Build the case for
your next tree investment.

What it covers

Pittsburg (an incorporated city) and Bay Point (a census-designated community the county governs), as drawn by the Census: one contiguous built-up area on the Suisun Bay shoreline in east Contra Costa County. 570 ranked areas of about 0.04 square miles each, home to an estimated 89,581 people. This build was cut from the county map because the two together hold 15 of the county’s 100 highest-need areas.

What the data shows

Half of residents (50%) live in areas with under 10% tree cover and 85% under 15%; the population-weighted average is 10%, the lowest of the five builds. The highest-need areas under the recommended mix are older, flat neighborhoods near the shore (Pittsburg SE and Pittsburg NE) and the larger apartment complexes (Woodland Hills, Fox Creek, Crestview). Home air conditioning runs from 69% to 96% of homes by Census estimate. Use the list’s “Show” menu to see Pittsburg, Bay Point or the unincorporated pockets separately, and the “Stable top tier” filter to keep only areas whose top-tier place survives the rank-stability re-draws: of the 57 top-tier areas, 53% stay there in at least 80% of draws, so the shortlist here needs a field check more than most.

How it differs from the county map

Surface heat is shown but not scored, for the same reason as on the county map and measured on this build’s own cells: within the built-up area, position still explains 28% of the variation in summer surface temperature, and hotter areas here are slightly greener, not barer, because the newer hillside subdivisions are hot and landscaped while the older shoreline neighborhoods are cooler and bare. Scoring heat would reward distance from the water. Surface heat has the numbers. Residents are spread by land area within each block group, not by buildings, because OpenStreetMap has traced only 8% of the housing here (Population); a hillside or grassland cell inside a populated block group can therefore show residents it may not have.

Planning note This is a screening map from public data, built by a student and not reviewed by any city or county. Use it to choose where to look first; confirm ownership, existing trees, utilities and planting space on the ground.

Use it to

Turn a study-area canopy map into a focused conversation about where to invest. A city team can shortlist areas for field assessment, compare policy priorities, and estimate the scale of a street-tree program in one place.

Why this approach

We bring canopy, residents and home-cooling context onto the same grid so staff can compare areas without assembling separate maps. The workflow connects a priority to its evidence and a planting scenario.

Planning note Use the shortlist to scope an assessment; confirm site feasibility and local costs before committing funds.

Use it to

Build a first-round investment shortlist that accounts for both missing shade and the people living with it. Open any area to explain the drivers behind its position in a budget or program-scoping discussion.

Why this approach

An additive score makes each input’s contribution inspectable. Canopy carries the largest default share because this product prioritizes tree investment; A/C and age add resident context. Population adjusts potential reach. These are explicit project policy choices, not externally prescribed weights.

Planning note Scores compare residential cells within this study area; they are not health-risk probabilities or cross-city rankings.

Rank stability A rank is an estimate with a band, not an exact position. 05b_stability.py re-draws each block group’s population and residents 65+ and each tract’s A/C estimate inside their published 90% margins of error (neighbouring areas cut from the same block group move together), jitters each non-zero recommended weight by ±10 points, and re-ranks; 300 draws. The detail panel shows each area’s 5th–95th percentile rank and how often it lands in the top tier (best 57 of 570). In this build the median band is 126 ranks wide, and of the 57 top-tier areas 53% stay top-tier in at least 80% of draws. Surface temperature and tree cover are held fixed (measurement error, no per-area margin published), as is the allocation of residents within a block group. Treat rank differences smaller than an area’s band as noise; use the top-tier share to shortlist.

Use it to

Test whether a proposed focus area remains a priority when staff emphasize shade, older residents or access to cooling. Use presets to structure a policy discussion instead of treating one ranking as the only answer.

Why this approach

Editable weights expose the value judgments behind a composite index. The JRC’s guidance supports examining rankings under alternative assumptions; it does not endorse our particular presets. Normalizing weights keeps their relative contribution explicit.

