Best Practices
We strongly encourage staff to read this Handbook in its entirety. However, for those who want to jump to best practices, or who just need a refresher, we have provided them below, with links to pages that have more details, guidance, and resources.
Best Practices Table
| Practice | Description | Source | Notes |
|---|---|---|---|
| It is helpful to survey a range of existing tools when considering the mapping of “disadvantaged” populations. | Each presents strengths and weaknesses to consider while keeping in mind that ultimately decisions will be driven by:
|
Web-based Tools for Identifying Underserved Communities, CNRA | This guidance discusses things to consider when evaluating tools and is applicable to work at the water boards too. |
| Users conducting an analysis with the CalEnviroScreen (CES) 4.0 dataset should be aware that it contains missing values, both for individual indicators and overall CES scores. | These missing values are distinct from zeros, which are also in the CES dataset. For more information about the missing (and zero) values, see the data dictionary that accompanies the CalEnviroScreen 4.0 results Excel workbook, available for download as a zip file here. | Racial Equity Data Hub, Tools REDAP Resource Hub Draft (ca.gov), 6/2/2023. | Things to consider when using CalEnviroScreen Scores. |
| Utilizing quantitative and qualitative analyses to identify inequities and response system effectiveness. | Various national level quantitative and quantitative tools exist to develop a continuum of care focusing on racial inequities and homelessness. Quantitative tools examine population data on race and ethnicity and program outcomes data. Qualitative tools include interviewing people with lived experience. | Racial Equity Data Guidebook from the United States Interagency Council on Homelessness: https://www.usich.gov/resources/uploads/asset_library/Racial_Equity_Data_Guidebook.pdf | Innovative tools when looking at homelessness and race. The tools could be used for other programs. |
| Using F.A.I.R. data principles | Four foundational principles—Findability, Accessibility, Interoperability, and Reusability—that serve to guide data producers and publishers in data management and stewarship. | Original principles from 2016 are available here: https://www.nature.com/articles/sdata201618%C2%A0some good documentation on how to operationalize FAIR principles for equity data is available here: https://www.nature.com/articles/s41597-022-01606-w | Data management principles. |
| Using CARE principles for Indigenous Data governance. | The ‘CARE Principles for Indigenous Data Governance’ address concerns related to the people and purpose of data; Collective benefit, Authority to control, Responsibility, and Ethics, and their respective sub-principles. The CARE Principles detail that the use of Indigenous data should result in tangible benefits for Indigenous collectives through inclusive development and innovation, improved governance and citizen engagement, and result in equitable outcomes. | A good source of information on CARE including the original publication is available here: https://www.gida-global.org/care CARE and FAIR principles can go hand and hand and there is good information on that available here: https://www.nature.com/articles/s41597-021-00892-0 | Data governance with a specific lens on tribal data and is applicable to broader groups of underrepresented peoples. |
Publish Water Boards Data
If you are a steward of Water Boards data - and it is not yet made appropriately open and accessible (including thorough documentation and complete metadata!) - you should complete the guidance on the Publish Your Data page before you begin thinking about planning for your project.
- Building a map (or other data visualization/interpretation tool) before making our own data open and accessible is not the right order. Share the we have first, then build a map or other resource showing what we think is interesting/important using that (and other) data.
Plan & Prepare
This phase involves conducting an equity assessment (Planning) and developing your data management plan using an equity lens (Data Preparation). Best practices for this phase include:
Not rushing through this phase! We often want to dive straight into “doing something” and taking the time to plan can feel like a waste of time. If done quickly and without using an equity lens, it will be time wasted. Instead, invest the time needed to do this phase well, rather than fast.
Using this phase to begin the process of trust and relationship building with Tribes, communities, and other expert partners interested in your project. Maybe it involves some early outreach, putting together an email list, establishing a technical advisory committee, or otherwise co-creating your planning documents with the communities that will be most impacted by the project’s implementation.
Make the products developed during this phase as open, transparent, and accessible as possible and appropriate. This doesn’t necessarily mean that every single thing should be made public - but it does mean seriously considering what can be made open, to whom, and when.
Use the best available science while relying on current, generally accepted Agency procedures for conducting risk assessments, economic or other technical analysis. But also understand that just because something has “always been done this way” does not automatically make it the best science, method, or process available. Be open to integrating other ways of knowing that might be different from “western science” or our business as usual methods or processes, such as the traditional knowledge landscape, and non-tribal expertise that stems from lived experiences.
Collect & Process
This phase involves collecting the data and information you need for your project (Data Collection) and making it tidy so that is ready to be used in your analyses or product development steps (Data Processing). Best practices for this phase include:
Use already existing and available frameworks and data, supplementing as appropriate.
Carefully select and justify the choice of the geographic unit you will use in your analysis and discuss any particular challenges or potential aggregation issues related to the choice of spatial scale.
Keep data disaggregated so you are able to reveal important spatial differences during your analysis phase (e.g., demographic information for each facility/place) when feasible and appropriate.
Prepare to invest the time required to tidy the data needed for your project. It’s commonly understood in the data science field that 80% of data analysis is spent on the process of cleaning and preparing the data (Dasu and Johnson 2003) - expect the same will be true for your project and prepare to invest time and resources accordingly.
Assure & Analyze
Remember - this phase is all about…. Best practices for this phase include:
Use the highest quality and most recent data available.
Carefully select and justify the choice of a comparison population group.
Analyze and compare effects in baseline and across policy scenarios to show differences in effects.
When data allow, characterize the distribution of risks, exposures, or outcomes within each population group, instead of presenting only average effects.
Present summary metrics for relevant population groups of concern as well as the comparison population group.
Be consistent with the basic assumptions underlying other parts of the analysis, such as using the same baseline and option scenarios.
Preserve & Store
Remember - this phase is all about…. Best practices for this phase include:
Discover & Integrate
Remember - this phase is all about…. Best practices for this phase include:
Describe
Remember - this phase is all about…. Best practices for this phase include:
Discuss the overall quality and main limitations of the data (e.g., completeness, accuracy, validation).
Discuss available evidence of factors that may make population groups of concern more vulnerable to adverse effects (e.g., unique pathways; cumulative exposure from multiple stressors; and behavioral, biological, or environmental factors that increase susceptibility).
Identify unique considerations for subsistence populations when relevant.
Discuss the severity and nature of the health consequences for which differences between population groups have been analyzed.
Clearly describe data sources, assumptions, analytic techniques, and results.
Discuss key sources of uncertainty or potential biases in the data (e.g., sample size, using proximity as a surrogate for exposure) and how they may influence results.
When possible, conduct sensitivity analysis for key assumptions or parameters that may affect findings.
Make elements of Environmental Justice (EJ) assessments as straightforward and easy for the public to understand as possible.