scholarly journals Summer Heat Risk Index: how to integrate recent climatic changes and soil consumption component

2017 ◽  
Author(s):  
Alfonso Crisci ◽  
Luca Congedo ◽  
Marco Morabito ◽  
Michele Munafò
Keyword(s):  
Risk Index ◽  
Population Data ◽  
Analysis Data ◽  
Italian Population ◽  
Risk Class ◽  
Environmental Threats ◽  
Air Temperatures ◽  
Administrative Unit ◽  
Italian National Institute ◽  
Soil Consumption

Face to the urban resiliency two major environmental threats are widely recognized: the increasing summer air temperatures and the soil consumption that affects a large number of city in Italy. The work have the goal to present preliminary the actual Heat Summer Risk defined by using Crichton's Risk Triangle (Crichton, 1999) on the second Italian level of administration (ADM2 - Province). For each administrative unit we have considered as hazard layer the most recent trend of summer air temperature assessed (1980-2014); the exposure layer is individuated by the amount of population living in each province and finally as vulnerable layer the mean degree of soil consumption expressed in percentage was considered. Thanks to these information Crichton's methodology are able to give a quantitative risk value index further classified in five risk class. Data sources was provided by several authoritative institutions : (i) ISPRA ( Italian National Institute for Environmental Protection and Research) that provide data about density of soil consumption for 2015 as reported in the Soil Consumption Report 2016; (ii) ECAD (European Climate Assessment \& Dataset) that gives detailed historical daily climatic layers (E-OBS 1950-2015 v 13.0); (iii) ISTAT ( Italian National Institute of Statistics) that provides the last updates on Italian population data (2016). The results was mapped and presented. All computations was carried out in R-STAT environment by using different library available for Spatial and Trend Analysis. Data and code are released in public repository.

scholarly journals Summer Heat Risk Index: how to integrate recent climatic changes and soil consumption component

2016 ◽  
Author(s):  
Alfonso Crisci ◽  
Luca Congedo ◽  
Marco Morabito ◽  
Michele Munafò
Keyword(s):  
Risk Index ◽  
Population Data ◽  
Analysis Data ◽  
Italian Population ◽  
Risk Class ◽  
Environmental Threats ◽  
Air Temperatures ◽  
Administrative Unit ◽  
Italian National Institute ◽  
Soil Consumption

Face to the urban resiliency two major environmental threats are widely recognized: the increasing summer air temperatures and the soil consumption that affects a large number of city in Italy. The work have the goal to present preliminary the actual Heat Summer Risk defined by using Crichton's Risk Triangle (Crichton, 1999) on the second Italian level of administration (ADM2 - Province). For each administrative unit we have considered as hazard layer the most recent trend of summer air temperature assessed (1980-2014); the exposure layer is individuated by the amount of population living in each province and finally as vulnerable layer the mean degree of soil consumption expressed in percentage was considered. Thanks to these information Crichton's methodology are able to give a quantitative risk value index further classified in five risk class. Data sources was provided by several authoritative institutions : (i) ISPRA ( Italian National Institute for Environmental Protection and Research) that provide data about density of soil consumption for 2015 as reported in the Soil Consumption Report 2016; (ii) ECAD (European Climate Assessment \& Dataset) that gives detailed historical daily climatic layers (E-OBS 1950-2015 v 13.0); (iii) ISTAT ( Italian National Institute of Statistics) that provides the last updates on Italian population data (2016). The results was mapped and presented. All computations was carried out in R-STAT environment by using different library available for Spatial and Trend Analysis. Data and code are released in public repository.

scholarly journals Summer Heat Risk Index: how to integrate recent climatic changes and soil consumption component

2017 ◽  
Author(s):  
Alfonso Crisci ◽  
Luca Congedo ◽  
Marco Morabito ◽  
Michele Munafò
Keyword(s):  
Risk Index ◽  
Population Data ◽  
Analysis Data ◽  
Italian Population ◽  
Risk Class ◽  
Environmental Threats ◽  
Air Temperatures ◽  
Administrative Unit ◽  
Italian National Institute ◽  
Soil Consumption

