Effect Of Spatial Scale Research Articles

Spatial scale effects of transportation, social and natural attributes of street environments on perceived activity opportunities for older adults

The street environment correlates with perceived activity opportunities for older adults. While numerous studies have examined the transportation and social attributes of street environments, the natural attributes have been less explored. Furthermore, the nonlinear relationship between the street environment and perceived activity opportunities across different spatial scales remains under-researched. We hypothesize that transportation, social, and natural attributes influence perceived activity opportunities, and their nonlinear effects vary across spatial scales. Therefore, we used activity survey data and multi-source big data from Dalian, China. Employing gradient boosted decision tree (GBDT) methods, we evaluated the nonlinear correlation between the street environment and perceived activity opportunities. Our study considered three community life circle scales: 5-min, 10-min, and 15-min. The results indicated that street features varied significantly at different spatial scales. Specifically, factors such as density of street network, diversity of street interface, distance to the nearest public transport station, accessibility of green space, diversion ratio, and distance to the nearest blue space notably influence perceived activity opportunities across these scales. The study also found that the nonlinear correlations between street environments and perceived activity opportunities were prevalent and varied across spatial scales. These results suggest that priorities for street environment improvements should account for these spatial scale variations. Our research provides multi-scale recommendations for the development of sustainable transportation, age-friendly communities, and the promotion of aging in place.

Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901.

Citizen science observations shed light on how anthropogenic food sources influence wildlife disease.

Research Highlight: Knutie, S., Bahouth, R., Bertone, M., Webb, C., Mehta, M., Nahom, M., Barta, R., Ghai, S., Love, A., Horan, S., Soldo, A., Cochrane, E., Bartholomew, J., Cowan, E., Bjerke, H., Balenger, S., Butler, M., Cornell, A., Kennedy, A., Rolland, V., Schultz, E., Stanback, M., Taff, C., Albery, G. (2024). Understanding spatiotemporal effects of food supplementation on host-parasite interactions using community-based science. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14155. Wildlife have become increasingly dependent on anthropogenic food supplementation, resulting in altered nutritional intake and inter- and intraspecific interactions. Subsequently, supplemental feeding can affect both the immunological function of individuals and transmission dynamics among individuals and species. The magnitude of the effect supplemental feeding has on disease is likely to vary across time and space with the nutritional demands of hosts. However, the broad temporal or spatial scale effects of supplementation are poorly understood. Recently, Knutie etal. (2024) introduced their citizen science program, the Nest Parasite Community Project, a broadscale coordinated effort by scientists and the public to monitor box nesting wild birds and their ectoparasites across the eastern United States. The authors amassed an impressive 4-year data set with hundreds of nests spanning the entire US breeding range of Eastern bluebirds (Sialia sialis). In the first study to come from the project, the authors demonstrate that the effects of food supplementation on host-parasite interactions vary across time and space and do not consistently influence host-parasite outcomes, highlighting that host-parasite interactions are often context dependent and influenced by many environmental factors (e.g. weather and habitat quality). The authors also found that supplemental feeding increases host fitness, regardless of parasitism. The study provides strong evidence that citizen science projects can help broaden our understanding of how human food sources influence wildlife disease in various environmental contexts.

Analyzing the spatial scale effects of urban elements on urban flooding based on multiscale geographically weighted regression

In the context of rapid urbanization and the frequent occurrence of extreme rainfall in cities, the risk of flooding in the future will further increase, and the problem of urban flooding cannot be ignored. Urban elements exhibit significant spatial heterogeneity, which largely determines the spatial distribution differences in urban flooding. Therefore, it is important to clarify the scale of influence of different urban elements and explore their scale effects on urban flooding to accurately assess the risk of urban flooding. Taking Zhengzhou City, China, as the study area, this study analyzed the urban elements associated with urban flooding, quantified the scale of the influence of urban elements on flooding using multiscale geographically weighted regression (MGWR), and further explored the spatial scale effects of urban elements on urban flooding. The results showed that MGWR can better fit the spatially non-uniform distribution of urban flooding and that the scale of the influence of urban elements on urban flooding can be reflected by the bandwidth of MGWR. The results of MGWR indicated that the bandwidths of elevation, number of drainage outfalls (NDO1Abbreviations: RRP, Rainfall return period; DDR, Distance to river; NDO, Number of drainage outfalls; ILMFCP, Input level of materials for flood control projects; PRL, Proportion of residential land; PCL, Proportion of commercial land; PIL, Proportion of industrial land; PPSL, Proportion of public service land; PRA, Proportion of road area; PGL, Proportion of green land; PD, Population density; MI, Manpower input; GDP, Gross domestic product; ILER, Investment level in education and research.1), distance to river (DR), Gross domestic product (GDP), proportion of residential land (PRL), and proportion of commercial land (PCL) were small, and their influence scales were localized. In contrast, the influence of rainfall return period (RRP), slope, proportion of industrial land (PIL), proportion of public service land (PPSL), proportion of road area (PRA), proportion of green land (PGL), input level of materials for flood control projects (ILMFCP), population density (PD), manpower input (MI), and investment level in education and research (ILER) were at global scales. The most influential factors for urban flooding were RRP, PD, and MI. Slope, DR, and PRA had less influence on urban flooding. This study helps improve the effectiveness of urban flood prevention and mitigation efforts.

