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Disaster Risk
More Details Crime Risk
More Details Disaster Risks
Disaster Risk is very low for Porcupine. The most significant environmental threat for this locationundefined
Hide detailsFlood Risk
0% Very low risk
There’s a Very Low Flood Risk score for Porcupine
Methodology
The Augurisk Coastal Flooding Hazard index is based on three key components: the mean elevation of your block, the expected sea level rise in your area, and your exposure to hurricanes.Note that storm surge is currently not explicitly described by the Augurisk coastal flooding hazard index. Depending on your location, storm surge can increase the water level by considerable amounts.During Hurricane Katrina, the sea level increased by up to 28 feet locally (NOAA). If you live along the coastline, it is strongly advised to follow official announcements in your area.
References
Elevation data - USGS 3DEP Elevation . Sweet, W.V., R.E. Kopp, C. P. Weaver, J. Obeysekera, R. M. Horton, E.R. Thieler and C. Zervas (2017), Global and Regional Sea Level Rise Scenarios for the United States. NOAA Tech. Rep. NOS CO-OPS 83. For more information on storm surges, visit nhc.noaa.gov
The Augurisk Coastal Flooding Hazard index is based on three key components: the mean elevation of your block, the expected sea level rise in your area, and your exposure to hurricanes.Note that storm surge is currently not explicitly described by the Augurisk coastal flooding hazard index. Depending on your location, storm surge can increase the water level by considerable amounts.During Hurricane Katrina, the sea level increased by up to 28 feet locally (NOAA). If you live along the coastline, it is strongly advised to follow official announcements in your area.
References
Elevation data - USGS 3DEP Elevation . Sweet, W.V., R.E. Kopp, C. P. Weaver, J. Obeysekera, R. M. Horton, E.R. Thieler and C. Zervas (2017), Global and Regional Sea Level Rise Scenarios for the United States. NOAA Tech. Rep. NOS CO-OPS 83. For more information on storm surges, visit nhc.noaa.gov
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Wildfire Risk
0% Very low risk
Porcupine faces a Very Low Wildfire Risk.
Methodology
The Augurisk Wildfire Hazard index is defined at the U.S. Census block level. It is based on the potential for wildfire burning in your block - based on data from Dillon et al. (2015, 2018) - and the impact of climate change on the potential for wildfire in the United States, particularly in the West (Abatzoglu and Williams, 2016). Therefore, the average temperature change in summer between the 1980-2005 and 2006-2100 has been extracted from two selected Regional Climate Models (RCMs) from the NA-CORDEX project (Mearns et al.,2017) and taken into account in defining the hazard score. The scenario considered is RCP4.5. All index values are normalized to a 0-100 scale..
References
Abatzoglou, J.T., and A.P. Williams. 2016. “Impact of anthropogenic climate change on wildfire across western US forests.” Proceedings of the National Academy of Sciences 113(42):11770. Mearns, L.O., et al., 2017: The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway, Boulder CO, accessed [December 2019], https://doi.org/10.5065/D6SJ1JCH Dillon, G.K.; J. Menakis; and F. Fay. 2015. Wildland Fire Potential: A Tool for Assessing Wildfire Risk and Fuels Management Needs. pp 60-76 In Keane, R. E.; Jolly, M.; Parsons, R.; and Riley, K. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 345 p. Note : data updated in 2018.
The Augurisk Wildfire Hazard index is defined at the U.S. Census block level. It is based on the potential for wildfire burning in your block - based on data from Dillon et al. (2015, 2018) - and the impact of climate change on the potential for wildfire in the United States, particularly in the West (Abatzoglu and Williams, 2016). Therefore, the average temperature change in summer between the 1980-2005 and 2006-2100 has been extracted from two selected Regional Climate Models (RCMs) from the NA-CORDEX project (Mearns et al.,2017) and taken into account in defining the hazard score. The scenario considered is RCP4.5. All index values are normalized to a 0-100 scale..
References
Abatzoglou, J.T., and A.P. Williams. 2016. “Impact of anthropogenic climate change on wildfire across western US forests.” Proceedings of the National Academy of Sciences 113(42):11770. Mearns, L.O., et al., 2017: The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway, Boulder CO, accessed [December 2019], https://doi.org/10.5065/D6SJ1JCH Dillon, G.K.; J. Menakis; and F. Fay. 2015. Wildland Fire Potential: A Tool for Assessing Wildfire Risk and Fuels Management Needs. pp 60-76 In Keane, R. E.; Jolly, M.; Parsons, R.; and Riley, K. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 345 p. Note : data updated in 2018.
