This feature layer shows the locations of the California State Operations Center and the three regional Emergency Operation Centers.California Office of Emergency ServicesWith over 38 million residents (12% of the population), the State of California is the most populous state in the nation and has the third largest land area among the states (163,695 square miles). California is culturally, ethnically, economically, ecologically, and politically diverse, and maintains the eighth largest economy in the world with 13 percent of the U.S. gross domestic product. California also faces numerous risks and threats to our people, property, economy, environment and is prone to earthquakes, floods, significant wildfires, prolonged drought impacts, public health emergencies, cybersecurity attacks, agricultural and animal disasters, as well threats to homeland security. Cal OES takes a proactive approach to addressing these risks, threats, and vulnerabilities that form the basis of our mission and has been tested through real events, as well as comprehensive exercises that help us maintain our state of readiness and plan for and mitigate impacts.Regional Operations​The California Office of Emergency Services (Cal OES) Agency has three administrative regions, Inland, Coastal and Southern which are located in Sacramento, Fairfield and Los Alamitos, respectively. Cal OES regions have the responsibility to carry out the coordination of information and resources within the region and between the SEMS state and regional levels to ensure effective and efficient support to local response. The regions serve as the conduit for local and regional perspective and provide a physical presence for Cal OES functions at the local level in all phases of emergency management.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

A dataset listing California counties by population for 2024.

Analysis of ‘Boston House Prices-Advanced Regression Techniques’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fedesoriano/the-boston-houseprice-data on 13 February 2022.

--- Dataset description provided by original source is as follows ---

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Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town 2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft. 3) INDUS: proportion of non-retail business acres per town 4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M] 6) RM: average number of rooms per dwelling 7) AGE: proportion of owner-occupied units built prior to 1940 8) DIS: weighted distances to five Boston employment centres 9) RAD: index of accessibility to radial highways 10) TAX: full-value property-tax rate per $10,000 [$/10k] 11) PTRATIO: pupil-teacher ratio by town 12) B: The result of the equation B=1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13) LSTAT: % lower status of the population

Output variable: 1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. LINK

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley LINK

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