Face to the urban resiliency two major environmental threats are widely recognized: the increasing summer air temperatures and the soil consumption that affects a large number of city in Italy. The work have the goal to present preliminary the actual Heat Summer Risk defined by using Crichton's Risk Triangle (Crichton, 1999) on the second Italian level of administration (ADM2 - Province). For each administrative unit we have considered as hazard layer the most recent trend of summer air temperature assessed (1980-2014); the exposure layer is individuated by the amount of population living in each province and finally as vulnerable layer the mean degree of soil consumption expressed in percentage was considered. Thanks to these information Crichton's methodology are able to give a quantitative risk value index further classified in five risk class. Data sources was provided by several authoritative institutions : (i) ISPRA ( Italian National Institute for Environmental Protection and Research) that provide data about density of soil consumption for 2015 as reported in the Soil Consumption Report 2016; (ii) ECAD (European Climate Assessment \& Dataset) that gives detailed historical daily climatic layers (E-OBS 1950-2015 v 13.0); (iii) ISTAT ( Italian National Institute of Statistics) that provides the last updates on Italian population data (2016). The results was mapped and presented. All computations was carried out in R-STAT environment by using different library available for Spatial and Trend Analysis. Data and code are released in public repository.

Italian population data for D1S1656, D3S1358, D8S1132, D10S2325, VWA, FES/FPS, and F13A01

2001 ◽  
Vol 123 (1) ◽  
pp. 71-73 ◽  
Author(s):  
Domenico De Leo ◽  
Stefania Turrina ◽  
Mario Marigo ◽  
Natascia Tiso ◽  
Gian Antonio Danieli
Keyword(s):  
Population Data ◽  
Italian Population

scholarly journals Flood Risk Index Assessment: Case Study in Lenggor River Basin, Johor, Malaysia

2018 ◽  
Vol 7 (4.34) ◽  
pp. 473
Author(s):  
Nurul Afiqa Adila Zakaria ◽  
Ahmad Shakir Mohd Saudi ◽  
Mohd Khairul Amri Kamarudin ◽  
Muhammad Hafiz Md Saad
Keyword(s):  
Water Level ◽  
River Basin ◽  
State Government ◽  
Flood Risk ◽  
Flood Control ◽  
Risk Index ◽  
Secondary Data ◽  
Control Limit ◽  
Statistical Process ◽  
Risk Class

The objective of this research is to determine the correlation of selected hydrological variables, to analyzed the significance factors influenced the occurrences of flood, to propose the flood control limit system and establish new flood risk index model in Lenggor River Basin based on secondary data derived from Department of Drainage and Irrigation (DID). Application of Chemometric technique such as Spearman’s Correlation Test, Principle Component Analysis, Statistical Process Control and Flood Risk Index created the most efficient results. Result shows water level has strong factor loading of 0.78 and significant for flood warning alert system application. The Upper Control Limit (UCL) for the water level in study area is 33.23m while the risk index for the water level set by the constructed formula of flood risk index consisting 0-100. The results show 20.6% classified as High Risk Class with index range from 70 and above. Thus, these findings are able to facilitate state government to come out with a comprehensive plan of action in strengthening the flood risk management at Lenggor River basin, Johor.  

scholarly journals Flood Risk Index Assessment: Case Study in Lenggor River Basin, Johor, Malaysia

2018 ◽  
Vol 7 (4.34) ◽  
pp. 473
Author(s):  
Nurul Afiqa Adila Zakaria ◽  
Ahmad Shakir Mohd Saudi ◽  
Mohd Khairul Amri Kamarudin ◽  
Muhammad Hafiz Md Saad
Keyword(s):  
Water Level ◽  
River Basin ◽  
State Government ◽  
Flood Risk ◽  
Flood Control ◽  
Risk Index ◽  
Secondary Data ◽  
Control Limit ◽  
Statistical Process ◽  
Risk Class

The objective of this research is to determine the correlation of selected hydrological variables, to analyzed the significance factors influenced the occurrences of flood, to propose the flood control limit system and establish new flood risk index model in Lenggor River Basin based on secondary data derived from Department of Drainage and Irrigation (DID). Application of Chemometric technique such as Spearman’s Correlation Test, Principle Component Analysis, Statistical Process Control and Flood Risk Index created the most efficient results. Result shows water level has strong factor loading of 0.78 and significant for flood warning alert system application. The Upper Control Limit (UCL) for the water level in study area is 33.23m while the risk index for the water level set by the constructed formula of flood risk index consisting 0-100. The results show 20.6% classified as High Risk Class with index range from 70 and above. Thus, these findings are able to facilitate state government to come out with a comprehensive plan of action in strengthening the flood risk management at Lenggor River basin, Johor.  

scholarly journals Estimated Number of Short-Stay Service Recipients in Hokkaido Prefecture, Japan, from 2020 to 2045: Estimation by Machine Learning and Review of Changing Trend by Cartogram

