Spatial and Temporal Trends of Persistent Organic Chemicals in Vietnam Soils

2011 ◽  
pp. 297-324
Keyword(s):  
Temporal Trends ◽  
Organic Chemicals ◽  
Spatial And Temporal Trends ◽  
Persistent Organic Chemicals

Spatial and temporal trends and effects of contaminants in the Canadian Arctic marine ecosystem: a review

1999 ◽  
Vol 230 (1-3) ◽  
pp. 83-144 ◽  
Author(s):  
D Muir ◽  
B Braune ◽  
B DeMarch ◽  
R Norstrom ◽  
R Wagemann ◽  
...  
Keyword(s):  
Marine Ecosystem ◽  
Temporal Trends ◽  
Canadian Arctic ◽  
Spatial And Temporal Trends

scholarly journals Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties

2021 ◽  
Vol 18 (16) ◽  
pp. 8259
Author(s):  
Sarah L. Jackson ◽  
Sahar Derakhshan ◽  
Leah Blackwood ◽  
Logan Lee ◽  
Qian Huang ◽  
...  
Keyword(s):  
United States ◽  
Great Plains ◽  
Explanatory Power ◽  
Temporal Trends ◽  
The United States ◽  
First Year ◽  
Significant Case ◽  
Urban Rural ◽  
Spatial And Temporal Trends ◽  
Mitigation Policies

This paper examines the spatial and temporal trends in county-level COVID-19 cases and fatalities in the United States during the first year of the pandemic (January 2020–January 2021). Statistical and geospatial analyses highlight greater impacts in the Great Plains, Southwestern and Southern regions based on cases and fatalities per 100,000 population. Significant case and fatality spatial clusters were most prevalent between November 2020 and January 2021. Distinct urban–rural differences in COVID-19 experiences uncovered higher rural cases and fatalities per 100,000 population and fewer government mitigation actions enacted in rural counties. High levels of social vulnerability and the absence of mitigation policies were significantly associated with higher fatalities, while existing community resilience had more influential spatial explanatory power. Using differences in percentage unemployment changes between 2019 and 2020 as a proxy for pre-emergent recovery revealed urban counties were hit harder in the early months of the pandemic, corresponding with imposed government mitigation policies. This longitudinal, place-based study confirms some early urban–rural patterns initially observed in the pandemic, as well as the disparate COVID-19 experiences among socially vulnerable populations. The results are critical in identifying geographic disparities in COVID-19 exposures and outcomes and providing the evidentiary basis for targeting pandemic recovery.

scholarly journals Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2737
Author(s):  
Leandro Ordonez-Ante ◽  
Gregory Van Seghbroeck ◽  
Tim Wauters ◽  
Bruno Volckaert ◽  
Filip De Turck
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Temporal Trends ◽  
Multidimensional Data ◽  
Data Sets ◽  
Distributed Data ◽  
Key Factors ◽  
Distributed Data Processing ◽  
Spatial And Temporal Trends ◽  
Query Latency

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving—on ingestion time—synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach.

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