The Influence of Vegetation Environment on Thermal Experience in Hot Summer: A Case Study from Perspectives of Fitting Scale and Gender Disparity

Vegetation exerts a significant cooling effect, particularly during the hot summer; however, the spatial scale effects and gender difference among occupants’ subjective thermal comfort remain elusive. Developing a comprehensive model to elucidate the multidimensional relationship between green spaces and thermal experience holds paramount importance. Taking Longzi River Park in Zhengzhou city as a case study, this research examined the influence of vegetation on thermal experience by using structural equation modeling (SEM) from perspectives of fitting scale and gender disparities. It was found that (1) The vegetation environment not only influences thermal sensation, comfort and demand independently, but also influences the pathway between them. These influence paths constitute a complex causal network, functioning as a framework of “sensation → comfort → demand” and its influencing factors. (2) There exists a scaling effect in the pathway framework, which conforms to a threshold of 10 m for the inner radius and 30 m for the outer radius. The goodness of SEM model fit declines with the increase in either the inner radius or the outer radius, or both. (3) Differences in genders are exhibited for the pathway framework, with the vegetation exerting a stronger influence on female sensation and comfort, as well as male demand. The pathway from sensation to comfort to demand is more pronounced in male populations. The research findings contribute to the development of improved and sustainable vegetation distribution in urban parks.

Exploring Spatial Non-Stationarity and Scale Effects of Natural and Anthropogenic Factors on Net Primary Productivity of Vegetation in the Yellow River Basin

Investigating the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its influencing factors are crucial for green and low-carbon development and facilitate human well-being in the Yellow River Basin (YRB). Although the research on NPP has advanced rapidly, in view of the regional particularity of the YRB, the persistence of its NPP change trend needs to be further discussed and more comprehensive impact factors need to be included in the analysis. Meanwhile, the spatial non-stationarity and scale effects of the impact on NPP when multiple factors are involved remain uncertain. Here, we selected a total of twelve natural and anthropogenic factors and used multi-scale geographically weighted regression (MGWR) to disentangle the spatial non-stationary relationship between vegetation NPP and related factors and identify the impact scale difference in the YRB. Additionally, we analyze the spatiotemporal variation trend and persistence of NPP during 2000–2020. The results revealed the following: (1) The annual NPP showed a fluctuating increasing trend, and the vegetation NPP in most regions will exhibit a future trend of increasing to decreasing. (2) The effects of different factors show significant spatial non-stationarity. Among them, the intensity of the impact of most natural factors shows a clear strip-shaped distribution in the east-west direction. It is closely related to the spatial distribution characteristics of natural factors in the YRB. In contrast, the regularity of anthropogenic influences is less obvious. (3) The impact scales of different factors on vegetation NPP were significantly different, and this scale changed with time. The factors with small impact scales could better explain the change in vegetation NPP. Interestingly, the impact size and scale of relative humidity on NPP in the YRB are both larger. This may be due to the arid and semi-arid characteristics of the YRB. Our findings could provide policy makers with specific and quantitative insights for protecting the ecological environment in the YRB.