2020 ◽  
Author(s):  
Junko Ouchi ◽  
Kanetoshi Hattori
Keyword(s):  
Machine Learning ◽  
Population Data ◽  
Learning Approaches ◽  
Short Stay ◽  
Term Care ◽  
Administrative Unit ◽  
Future Care ◽  
Level 3 ◽  
Changing Trend ◽  
Administrative Units

Abstract Background: The present study aimed to estimate the numbers of short-stay service recipients in all administrative units in Hokkaido from 2020 to 2045 with the machine learning approaches and reviewed the changing trends of spatial distributions of the service recipients with cartograms.Methods: A machine learning approach was used for the estimation. To develop the model to estimate, population data in Japan from 2015 to 2017 were used as input signals, whereas data on the numbers of short-stay service recipients at each level of needs for long-term care (levels 1–5) from 2015 to 2017 were used as a supervisory signal. Three models were developed to avoid problems of repeatability. Then, data of the projected population in Hokkaido every 5 years from 2020 to 2045 were fed into each model to estimate the numbers of the service recipients for the 188 administrative units of Hokkaido. The medians of the estimations from the models were considered as the final results; the estimates for 188 administrative units were presented with continuous area cartograms on the map of Hokkaido.Results: The developed models predicted that the number of the service recipients in Hokkaido would peak at 18,016 in 2035 and the number of people at level 3 in particular would increase. The cartograms for levels 2 and 3 from 2020 to 2030 and level 3 for 2035 were heavily distorted in the several populated areas in Hokkaido, indicating that the majority of the service recipients would be concentrated in those populated areas. Conclusions: Machine learning approaches could provide estimates of future care demands for each administrative unit in a prefecture in Japan based on past population and care demand data. Results from the present study can be useful when effective allocations of human resources for nursing care in the region are discussed.

Italian Population Data on the Polymarker System and on the Five Short Tandem Repeat Loci CSF1PO, TPOX, TH01, F13B, and vWA

1998 ◽  
Vol 43 (4) ◽  
pp. 14315J ◽  
Author(s):  
Luciano Garofano ◽  
Gianpietro Lago ◽  
Cesare Vecchio ◽  
Marco Pizzamiglio ◽  
Carlo Zanon ◽  
...  
Keyword(s):  
Tandem Repeat ◽  
Short Tandem Repeat ◽  
Population Data ◽  
Italian Population ◽  
Short Tandem Repeat Loci ◽  
Short Tandem

scholarly journals Using Information on Settlement Patterns to Improve the Spatial Distribution of Population in Coastal Impact Assessments

2018 ◽  
Vol 10 (9) ◽  
pp. 3170 ◽  
Author(s):  
Jan-Ludolf Merkens ◽  
Athanasios Vafeidis
Keyword(s):  
Population Data ◽  
Population Densities ◽  
Exposed Population ◽  
Vulnerability Assessments ◽  
Baltic Sea Region ◽  
Administrative Unit ◽  
Urban Extent ◽  
Administrative Units ◽  
Uniform Population

Broad-scale impact and vulnerability assessments are essential for informing decisions on long-term adaptation planning at the national, regional, or global level. These assessments rely on population data for quantifying exposure to different types of hazards. Existing population datasets covering the entire globe at resolutions of 2.5 degrees to 30 arc-seconds are based on information available at administrative-unit level and implicitly assume uniform population densities within these units. This assumption can lead to errors in impact assessments and particularly in coastal areas that are densely populated. This study proposes and compares simple approaches to regionalize population within administrative units in the German Baltic Sea region using solely information on urban extent from the Global Urban Footprint (GUF). Our results show that approaches using GUF can reduce the error in predicting population totals of municipalities by factor 2 to 3. When assessing exposed population, we find that the assumption of uniform population densities leads to an overestimation of 120% to 140%. Using GUF to regionalise population within administrative units reduce these errors by up to 50%. Our results suggest that the proposed simple modeling approaches can result in significantly improved distribution of population within administrative units and substantially improve the results of exposure analyses.

Italian population data on two new short tandem repeat loci: D2S1338 and Penta E

1999 ◽  
Vol 105 (2) ◽  
pp. 131-136 ◽  
Author(s):  
Luciano Garofano ◽  
Marco Pizzamiglio ◽  
Francesco Donato ◽  
Fulvio Biondi ◽  
Matteo Rossetti ◽  
...  
Keyword(s):  
Tandem Repeat ◽  
Short Tandem Repeat ◽  
Population Data ◽  
Italian Population ◽  
Short Tandem Repeat Loci ◽  
Short Tandem
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