scholarly journals FURTHER EVIDENCE ON THE SPATIO-TEMPORAL MODEL OF HOUSE PRICES IN THE UNITED STATES

2013 ◽  
Vol 29 (3) ◽  
pp. 515-522 ◽  
Author(s):  
Badi H. Baltagi ◽  
Jing Li
Keyword(s):  
United States ◽  
House Prices ◽  
The United States ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals House prices and credit risk: Evidence from the United States

2015 ◽  
Vol 51 ◽  
pp. 123-135 ◽  
Author(s):  
Mohammad Tajik ◽  
Saeideh Aliakbari ◽  
Thaana Ghalia ◽  
Sepideh Kaffash
Keyword(s):  
United States ◽  
Credit Risk ◽  
House Prices ◽  
The United States

scholarly journals Fine-grained, spatio-temporal datasets measuring 200 years of land development in the United States

2020 ◽  
Author(s):  
Johannes H. Uhl ◽  
Stefan Leyk ◽  
Caitlin M. McShane ◽  
Anna E. Braswell ◽  
Dylan S. Connor ◽  
...  
Keyword(s):  
United States ◽  
Temporal Resolution ◽  
Data Extraction ◽  
Historical Analysis ◽  
Land Development ◽  
Remote Sensing Data ◽  
The United States ◽  
Building Stock ◽  
Fine Grained ◽  
Spatio Temporal

Abstract. The collection, processing and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth’s surface. While satellite-based earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially-explicit settlement data for the United States that extend back to the early nineteenth century, and is consistently enumerated at fine spatial and temporal granularity (i.e., 250 m spatial, and 5 a temporal resolution). We create these time series using a large, novel building stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at unprecedented spatial and temporal resolution. The datasets are available at https://dataverse.harvard.edu/dataverse/hisdacus (Uhl and Leyk, 2020a, b, c, d).

scholarly journals Spatio-Temporality and Tribal Water Quality Governance in the United States

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 99 ◽  
Author(s):  
Teresa Cavazos Cohn ◽  
Kate Berry ◽  
Kyle Powys Whyte ◽  
Emma Norman
Keyword(s):  
United States ◽  
Water Quality ◽  
Water Governance ◽  
Dynamic Network ◽  
The United States ◽  
Space Time ◽  
Cultural Bias ◽  
Ontological Pluralism ◽  
Political Relationships ◽  
Spatio Temporal

Hydrosocial spatio-temporalities—aspects of water belonging to space, time, or space-time—are central to water governance, providing a framework upon which overall hydrosocial relations are constructed, and are fundamental to the establishment of values and central to socio-cultural-political relationships. Moreover, spatio-temporal conceptions may differ among diverse governing entities and across scales, creating “variability” through ontological pluralism, as well as power asymmetries embedded in cultural bias. This paper explores spatio-temporal conceptions related to water quality governance, an aspect of water governance often biased toward technical and scientific space-time conceptions. We offer examples of different aspects of spatio-temporality in water quality issues among Tribes in the United States, highlighting several themes, including spatiotemporal cycles, technological mediation, and interrelationship and fluidity. Finally, we suggest that because water is part of a dynamic network of space-times, water quality may be best governed through more holistic practices that recognize tribal sovereignty and hydrosocial variability.

scholarly journals Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave in Europe and the United States

2021 ◽  
Author(s):  
Jessica T Davis ◽  
Matteo Chinazzi ◽  
Nicola Perra ◽  
Kunpeng Mu ◽  
Ana Pastore y Piontti ◽  
...  
Keyword(s):  
United States ◽  
Early Stage ◽  
The United States ◽  
International Travel ◽  
Surveillance Systems ◽  
Model Driven ◽  
Global Dynamic ◽  
The Us ◽  
Community Transmission ◽  
Spatio Temporal

Given the narrowness of the initial testing criteria, the SARS-CoV-2 virus spread through cryptic transmission in January and February, setting the stage for the epidemic wave experienced in March and April, 2020. We use a global metapopulation epidemic model to provide a mechanistic understanding of the global dynamic underlying the establishment of the COVID-19 pandemic in Europe and the United States (US). The model is calibrated on international case introductions at the early stage of the pandemic. We find that widespread community transmission of SARS-CoV-2 was likely in several areas of Europe and the US by January 2020, and estimate that by early March, only 1-3 in 100 SARS-CoV-2 infections were detected by surveillance systems. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 with possible importation and transmission events as early as December, 2019. We characterize the resulting heterogeneous spatio-temporal spread of SARS-CoV-2 and the burden of the first COVID-19 wave (February-July 2020). We estimate infection attack rates ranging from 0.78%-15.2% in the US and 0.19%-13.2% in Europe. The spatial modeling of SARS-CoV-2 introductions and spreading provides insights into the design of innovative, model-driven surveillance systems and preparedness plans that have a broader initial capacity and indication for testing.

scholarly journals On stay at home orders: Using the power of data science for spatial and temporal modeling and visualization of COVID-19

2021 ◽  
Author(s):  
Christopher R Prentice ◽  
Rachel Carroll
Keyword(s):  
United States ◽  
Data Science ◽  
The United States ◽  
Data Generation ◽  
Community Members ◽  
Temporal Modeling ◽  
Related Research ◽  
Mapping Model ◽  
Spatio Temporal ◽  
At Home

Abstract Coronavirus disease 2019 dominated and augmented many aspects of life beginning in early 2020. Related research and data generation developed alongside its spread. We developed a Bayesian spatio-temporal Poisson disease mapping model for estimating real-time characteristics of the coronavirus disease in the United States. We also created several dashboards for visualization of the statistical model for fellow researchers and simpler spatial and temporal representations of the disease for consumption by community members in our region. Findings suggest that the risk of confirmed cases is higher for health regions under partial stay at home orders and lower in health regions under full stay at home orders, when compared to before stay at home orders were declared. These results confirm the benefit of state-issued stay at home orders as well as suggest compliance to the directives towards the older population for adhering to social distancing guidelines.

scholarly journals Forecasting House Prices in the United States with Multiple Structural Breaks

2014 ◽  
Vol 6 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Mahua Barari ◽  
Nityananda Sarkar ◽  
Srikanta Kundu ◽  
Kushal Banik Chowdhury
Keyword(s):  
United States ◽  
Structural Breaks ◽  
House Prices ◽  
The United States ◽  
Multiple Structural Breaks
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