Scale dimensions in public participation GIS: an overview for planning and research

Questions of spatial scale infiltrate Public Participation GIS (PPGIS) in multiple phases of the data collection from the design of a PPGIS survey to the use of the produced spatial knowledge. This paper provides an overview on how spatial scale and scale effects influence the use of PPGIS tools and the validity and usability of the spatial data they produce. The paper approached scale from two complimentary perspectives: as a concept in human geography and as an attribute of cartographic representation in geographic information science. This discussion is organized around three main viewpoints on scale and PPGIS, namely (1) scale in data collection denoting the geographic extent of the study area and the study population, (2) scale in survey design manifested in technical issues, cartographic representation, usability, and design of the mapping activities, and (3) scale and scale effects in the analysis of PPGIS data and the production of spatial knowledge. Together, these views on scale outline the main scale-related issues to consider when planning a PPGIS survey or working with secondary PPGIS data.

Effects of Spatial Type and Scale of Small Urban Open Spaces on Perceived Restoration: An Online Survey-Based Experiment

Small urban open spaces are critical components of high-density urban environments, and could bring multiple health benefits. However, the factors related to the restorative effect of these small urban open spaces are not well studied. This study explored how site type (lawn, water, and plaza) and spatial scales (10 × 10 m, 20 × 20 m, 30 × 30 m, and 40 × 40 m) might be associated with small urban open space’s restorative effects. We created the virtual scene images of the 12 sites and used PRS-11 to measure 1130 participants’ perceived restoration when viewing those images. The results indicated that lawn has the highest restorative effects, and are the most preferred. No significant differences in the restorative effects of sites with different spatial scales were detected, even a 10 × 10 m site has considerable restorative effects. We found high preference contributes to larger differences in the restorative effects of lawns and plazas. Those who prefer the sites may gain more restoration increases when viewing lawn scenes compared to plaza scenes. Moreover, adults over 50 years old have higher perceived restorations, and young adults aged 18–25 have a greater increase in restoration between plaza scenes and natural scenes, indicating young adults could benefit more from the building of small green spaces. These findings have direct implications for design practice. More small urban open spaces of lawn and water should be built to provide more restoration benefits, especially for young people.

Trade-Off and Synergy Relationships and Driving Factor Analysis of Ecosystem Services in the Hexi Region

The Hexi region, located in a sensitive and fragile ecological zone in northwest China, requires a scientific assessment of ecosystem services and their interactions. Identifying the main factors influencing spatial distribution is crucial for the sustainable development and effective management of the region. This study evaluates key ecosystem services, including regulating services (water conservation, soil conservation, carbon storage) and provisioning services (NPP), using Spearman’s correlation and pixel-by-pixel spatial analysis to calculate spatial trade-offs and synergies. Geographic detectors were used to uncover the underlying driving mechanisms. The results show that: (1) From 2000 to 2020, soil conservation, NPP, and carbon storage showed fluctuating growth, while water conservation declined. Spatially, high-value areas of water conservation, carbon storage, and NPP were concentrated in the central and southern areas, while high values of soil conservation services were mainly in the northwest and southeast regions. (2) The trade-offs and synergies among various ecosystem services exhibit temporal shifts, along with spatial scale effects and heterogeneity. In the study area, the proportion of pixels showing a trade-off relationship between water conservation and soil conservation, and between water conservation and NPP, accounts for 48.21% and 21.42%, respectively. These trade-offs are mainly concentrated in the central and southeastern regions, while the northwestern counties predominantly exhibit synergies. (3) Precipitation was the dominant factor for water conservation, carbon storage, and NPP, as well as for the trade-offs among these services. Among natural factors, climatic factors were significantly more influential than socio-economic factors, and the interaction between two factors had a greater explanatory power than single factors.

Effect of spatial scale, color infrared and sample size on learning poverty from aerial images

There is a growing amount of literature that focuses on using machine learning algorithms to predict poverty from satellite and aerial images on a low regional level, particularly for countries without a high-quality official statistical system. The data used for annotating images and training an algorithm are generally based on sample surveys. In The Netherlands, statistics on income and poverty are derived from tax registers resulting in a complete enumeration of the Dutch population. In this paper, we use this complete enumeration to simulate to which extent satellite or aerial images can predict poverty on low regional levels. After geocoding these households, aerial images are annotated and a deep learning algorithm is trained to predict poverty. The precision of the predictions is evaluated by comparing it with the true poverty fractions known from tax registers. The effect of different spatial scales (1-ha vs. 25-ha images), spectral bands (RGB vs. CIR), and sample sizes for the training set are compared. It is discussed how this information can be used in the production of low regional statistics on poverty in countries where high-quality official statistical systems are lacking.