Count people more (the population multiplier)

The four condition sliders say what counts; this slider says how much the number of residents counts. Each area first gets a need figure from its conditions. That figure is then multiplied by a factor that runs from (1 − P) for the least-populated area up to 1 for the most-populated, where P is the slider. At P = 0% every area is judged on conditions alone. At the recommended 45%, the most-populated area counts about 1.8× the least. At 100%, an area with almost no residents scores near zero whatever its heat or shade, because there is almost nobody there to help. Population is normalized between the 2nd and 98th percentile of ranked areas, so one very crowded area does not flatten everyone else.

Planning note Presets represent alternative priorities. They change rankings, not planting costs.

Use it to

See the outdoor thermal context around proposed investments and compare it with canopy gaps. The separate heat view helps staff distinguish a shade-priority question from a surface-heat question.

Why this approach

Landsat provides a repeatable, spatially consistent thermal record. Summer medians reduce dependence on a single scene. Heat is shown but not scored here, as on the county map, and the reason is a measurement on this build’s own cells. Among ranked areas hotter than 35 °C (the built-up area, with marsh and water set aside) position alone explains 28% of the variance in summer surface temperature (39% county-wide, 11% in West Contra Costa): the gradient from the Suisun shoreline into the hills is most of the heat signal. And heat does not track missing shade here: it runs slightly with tree cover (r = +0.25), because the newer hillside subdivisions are both hotter and greener than the older neighborhoods near the shore. Scoring heat would reward distance from the water, not lack of trees. The layer, the sort and the slider all still work; the recommended weight is zero.

Planning note Surface temperature is not air temperature or a live heat alert. Current scenes cover summers 2022–2024.

Use it to

Locate shade deficits and establish a baseline for scoping a canopy assessment. Staff can compare canopy coverage across candidate areas before choosing where to investigate street-tree opportunities.

Why this approach

The USFS/CAL FIRE aerial canopy classification targets tree crowns, which fits a shade-investment question more directly than general greenness. Equal-area aggregation preserves ground-area proportions; the assessed urban boundary defines the denominator. Validation. All 570 ranked areas were compared with the CDFW 2025 lidar vegetation map (returns above 4.6 m): the aerial product tracks it at a rank correlation of 0.82; the satellite stand-in it previously used for 16 areas had none (−0.05), so those areas now use the lidar canopy directly (“trees · lidar”). The 52 height-model areas on the marsh edge have almost no trees in either source and are left as assessed.

Planning note Canopy coverage uses aerial and height-map sources; 16 ranked cells use a vegetation proxy for scoring, labeled in cell details; an aerial assessment covering under 10% of a cell is not used. Percentages describe assessed cover, not individual trees or verified planting space.

Use it to

Bring the scale of resident need into investment decisions. Population helps distinguish a sparsely occupied canopy gap from an area where a similar gap affects the surroundings of many more residents.

Why this approach

ACS five-year data supplies population, age and income on a consistent small-area geography. In Pittsburg and Bay Point the OpenStreetMap building mask failed the completeness check (2,889 mapped homes against 35,606 ACS housing units, 0.08 coverage), so residents are spread by land area within each block group instead, with cells whose dominant land cover is open water or wetland receiving none. That is a coarser placement than the building-based one used in West Contra Costa and San Ramon, and it is the reason a hillside or grassland cell inside a populated block group can show residents it may not have.

Planning note Cell populations are allocated estimates, not household counts. Published ACS margins of error are retained with the source data; they are not yet propagated into score uncertainty.

Use it to

Bring older residents into the shortlist for shade investment and outreach planning. The seniors preset lets staff see how prioritizing this population changes the areas they would investigate first.

Why this approach

CDC identifies adults aged 65+ as more susceptible to heat-related problems, supporting age as one input. The project smooths small-cell percentages to avoid unstable extremes; the 100-person smoothing strength is a design choice, not a CDC recommendation.

Planning note Shares are smoothed area estimates. They do not identify individual residents or their health needs.

Use it to

Identify areas where limited home cooling adds a reason to investigate shade and public cooling access. This gives the investment discussion a housing dimension alongside canopy and population.

Why this approach

Census LACE combines AHS air-conditioning information with the ACS’s geographic coverage to estimate prevalence at tract scale. We chose it to use a documented, cooling-specific national product instead of treating local income rank as A/C availability.

