a MANOVA and to a linear regression model.ResultsThe Northwest region suffered more wildfires and large wildfires than the other ecoregions. The differences in frequency of large wildfires were small and low in. Some landscape metrics were positively related to wildfire frequency (forest plantations and scrublands) and other negatively (“dehesas” and crops). Spatial resilience to wildfires and large wildfires was lower in the Northwest area but for large wildfires were also low in the Mediterranean Littoral and Central region.ConclusionsThe relationship between the frequency of the two types of wildfires and landscape structure determines differences in spatial resilience indices. Large wildfires are positively related to shrubland abundance, whereas small wildfires are positively related to heterogeneous agricultural areas. Therefore, spatial resilience indices could be used as early warning tools to prevent wildfires.

Spatiotemporal Patterns and Driving Factors of the Ecological Environmental Quality along the Jakarta–Bandung High-Speed Railway in Indonesia

Economic globalization and rapid urbanization have affected ecological environments in global regions to different degrees. Timely, objective and quantitative evaluations of the spatiotemporal variations in regional ecological environmental quality are the key to environmental protection and decision making. The spatial distributions of ecological environment quality levels along the Jakarta–Bandung high-speed railway from 2000 to 2020 were obtained based on Google Earth Engine and the Remote Sensing Ecological Index (RSEI). Then, the Theil–Sen median and Mann-Kendall methods were used to assess the temporal RSEI trend; the spatial autocorrelation evaluation index was used to evaluate RSEI clustering on a spatial scale. The results show that the overall ecological environmental quality from 2000 to 2020 was moderate, with temporally fluctuating changes and very significant spatial clustering. Approximately 20% of the area showed very strong changes (improvement or degradation). Areas with relatively better ecological quality were located mainly in relatively high-elevation and high-vegetation-coverage regions, while areas with poorer ecological quality were located mainly in the four major cities, including Jakarta, with concentrated populations and developed economies. The research results provide local governments with scientific suggestions regarding the synergistic development of high-speed railway construction and ecological environmental protection.

A first study on the bioaccumulation of trace metals in Rhyssoplax olivacea (Mediterranean Polyplacophora)

This study investigates, for the first time, the bioaccumulation of trace metals in the chiton Rhyssoplax olivacea. Fe, Cu, Co, Cr and Cd were measured in the shell and soft tissue of R. olivacea sampled in five sites along the Algerian west coast during the cold and hot seasons. Physiological and contamination indices were calculated. The condition index provides information on habitat quality and on R. olivacea reproductive performance and physiological status. The metal/shell-weight index informs on the bioavailability of trace metals. The trace element pollution index is used to assign a global contamination status to the studied sites. The trace element spatial variation index ranks Cd and Cr as trace metals of primary environmental concern based on the overall variability of their levels. An exhaustive review compiling data on trace element bioaccumulation in chitons is performed. The potential use of R. olivacea as bioindicator species is discussed.

Spatial mismatch and its evolution of new energy consumption, industrial structure upgrading, and carbon carrying capacity.

In view of the problems of insufficient exploitation of carbon emission reduction potential and low-carbon emission reduction efficiency caused by the spatial mismatch of economy, society, and ecological environment, this study used the ArcGIS10.8 intuitive expression tool, the barycenter model, and the spatial mismatch index to systematically investigate the spatial-temporal pattern and its spatial mismatch characteristics of new energy consumption, industrial structure upgrading, and carbon carrying capacity in 30 provinces in China from 2009 to 2019. The findings of this study included mainly the following aspects. (1) The new energy consumption level showed the significant differences in spatial aggregation. The industrial structure upgrading level decreased from the southeast to the northwest. The carbon carrying state showed the gradual geographical evolution characteristics of the empty load, suitable basic load, suitable load, and overload from the southeast to the northwest. (2) The barycenter of new energy consumption shifted from the south to the north; the barycenter of industrial structure upgrading presented a phased migration trajectory of first to the northwest, then to the south, and then to the southwest; the barycenter of carbon carrying capacity oscillated from the southwest to the northeast. (3) Provinces with the positive mismatch of new energy consumption, industrial structure upgrading, and carbon carrying capacity were widely distributed, but provinces with the negative mismatch were sporadically distributed. The spatial mismatch degree of the three elements tended to expand on the whole, but the direction and magnitude of change were different. The high mismatch areas showed a trend of agglomeration in eastern coastal economic circles such as the Bohai Rim, the Yangtze River Delta, and the Pearl River Delta. Moreover, the high mismatch areas showed a trend of westward spread. (4) The contribution of province to the overall spatial mismatch decreased from the eastern to the western. This study would provide a reference for the related research on carbon peaking, carbon neutrality, and the coordinated high-quality development between economy-society and ecological environment.

Consequences of field obstacles on tactical, conditional, and emotional dimensions in young female soccer players during small-sided games