Earthquake Risk
27% Low risk
Such an earthquake is light. It is felt indoors by many, outdoors by few during the day. At night, some awaken. Dishes, windows, doors are disturbed; walls make cracking sounds. A sensation like a heavy truck is striking the building can be felt. Standing motor cars rock noticeably.
Methodology
The Augurisk Earthquake Hazard index is defined at the U.S. Census block level. It is based on the intensity of the earthquake happening in your block with a 2% probability over the next 50 years, and computed using data from USGS (Rukstales and Petersen, 2019). Intensity is defined based on the Modified Mercalli Intensity scale (MMI). All scores are normalized to a 0-100 scale.
References
Rukstales, K.S., and Petersen, M.D., 2019, Data Release for 2018 Update of the U.S. National Seismic Hazard Model: U.S. Geological Survey data release, https://doi.org/10.5066/P9WT5OVB. More information on the Modified Mercalli Index and its interpretation can be found on usgs.gov.
The Augurisk Earthquake Hazard index is defined at the U.S. Census block level. It is based on the intensity of the earthquake happening in your block with a 2% probability over the next 50 years, and computed using data from USGS (Rukstales and Petersen, 2019). Intensity is defined based on the Modified Mercalli Intensity scale (MMI). All scores are normalized to a 0-100 scale.
References
Rukstales, K.S., and Petersen, M.D., 2019, Data Release for 2018 Update of the U.S. National Seismic Hazard Model: U.S. Geological Survey data release, https://doi.org/10.5066/P9WT5OVB. More information on the Modified Mercalli Index and its interpretation can be found on usgs.gov.
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Porcupine Disaster Risk Rankings
Crime Risk
Crime risk is severe for Porcupine. On average, our algorithm predicts 8 violent crimes 31 property crimes, 9 acts of vandalism and 3 motor vehicle thefts this year in your block group.
Hide detailsProperty Crime
70% Severe risk
31 predicted Property Crimes in a year
Vehicle Theft
70% Severe risk
9 predicted Vehicle Thefts in a year
Violent Crime
80% Severe risk
8 predicted Violent Crimes in a year
Acts of Vandalism
80% Severe risk
9 predicted Acts of Vandalism in a year
Methodology
The Augurisk Crime score is defined at the Census Block Group, by taking into account yearly violent and property crimes rates per 1000 inhabitants, predicted by our machine learning algorithm between 2015 and 2019. In order to predict crime occurrences where data is unavailable, our algorithm uses over 200 socio-economic, demographic, climatic, spatial, and law enforcement predictors selected based on various insights, from multidisciplinary literature review to correlation analysis and feature engineering. The predicting model is based on and has been tested against historical high-resolution crime data collected by the Open Crime Database for around 15 cities in the US with up to 79% accuracy.
References
Ashby, M.P.J. (2019): Studying Crime and Place with the Crime Open Database. Research Data Journal for the Humanities and Social Sciences 4(1), pp. 65-80. U.S. Census Bureau: American Community Survey, 2015-2019 5-Year Estimates. Fick. S.E., and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37(12): 4302-4315. Our algorithm has been published in an academic journal: Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model.
The Augurisk Crime score is defined at the Census Block Group, by taking into account yearly violent and property crimes rates per 1000 inhabitants, predicted by our machine learning algorithm between 2015 and 2019. In order to predict crime occurrences where data is unavailable, our algorithm uses over 200 socio-economic, demographic, climatic, spatial, and law enforcement predictors selected based on various insights, from multidisciplinary literature review to correlation analysis and feature engineering. The predicting model is based on and has been tested against historical high-resolution crime data collected by the Open Crime Database for around 15 cities in the US with up to 79% accuracy.
References
Ashby, M.P.J. (2019): Studying Crime and Place with the Crime Open Database. Research Data Journal for the Humanities and Social Sciences 4(1), pp. 65-80. U.S. Census Bureau: American Community Survey, 2015-2019 5-Year Estimates. Fick. S.E., and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37(12): 4302-4315. Our algorithm has been published in an academic journal: Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model.