Spatial Scale Effect on Fractional Vegetation Coverage Changes and Driving Factors in the Henan Section of the Yellow River Basin

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m ( q = 0.48).

Spatial scale effects on the value of ecosystem services in China's terrestrial area

Identifying the response characteristics of ecosystem service value (ESV) to changes in spatial scales, known as spatial scale effects, is crucial in guiding the development of corresponding management strategies. This paper examines ESV in China's terrestrial area during the year 2020, revealing the spatial aggregation characteristics of ESV and the trade-off and synergistic relationships of ecosystem services at different spatial scales, ranging from 1 km × 1 km–10 km × 10 km, with a gradient of 1 km. The results indicate: 1) The distribution pattern of ESV in China's terrestrial area is "high in the southeast and low in the northwest." 2) The spatial characteristics of ESV in China's terrestrial area undergo a distinct transition at the 3 km × 3 km scale. In detail, the spatial clustering features show a trend of first rising and then falling with the increase in spatial scale, while the synergistic relationships between different ecosystem services strengthen and the trade-off relationships weaken with the increase of the spatial scale. These findings can inform the formulation of differentiated ecological protection compensation policies and enable cross-area trading of ecological values in China.

Spatial and Temporal Scale Effects of Landscape Pattern on Riverine Nitrogen and Phosphorus Nutrients in the Qingshan Lake Watershed

The influences of landscape pattern on water quality are dependent on spatial-temporal scales. However, the effects of landscape composition, landscape configuration, and landscape slope metrics on seasonal water quality at different spatial scales remain unclear. Based on the total nitrogen, total phosphorus, nitrate-N, and ammonium-N data from 26 sampling sites in the Qingshan Lake watershed, this study coupled landscape pattern analysis, redundancy analysis, and partial redundancy analysis to quantify the spatiotemporal scale effects of landscape pattern on riverine nitrogen （N） and phosphorus （P） concentrations. The results showed that： ① The explanatory ability of landscape pattern at the sub-watershed scale on riverine N and P concentrations was 6.8%-8.4% higher than that at the buffer scale, and this effect was more obvious in the dry season. ② At the sub-watershed scale, the percentage of forestland and the interspersion and juxtaposition degree of residential land had a greater influence on riverine N and P concentrations. At the buffer scale, the slope of farmland and residential land and the aggregation degree of forestland patches were the key factors affecting riverine N and P concentrations. ③ The contribution rate of landscape configuration to riverine N and P concentration variations （20.1%-36.5%） was the highest. The sensitivity of the effect of landscape configuration on riverine N and P concentrations to seasonal changes was the highest, and the effect of landscape slope on riverine N and P concentrations had the highest sensitivity to spatial scale changes. Therefore, landscape pattern-regulated non-point source pollution should be considered from a multi-scale perspective. These results can provide scientific basis for the formulation of landscape pattern optimization measures aiming at non-point source pollution control.

Dynamic Evolution of Multi-Scale Ecosystem Services and Their Driving Factors: Rural Planning Analysis and Optimisation

Rural areas provide ecosystem services (ESs) to urban metropolitan regions. These services are threatened by the constant pressure of urbanisation and new interest in rural development. This has heightened the conflict between environmental concerns and developmental needs, thereby presenting significant land management and rural planning challenges. Employing a quantitative measurement and optimisation framework, we investigate six representative ES variables to assess planning strategies that can address this contradiction. We used a suburban rural area around Nanjing, China, as our study area. We collected spatial data from 2005 to 2020 at two scales (village level and 500 m grid) to map ESs, quantify interactions (trade-offs and synergies among ES bundles), and identify the social, ecological, and landscape drivers of rural change. Based on this, rural planning strategies for optimising ESs at different scales have been proposed. Our findings include (1) spatial heterogeneity in the distribution of ESs, (2) the identification of seven synergistic and eight trade-off pairs among ESs, (3) a spatial scale effect in suburban rural areas, and (4) the spatial trade-offs/synergies of ESs exhibiting a ‘Matthew effect’. The identification of key trade-offs and synergistic ES pairs and the categorisation of ES bundles form the basis for a multi-scale hierarchical management approach for ESs in the region. By examining the commonalities and variations in drivers across diverse scales, we established connections and focal points for spatial planning. We use these findings to propose spatial planning and landscape policy recommendations for rural suburban areas on multiple scales. This study aims to provide a comprehensive and detailed spatial optimisation strategy for rural areas that can help contribute to their revitalisation.

