A path through the evidence
The problem is not always a lack of data. Teams may already have datasets, maps, and guidance, but find them scattered across platforms and described in terminology that does not match the question at hand. Technical-assistance time can then return repeatedly to locating variables and rebuilding basic views, leaving less room for comparison, limitations, and interpretation.
I examine how people currently find information, what they need to understand before using it, where recurring preparation occurs, and how an analytical result will be discussed or communicated. The resulting system can support the whole path rather than stopping when a map or chart appears.
Connecting source data to use
The source layer defines records, relationships, measures, geography, and provenance. Reusable rules can validate, transform, combine, and publish that material consistently.
The analytical layer may include maps, dashboards, comparison tools, or focused applications. The interpretive layer places definitions, limitations, relevant comparisons, and discussion guidance close to the evidence they concern. Together they determine whether someone can move from finding a resource to understanding what it can support.
Discovery with real questions
I begin with current project questions and the way teams are already approaching them. Direct assistance sessions and task-based testing can show whether people can locate relevant information, interpret it, and decide how they might use it.
Those observations guide the information architecture, interface, analytical workflow, and production process. I carry the work through data preparation, design, implementation, testing, documentation, and handoff. When the same preparation recurs, I can turn it into a reproducible workflow rather than continuing to rebuild it manually.
Extending GIS and analytical platforms
An existing GIS, public-data portal, or analytical platform may remain the right place for the work. Custom development can focus on organization, transformations, interfaces, and interpretive context that the platform does not supply on its own.
For All Children Thrive, ArcGIS Hub remained the shared environment for data and applications. A reusable Python workflow accelerated recurring Census-based map preparation, while the maps, applications, and guides remained available through the Hub. The automation handled repeatable production work; interpretation remained part of the analytical and community process.
A working example
Shared data infrastructure for locally defined community questions
Within UCLA’s data technical-assistance program, I identified how scattered resources were slowing work with 14 community coalitions, then proposed and built a central ArcGIS Hub. An initial test with five users showed that people could often find a resource but were less certain about the next step.
I reorganized the Hub around finding, understanding, analyzing, and communicating evidence. Four users subsequently completed the full path. Across the engagement, I produced more than 35 layers, curated datasets, maps, applications, dashboards, walkthroughs, and guides. A reusable preparation workflow reduced one recurring task from approximately four hours to approximately 30 minutes.
A separate Stockton analysis compared youth-selected and city-selected parks using walksheds and a shared priority-area layer. The spatial comparison supported the discussion without replacing the youth participants’ knowledge of park use, safety, facilities, access, and local conditions.
Read the All Children Thrive case studyQuestions to resolve before development
Do we need another dashboard?
Not necessarily. The useful intervention may be a clearer information architecture, a reproducible transformation, an interpretive guide, or a connection between resources that already exist. The decision should follow the question and the observed workflow.
What should be automated?
Repeated, well-defined preparation is a good candidate. Analytical judgment, interpretation, and decisions about use still require the people who understand the research and its setting.
Where should interpretation guidance appear?
As close as possible to the relevant measure, map, or comparison. Definitions and limitations are easier to use when they are part of the analytical path rather than placed in a separate document people must know to find.
How should local knowledge and public data relate?
Public data can make comparisons visible and test a specific proposition. It does not replace knowledge of local history, conditions, priorities, or lived use. A sound analytical system keeps both forms of evidence legible.
Discuss a data or GIS workflow
If a research or community-data program is spending too much time locating, preparing, or re-explaining the same material, a current question and the resources already in use are enough to begin.
Email Christopher to arrange an introductory call