Porcupine Crime Risk Rankings
Societal Risks
Societal risk is severe for Porcupine. The most significant societal threat for this propertyundefined
Hide detailsAir Pollution Hazard
51% High risk
Methodology
The Augurisk Air Pollution hazard score is defined at the U.S. block level. It is based on the daily median and maximal air quality values recorded at the nearest U.S. EPA AQI monitor over the last 3 years. All values are normalized to a 0-100 scale. To avoid disturbances in the distribution of risk scores, specific transformations to the higher percentiles of selected variables (i.e. winsorization) were applied to the maximal recorded AQI variable.
References
AQI data was collected from the U.S. EPA AirNow program. Map data is provided by Air Now Current Monitor Data Public on ESRI. More information on the U.S. AQI computation and interpretation can be found on the AirNow website.
The Augurisk Air Pollution hazard score is defined at the U.S. block level. It is based on the daily median and maximal air quality values recorded at the nearest U.S. EPA AQI monitor over the last 3 years. All values are normalized to a 0-100 scale. To avoid disturbances in the distribution of risk scores, specific transformations to the higher percentiles of selected variables (i.e. winsorization) were applied to the maximal recorded AQI variable.
References
AQI data was collected from the U.S. EPA AirNow program. Map data is provided by Air Now Current Monitor Data Public on ESRI. More information on the U.S. AQI computation and interpretation can be found on the AirNow website.
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Socioeconomic Risk
0% Very low risk
Methodology
The Augurisk socioeconomic risk index is based on multiple indicators, the two most important being the median household income and the unemployment rate. Additional factors include the mean educational attainment, poverty rate and income inequality in various proportions based on their respective correlations and sensitivity analysis. All values are normalized to a 0-100 scale.
The socioeconomic vulnerability index shown on the map is a distinct index, created by the U.S. Centers for Disease Control and Prevention and based on 15 social factors. More information on the CDC SVI can be found in Flanagan et al. (2018).
References
Flanagan, B.E., E.J. Hallisey, E. Adams, and Lavery A. 2018. “Measuring Community Vulnerability to Natural and Anthropogenic Hazards: The Centers for Disease Control and Prevention’s Social Vulnerability Index.” Journal of Environmental Health 80(10):34–37. United States Department of Agriculture Economic Research Service: county-level datasets. University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2019. countyhealthrankings.org.
The Augurisk socioeconomic risk index is based on multiple indicators, the two most important being the median household income and the unemployment rate. Additional factors include the mean educational attainment, poverty rate and income inequality in various proportions based on their respective correlations and sensitivity analysis. All values are normalized to a 0-100 scale.
The socioeconomic vulnerability index shown on the map is a distinct index, created by the U.S. Centers for Disease Control and Prevention and based on 15 social factors. More information on the CDC SVI can be found in Flanagan et al. (2018).
References
Flanagan, B.E., E.J. Hallisey, E. Adams, and Lavery A. 2018. “Measuring Community Vulnerability to Natural and Anthropogenic Hazards: The Centers for Disease Control and Prevention’s Social Vulnerability Index.” Journal of Environmental Health 80(10):34–37. United States Department of Agriculture Economic Research Service: county-level datasets. University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2019. countyhealthrankings.org.
Healthcare Infrastructure
52% High risk
Methodology
The Augurisk Healthcare Infrastructure risk index is based on multiple risk components, including the number of health practitioners in your county, the distance to the nearest primary acute care or critical access hospital in the HIFLD Hospitals Database, and the size of the nearest hospital as expressed by its number of beds. All values are normalized to a 0-100 scale.
References
Homeland Infrastructure Foundation-Level Data: Hospitals at this link. University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2019. www.countyhealthrankings.org.
The Augurisk Healthcare Infrastructure risk index is based on multiple risk components, including the number of health practitioners in your county, the distance to the nearest primary acute care or critical access hospital in the HIFLD Hospitals Database, and the size of the nearest hospital as expressed by its number of beds. All values are normalized to a 0-100 scale.
References
Homeland Infrastructure Foundation-Level Data: Hospitals at this link. University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2019. www.countyhealthrankings.org.
Porcupine Societal Risk Rankings
Although the information found on this system and the Site has been produced and processed from sources believed to be reliable, no warranty, express or implied, is made regarding accuracy, adequacy, completeness, legality, reliability or usefulness of any information or predictions provided.