Rising risk and localized patterns of Indian Summer Monsoon rainfall extremes

Extreme precipitation during the Indian Summer Monsoon period often leads to heavy flooding, posing risks to infrastructure and human life across India. Modelling such phenomena requires developing spatio-temporal models that incorporate spatial dependence for a more realistic representation of spatial variation over a large domain. In this study, we employed a multivariate max-stable process-based model to capture the joint spatial dependence and to estimate the impact of geographic and temporal covariates on the risk associated with anomalous rainfall. Data pooling and local optimization at the cluster level help to reduce the uncertainty in the parameter estimates, whereas the incorporation of El Niño Southern Oscillations (ENSO) enhances the predictive power of the models. The results indicate that approximately 75% of India's land area is at risk of experiencing heavy rainfall with a 50-year or more return period. The proposed max-stable process model adequately captured the nonstationary behaviour of Indian Summer Monsoon Rainfall (ISMR) extremes. A post-hoc analysis was conducted to assess the practical significance of differences in estimated parameters. This is essential for understanding the real-time variations in the extremal behaviour of ISMR across the country. In addition, the phase effect of ENSO and spatial scale (cluster/region) changes were quantified using a linear mixed-effects model. The results suggest that the impact of ENSO becomes more pronounced for longer return periods and intensification of ISMR extremes occurs due to the shift from El Niño to La Niña phases. Moreover, treating spatial scales as a random component in the modelling reveals that ISMR extremes are highly localized in nature. Specifically, around 80% of the variation in ISMR extremes for shorter return levels is attributed to the nested spatial variation.

Detection of functional diversity gradients and their geoclimatic filters is sensitive to data types (occurrence vs. abundance) and spatial scales (sites vs. regions)

Functional diversity (FD) reflects within- and between-site variation of species traits (α- and β-FD, respectively). Understanding how much data types (occurrence-based vs. abundance-weighted) and spatial scales (sites vs. regions) change FD and ultimately interfere with the detection of underlying geoclimatic filters is still debated. To contribute to this debate, we explored the occurrence of 1690 species in 690 sites, abundances of 1198 species in 343 sites, and seven functional traits of the Atlantic Forest woody flora in South America. All FD indices were sensitive and dependent on the data type at both scales, with occurrence particularly increasing α richness and dispersion (occurrence > abundance in 80% of the sites) while abundance increased β total, β replacement, and α evenness (abundance > occurrence in 60% of the sites). Furthermore, detecting the effect of geoclimatic filters depended on the data type and was scale-dependent. At the site scale, precipitation seasonality and soil depth had weak effects on α- and β-FD (max. R2 = 0.11). However, regional-scale patterns of α richness, dispersion, and evenness strongly mirrored the variation in precipitation seasonality, soil depth, forest stability over the last 120 kyr, and cation exchange capacity (correlations > 0.80), suggesting that geoclimatic filters manifest stronger effects at the regional scale. Also, the role of edaphic gradients expands the idea of biogeographical filters beyond climate. Our findings caution functional biogeographic studies to consider the effect of data type and spatial scale before designing and reaching ecological conclusions about the complex nature of FD.

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin-destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales-10-100 m and 100-3000 m-to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data.

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.

Scale effects and implications of the stochastic structure of customer water demands

ABSTRACT The effect of different temporal (from seconds to months) and spatial aggregation scales (from individual users to full urban areas) on water demand behavior has been explored to a limited degree. The effort described here extends those works by evaluating the scale effects of residential water consumption in a unique US data set that covers 10,000 households with a 1-gallon (3.79 L) hourly resolution over 2 years. A preliminary data analysis and a sequential Principal Component Analysis (PCA) is carried out to assess the effect of different temporal (weekly, daily, hourly) and spatial aggregation (individual meters and groups every 10, 100 and 1,000 meters) levels on demand. Results show that individual users act very differently from each other, and individual consumer variability is only canceled out when a significant number of households are aggregated. The implications of this finding are assessed from a hydraulic modeling perspective as the spatiotemporal scale of measurements may condition the type of analysis that can be carried out in practice. However, additional work is needed to explore the point at which it may be worth embracing a micro (per fixture/household) or a macro (per node/network) approach for different purposes.

Spatial scaling effects of gully erosion in response to driving factors in southern China

Spatial scaling effects of gully erosion in response to driving factors in southern China