Matching 5.0 Alpha (Limited release): Enhanced modeling of child-to-provider distances
Matching 5.0 Alpha uses a probabilistic approach to model child locations based on empirically observed distributions of child-to-provider distances in available child-level data from state and other program data sources.
- More comprehensive estimation of child-to-provider distances for all served children in the database.
- Probabilistic modeling of served children’s geolocations results in spatial patterns that reflect real-world distributions of child-to-provider distances.
- Higher replicability of spatial patterns of children’s addresses allows greater consistency across data releases. The matching algorithm can use the same probability distribution with identical characteristics to generate similar spatial patterns of child-to-provider distances over multiple data releases.
- The inherent flexibility of probabilistic spatial modeling allows adjustments to the parameters of the statistical probability distribution—for example, degree of clustering, dispersion, and spatial density—to refine spatial patterns of the geolocations of children served by different funding programs.
Matching 5.0 Alpha (Limited release): Enhanced private pay estimation
Integration with the Census Bureau’s 2021 SIPP data (which includes survey panels from 2018, 2020, and 2021) to determine the distribution, by income and employment segment, of the percentage share and number of children served by private pay child care.
- Enhanced accuracy of matching between children served by providers and funding sources, including private pay child care, based on child and household characteristics.
- Integration with empirical survey data from the Census Bureau makes estimates of children served by private pay (which represents the largest share of children served by child care, notably for children under 5 years) more verifiable and reliable than prior CUSP versions.
- Eliminates need for simplifying analytical assumptions about utilization of private pay child care in different income and employment segments, thus making CUSP outputs more robust and valid.
Eligibility table for program requirements over time
CUSP’s newly implemented eligibility table allows centralized, consolidated storing and management of the eligibility requirements of all the major funding programs incorporated in the data model.
- Centralized storage, maintenance, and updates of all major child care funding program eligibility requirements.
- Seamless, flexible, and timely incorporation of changes in program eligibility requirements into the CUSP model and data outputs.
- Enables longitudinal tracking of program requirements and how they correlate with program reach and utilization over time.
- CUSP provides up-to-date insight on regional and local socio-economic vulnerability that directly or indirectly impacts access to child care and child care funding for children and families.
- Enrichment of the CUSP model with the most recent available SVI data allows users to perform correlation and other analysis on child care service and reach in the context of a widely accepted indicator of socio-economic vulnerability of different geographies across the state.
- Updated SVI data allows policy makers to make decisions related to provider support and funding based on the most recent available data on local vulnerability.
CUSP Research Starter Kit in Power BI (Alpha)
With this alpha release, 3Si completed a proof-of-concept of its implementation of the Research Starter Kit in Power BI. The CUSP Research Starter Kit is now effectively agnostic between Tableau and Power BI, and can be implemented using either visualization tool.
- With this alpha release, 3Si completed a proof-of-concept of its implementation of the Research Starter Kit in Power BI. The CUSP Research Starter Kit is now effectively agnostic between Tableau and Power BI, and can be implemented using either visualization tool.
- Optimal for customers who wish to fully leverage their Microsoft cloud infrastructure, including Microsoft tools, services, and technologies, for their front-end analytical solutions, such as the RSK.
- Offers all the advantages of Power BI to CUSP Research Starter Kit users, such as a familiar and intuitive user interface, native integration with Excel, and flexible and easy-to-use capabilities for self-service analytics and visualizations.
- Converted the team3si Python package (referred to internally as the CUSP UCODE) into the CUSP Software Development Kit (SDK). For this, the 3Si engineering team incorporated and implemented the prevalent best practices for Python SDKs that are generally followed by other software providers.