This site contains data and features in geographical representations that are not legal representations. The accuracy of the data may change depending on scale adjustments.
The California Department of Parks and Recreation Office of Grants and Local Services (OGALS) presents the information on this website as a service to the public. Every effort has been made to ensure the information is accurate. OGALS makes no warranty or guarantee concerning the completeness, accuracy, or reliability of the content. Assessing accuracy and reliability of this information is the sole responsibility of the user and data shall be used and relied upon only at the risk of the user.
Data Definitions and Sources
Census Block — TIGER files from the 2010 decennial Census. A census block is the smallest geographic unit used by the US Census Bureau for the tabulation of data. Multiple blocks make up one census blockgroup.
Census Blockgroup — TIGER files from the American Community Survey 2008-2012 5-year survey (2012 TIGER) are used for the map tools. A census block group averages 1,500 residents and is the next largest unit after a block, and smaller than a tract. A block group is the smallest geography for which the bureau publishes data on poverty, median household income, and other sample data attributes.
Census Tract —
TIGER files from 2012 are used for the park access, acres of parks per thousand residents tool. Census tracts average 4,000 residents and are a larger unit than a census blockgroup.
No population areas — blocks with no population, Census 2010 (P1)
Parks — open space land available for the general public, it includes a formal recreation destination or facility. See the 2015 SCORP, page 4, for more information about what is and is not counted as open space recreation land for purpose of these SCORP tools.
Source: CPAD October 2014 adjusted for California State Parks
Park access — the Park Access Tool calculates the population living in close proximity to a park using two approaches:
- A half mile zone surrounds all statewide parks. The population living within a half mile of a park is counted as being in close proximity of a park.
- Park acreage per 1,000 residents within a census tract.
For more information about the use of these standards, see the 2015 SCORP, page 15.
Population — block and blockgroup population from Census 2010 (P1) and American Community Survey 2008-12 5 year estimates (B01001e1)
Half Mile Park Access Methodology
With GIS tools, we created a half mile zone around all statewide parks. Using the most recent American Community Survey block group population estimates, and an assumption of equal population distribution within block groups, we then analyzed how many of California's 38 million residents live within, or outside of, the half mile zones surrounding all statewide parks.
24% of Californians, nearly 9 million people, live over a half mile from a park or open space.
Overview of Methods and Data
Methods used to determine the population with or without close-to-home park access in California:
Classify all areas of the state as park, park access, or no park access:
- Define "parks", as the CFF version of CPAD [full definition below]
- Buffer all parks by 1/2 mile to define "park access areas"
- Define all non-park, and those areas outside the half mile buffer, as "no park access"
- Erase areas: water, Department of Defense lands, blockgroups with no population
- Download the most recent blockgroup population figures (ACS 2012 5-yr estimates)
- Erase the portion of block groups that are water/bay
- Assuming equal distribution of populations in blockgroups, determine the number of people living in the three classifications:
- Intersect the access types with the remaining blockgroups
- Calculate the percent of each block group in each of the access types
- Using the results of b., apply the same percent of the population to that access type.
- Blockgroup acres: 500
- Park acres in block group: 25
- Park access acres in blockgroup: 100
- No park access acres in blockgroup: 375
- Blockgroup population: 100
- In park access areas: 25
- In no park access areas: 75
Acres of Park per Thousand Residents
For this statewide analysis, California State Parks chose census tracts as the area against which to assess the amount of existing parkland in comparison to the 3 acres/1,000 population metric. Census tracts vary in population size, but average approximately 4,000 persons. Note that in some situations, additional parkland may lie at the edge of (or very close to) a particular tract, a condition not measured by this analysis.
Results: Over 60% of Californians live in census tracts with less than 3 acres/1,000 residents.
Overview of Methods and Data
Methods used to determine the park acres per thousand residents, by census tract:
- Intersect the census tracts with parks and open space
- Recalculate the park acres for all park pieces cut up to tracts
- Summary the total acres of park and open space in each tract
- Calculate park acres per thousand residents for all tracts with population (tracts with no population were not calculated)
Community Fact Finder (CFF) runs demographic summaries for a half mile radius of any point drawn in California. The demographic data is from the Census Bureau's American Community 2008-2012 5-year survey.
To operate, the program draws a half mile circle around the selected point. It then finds all of the block groups that intersect with that radius. The intersected block groups are split so that only
the portion of the block group polygons that are inside of the polygon are retained. While splitting the block groups, the program assumes and equal distribution of population. Therefore, if only 1/3
of the block group is inside of the radius, only 1/3 of the population is counted. The resulting full and partial block groups are then summarized and reported.
While this method produces accurate results in urban areas, rural areas with small pocket populations can be improved. To improve rural area calculations, one needs to distribute the block group data proportionally
down to the census block level. This is done to avoid large geographic block groups with a small populated portion from being over distributed. For example, a block group covers 35 square miles with a population of 1,000.
Inside that block group there is one contiguous 5 square mile cluster with 900 people, the other 100 people are spread out over the remaining 30 square miles. By breaking the block group geometry down to blocks one can more
accurately place the population, and thus the park acreage per thousand residents and other attributes.
Each time a circle is identified the CFF tool reviews the block groups overlapped by the circle. If any of the block groups are classified as rural, the tool uses the block level allocated estimates for the attribute reporting.
Circles do not mix block group and block input methods – this avoids possible edge matching issues.
Overview of Methods and Data
BG_ID = Block group ID (standard Census format)
SA_ac = Total area in acres
BG_ac = Acres of the full block group
BG_SA_ac = Acres of block group within the area
TotPop = Total population
PCI = Per capita income
Population is calculated as:
Population of Service Area = ∑ of [TotPop] *[BG_SA_ac]/[BG_ac]
Per Capita Income is calculated as:
Per Capita Income, Weighted Average = ([BG_SA_ac] * [PCI])/ [SA_ac]
||Population of Service Area
||BG_SA_ac * PCI
||PCI Weighted Avg
The area has a total population of 125, with a per capita income of $68,750.
- Any attributes based on a count (people in poverty, race/ethnicity, etc.) are calculated with the same formula used for total population.
- Any attributes based on a value (median household income, etc.) are calculated with the same formula used for per capita income.
- The ACS reports a margin of error (MOE) for each statistic. Users should review and become familiar with the MOE. The Census Bureau provides a variety of resources online.
- The MOE does not reflect the error inherit in assuming equal distribution of an attribute across a block group, or the error inherit in using a weighted average.
GIS processing and analysis was conducting using ESRI desktop software and extensions.