This study aims to compare tactical, conditional, and emotional responses of young female soccer players with two small-sided games (SSG) conditions – with and without field obstacles. Twelve Spanish under-12 female soccer players (age: 12.5 ± 0.7 years) played two different 6-a-side SSGs (i.e. 5 vs 5 plus goalkeepers): one with field obstacles (SSGobstacles) and the other without these (SSGfree). Positional data were gathered using a GPS to measure tactical (distance between players, stretch-index [SI], spatial exploration index [SEI] and their normalized approximate entropy measures [ApEn norm], and longitudinal and lateral synchronizations) and conditional performances (total and walking, jogging, running, and high-speed running distances). Participants assessed the intensity of their emotional experience using the BECS scale of perceived enjoyment and competence. SSGobstacles reduced the SEI values ( p = 0.038, Cohen’s d = −0.40 [−0.77; −0.02]), impaired lateral synchronization ( p &lt; 0.001, d = −0.62 [−0.89; −0.34]), and increased regularity (distance between playersApEn norm: p = 0.016, d = −0.32 [−0.58; −0.06]; SIApEn norm: p = 0.029, d = −0.42 [−0.79; −0.04]) of the tactical behavior in comparison to SSGfree. Players traveled more distance overall ( p = 0.009, d = 0.51 [0.13; 0.89]) and while running ( p = 0.028, d = 0.42 [0.04; 0.79]) during SSGobstacles in comparison to SSGfree. No significant differences between the two SSG conditions were apparent in players’ perceived enjoyment ( p = 0.060) and competence levels ( p = 0.530). Placing obstacles on the field during SSGs, compared with those free of obstructions, could decrease young female soccer players’ spatial exploration, stabilize their tactical behavior, and raise their physical effort.

Assessment of land use effect, mapping of human health risks and chemometric analysis of potential toxic elements in topsoils of Aran-o-Bidgol, Iran.

This study examines topsoil contamination in Aran-o-Bidgol urban region of central Iran, with a focus on potentially toxic elements (PTEs). A total of 135 topsoil samples in different land types were characterized, ranging from areas with agricultural farms, desert, industrial and residential activity, and brick kilns. The average concentrations of Cd, Pb, Cu, Ni, Cr, Co, Fe, Zn, and Mn were 0.72, 11.41, 14.82, 29.87, 51.13, 106.69, 8741.87, 48.59, and 346.42mgkg-1, respectively, which all exceed the local background levels. The results reveal that land use significantly affected PTE concentrations. Cr, Co, Mn, and Fe concentrations in soils of residential and brick kiln areas were especially high. In contrast, concentrations of Cu, Ni, and Zn were higher in agricultural and residential areas. Risk assessment analysis showed that the sum of toxic units for PTEs for brick kilns (1.72), residential (1.82), and agricultural (1.79) areas exceeded those of other land types and that Ni and Cr contributed the most to the high toxic risk index values. Both carcinogenic and non-carcinogenic risk indices of PTEs in soils were within an acceptable limit, except for the cancer risk of Ni (3.52E-04) and Cr (3.00E-04) among children. The spatial hazard index and carcinogenic health risk of PTEs showed that samples from the southwestern parts of the study area might pose significant health problems to adults and children. This study demonstrates how combining different techniques can help spatially characterize PTE accumulation and protect populations at risk.

Spatial Analysis of Dense LiDAR Point Clouds for Tree Species Group Classification Using Individual Tree Metrics

This study presents a method of tree species classification using individual tree metrics derived from a three-dimensional point cloud from unmanned aerial vehicle laser scanning (ULS). In this novel approach, we evaluated the metrics of 1045 trees using generalized linear model (GLM) and random forest (RF) techniques to automatically assign individual trees into either a coniferous or broadleaf group. We evaluated several statistical descriptors, including a novel approach using the Clark–Evans spatial aggregation index (CE), which indicates the level of clustering in point clouds. A comparison of classifiers that included and excluded the CE indicator values demonstrated their importance for improved classification of the individual tree point clouds. The overall accuracy when including the CE index was 94.8% using a GLM approach and 95.1% using an RF approach. With the RF approach, the inclusion of CE yielded a significant improvement in overall classification accuracy, and for the GLM approach, the CE index was always selected as a significant variable for correct tree class prediction. Compared to other studies, the above-mentioned accuracies prove the benefits of CE for tree species classification, as do the worse results of excluding the CE, where the derived GLM achieved an accuracy of 92.6% and RF an accuracy of 93.8%.

BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation.

In this work we present BIANCA-MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA-MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA-MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA-MS to other widely used tools. Second, we tested how BIANCA-MS performs in separate datasets. Finally, we evaluated BIANCA-MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA-MS clearly outperformed other available tools in both high- and low-resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA-MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA-MS is a robust and accurate approach for automated MS lesion segmentation.

Unraveling the Impact of COVID-19 Pandemic Dynamics on Commercial Water-Use Variation

Water use was impacted significantly by the COVID-19 pandemic. Although previous studies quantitatively investigated the effects of COVID-19 on water use, the relationship between water-use variation and COVID-19 dynamics (i.e., the spatial-temporal characteristics of COVID-19 cases) has received less attention. This study developed a two-step methodology to unravel the impact of COVID-19 pandemic dynamics on water-use variation. First, using a water-use prediction model, the water-use change percentage (WUCP) indicator, which was calculated as the relative difference between modeled and observed water use, i.e., water-use variation, was used to quantify the COVID-19 effects on water use. Second, two indicators, i.e., the number of existing confirmed cases (NECC) and the spatial risk index (SRI), were applied to characterize pandemic dynamics, and the quantitative relationship between WUCP and pandemic dynamics was examined by means of regression analysis. We collected and analyzed 6-year commercial water-use data from smart meters of Zhongshan District in Dalian City, Northeast China. The results indicate that commercial water use decreased significantly, with an average WUCP of 59.4%, 54.4%, and 45.7%during the three pandemic waves, respectively, in Dalian. Regression analysis showed that there was a positive linear relationship between water-use changes (i.e., WUCP) and pandemic dynamics (i.e., NECC and SRI). Both the number of COVID-19 cases and their spatial distribution impacted commercial water use, and the effects were weakened by restriction strategy improvement, and the accumulation of experience and knowledge about COVID-19. This study provides an in-depth understanding of the impact of COVID-19 dynamics on commercial water use. The results can be used to help predict water demand under during future pandemic periods or other types of natural and human-made disturbance.