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Public Health Alliance of Southern California. California Healthy Places Index. 2019. https://healthyplacesindex.org.

© 2018 Public Health Alliance of Southern California

Permission is hereby granted to use, reproduce, and distribute these materials for noncommercial purposes, including educational, government and community uses, with proper attribution to the Public Health Alliance of Southern California including this copyright notice. Use of this publication does not imply endorsement by the Public Health Alliance of Southern California.

© 2018 California Department of Public Health (CDPH)

Permission is hereby granted to use, reproduce, and distribute these materials for noncommercial purposes, including educational, government, and community uses, with proper attribution to the CDPH, including this copyright notice. Use of this publication does not imply endorsement by the CDPH.

The index does not include certain neighborhood characteristics critical to health because they did not meet the criteria for inclusion (described in question 3). For instance, this included physician ratios (the number of physicians per 100,000 population) because data was missing for a majority of census tracts. In fact, the steering committee was unable to locate much data on health care access or quality at the census-tract level (only data on health care insurance coverage was available).  

 The index was previously critiqued in ways that led to a shift from framing data in terms of “disadvantage” towards a framework of “opportunity”. This led to not only a renaming of the index (from “the Health Disadvantage Index to the Healthy Places Index) but also a shift in reporting of data (e.g. highlight the percentage of the population with a BA degree or higher rather than the percentage of population without a college degree). 

The HPI is also limited in terms of the effects of confounding, with some indicators with strong evidence of health effects showing contrary associations with life expectancy at birth by census tract. The steering committee has also acknowledged that the HPI might not be accurate for census tracts undergoing rapid population change (e.g. due to immigration, rapid gentrification, or other changes).

The HPI notably does not correlate strongly with CalEnviroScreen, which the steering committee for the HPI noted failed to identify one-third of census tracts with the worst conditions for population health. The HPI is ultimately more centered on considering environmental factors as a part of overall health, rather than as a central determinant. However, this disconnect between CalEnviroScreen and the HPI may also be a reflection of the challenges environmental injustice advocates have faced in linking environmental factors to health outcomes (which might not be as visible and geographically direct as the links between health and other indicators).

This text builds from earlier conceptions of the term “land dispossession” and “land grab”. As defined by the 2011 International Land Coalition, land grabbing specifically refers to large scale land acquisitions that are “ in violation of human rights, without prior consent of the preexisting land users, and with no consideration of social and environmental impacts”. Characterization of land grabs and their resulting harms most commonly considers the effect of physical displacement and harms within the articulated “grabbed” area (Nyantakyi-Frimpong, 2017;Ogwand, 2018;  huaserman, 2018). Li and Pan seek to expand the frame of analysis for land grabs beyond the site of grabbed land to consider the full extent of harms associated with land grabs both geographically (via pollution spillover to areas outside of “grabbed land”) and temporally (via latent “expulsion by pollution). 

 “While the villagers are not passive victims and have adopted various resistance strategies, the space for them to struggle and achieve success is confined and shaped by the existing power asymmetry in which local villagers, capital and local government are embedded.”  (Li and Pan, 2021, p 418). 

“...this framing of land dispossession is problematic in two aspects. Firstly, it obscures an invisible form of land dispossession in which people still maintain control of their land but its use value is damaged by pollution. This kind of indirect land dispossession could lead to expulsion, not due to the direct loss of control over land but by it being rendered useless by pollution.” Li and Pan, 2021, p 409). 

 This text employs a case study approach to characterize how villagers in a village in China have been displaced “in-place” as a result of new industrial activities within the area  (all specific details have been hidden within the publication, wherein the names of villager groups and the site of study itself is referenced only by coded letters). The scale of analysis primarily centers at the village level, though analysis of the case study itself extends towards the country level specifically when analysis of state actors are involved. 

Authors Hua Li and Lu Pan are scholars from China. Li is  affiliated with the College of Humanities and Law at Taiyuan University of Technology, wherein her research focuses specifically on water politics, environmental justice, and rural development and agrarian change. Pan is affiliated with the College of Humanities and Development at China Agricultural University. Her research interests include marginalized communities, rural development, and agrarian change.

The HPI draws data about 25 community characteristics into a single indexed HPI score. The includes sub-scores for 8 “Policy Action Areas”: Economic, Education, Housing, Health Care Access, Neighborhood, Clean Environment, Transportation, and Social Factors. These scores are meant to be used to evaluate health geographically. Each policy action area includes the following individual indicators and weights:

ECONOMIC (0.32)

• Poverty
• Employment
• Income

EDUCATION (0.19)

• Pre-school enrollment
• High school enrollment
• Bachelors attainment

HEALTHCARE (0.05)

• Insured adults

HOUSING (0.05)

• Severe cost burden low income
• Homeownership
• Kitchen and plumbing
• Crowding

NEIGHBORHOOD (0.08)

• Retail jobs
• Supermarket access
• Parks
• Tree canopy
• Alcohol establishments

CLEAN ENVIRONMENT (0.05)

• Diesel PM
• Ozone
• PM2.5
• Drinking water

SOCIAL (0.10)

• Two parent household
• Voting

TRANSPORTATION (0.16)

• Healthy community
• Automobile access

*The steering committee for the HPI sought to include race/ethnicity as a 9th policy action area, but they were prohibited from doing so by state law which does not allow California state agencies to use race as a basis for public contracting.

The primary HPI Index is designed to align with life expectancy at birth as a predictive measure of community health status. However, the Healthy Places mapping tool can also be used to create custom scores using different indicators. The mapping tool includes detailed definitions of each indicator.

Each indicator is linked to a policy guide, which outlines concrete actions (e.g. best practices, emerging policy options) that local jurisdictions can take to improve HPI indicators. These actions are sometimes aimed at addressing direct links between policy and an action area, and other times aimed at addressed the root causes of an action area. The mapping tool also enables filtering results by “Decision support layers” like health outcomes, health risk behaviors, race/ethnicity, climate change effects, and other layers that the alliance identifies as important for advancing “resilient, equitable communities in California”. Geographies (e.g. census tracts) can also be compared by indicator using a ranking tool. The pool function can be used to create customized aggregations of data to map (e.g. adding several census-tracts together).

Data is available at several different scales: census-tracts, congressional districts, state assembly districts, state senate districts, cities, core based statistical areas, elementary school districts, metropolitan planning organization and medical service study areas.  

Creating maps by different combinations of indicators or geographic aggregations could be tinkered with to produce provocative data visualizations. Ranking scores can be used to draw distinction between different census tracts. However, clear inequities are evident even without these adjustments, with the HPI index score clearly demonstrating noticeable differences across geographies.