Planning note Experimental tract estimates, weighted by occupied homes. It does not establish working or affordable A/C in a particular home.

Use it to

Screen for areas where residents may face a long walk to a potential cooling facility. Use the map to focus a facility-verification exercise or a discussion about where additional relief may be needed.

Why this approach

OSM provides facility locations and a pedestrian graph in a common, reproducible source. Shortest-path routing represents mapped street connections more realistically than straight-line distance alone. This is a facility-access screen, not an emergency service directory.

Planning note Verify designation, hours, fees and accessibility. Category filters change markers only; walking estimates remain based on all mapped sites.

County-listed cooling locations and phone numbers ↗

The county EHSD bulletin lists 17 designated locations and asks visitors to call for hours; two are inside this study area (Pittsburg Senior Center, Ambrose Community Center in Bay Point) and are drawn on the map, the rest are listed only. Its June 2026 revision says El Cerrito and Kensington libraries lack A/C; that exclusion is applied uniformly but touches nothing here. The directory is not an open-now feed. access_snap_m records the straight-line cell-to-network connection; connections over 100 m are flagged for approach review, without implying that shorter approaches are verified. Walking estimates still use the 8 discovery sites, not just the official directory.

Use it to

Translate a priority area into a concrete program-scale question: what would planting a quarter, half or all of the mapped street capacity involve? Compare tree counts, canopy gain and indicative cost before commissioning site surveys.

Why this approach

Street length connects the scenario to a physical intervention that a public-works team can investigate. Both sides at 10 m spacing gives a transparent, repeatable capacity assumption. It is a project scenario convention, not a verified spacing requirement for every street.

Planning note Capacity is theoretical; confirm ownership, existing trees, utilities and space. Partial and unknown canopy coverage allow conditional scenarios; they do not establish a reliable future total or verified planting capacity.

Use it to

Put an initial scale of investment beside the shortlist. A shared unit-cost and crown-area assumption lets staff compare scenarios consistently and prepare questions for arborists and procurement teams.

Why this approach

A linear model makes every dollar and canopy increment traceable to the tree count. The cost per tree is a band, not a point: no Contra Costa contract price is published online, so the app offers three sourced presets and lets you type your own. The 40 m² mature crown is a planning assumption.

Planning note Indicative cost at assumed maturity; not a bid, lifecycle budget or financial ROI. Growth, survival and crown overlap are not modeled.

Use it to

Connect tree investment to cooler streets and shade where people walk and wait. Added crowns can reduce direct sun and surface heating; the air-cooling number describes a different, area-wide effect and does not capture the full benefit of standing in shade.

Why this approach

A review of urban cooling simulations estimates about 0.3 °C of afternoon air cooling per 10 percentage points of canopy under clear-sky summer conditions. We use that relationship for an illustrative scenario, separate from the satellite surface-temperature layer.

Planning note Literature-based illustration at maturity, not a locally calibrated forecast or a count of residents cooled.

Use it to

Compare the same geographic units across all map layers. A consistent grid helps staff move from a study-area overview to an identifiable area for follow-up without changing the unit of analysis.

Why this approach

H3 cells give each area a stable identifier and allow different source geographies to be combined. Keeping developed and undeveloped cells visible preserves geographic context while residential-only ranking keeps the shortlist aligned with resident priorities.

Planning note Cells are not census blocks or neighborhood boundaries. Place names are approximate; gray means unranked.

Use it to

Open a conversation with one or two figures that are easy to check against the map, then inspect the candidate areas directly.

Why this approach

These figures use only the tree-cover and population fields shown on the map, weighted by residents so that a populous area counts for more than an empty one. They are independent of the composite score and its weights.

Planning note These are screening figures from allocated census estimates and an aerial canopy product, not a tree inventory.

Use it to

Keep historical context available where the data supports it, without distracting the current investment workflow with an empty layer.

Why this approach

The application checks for actual HOLC grades before offering the control. Mapping Inequality is the documented historical source; it has no 1930s map of Pittsburg or Bay Point, so this build has no graded cells.

Planning note No HOLC layer does not imply no history of discrimination.

Use it to

Move from a quick shortlist to detailed exploration at your own pace. Switch layers to inspect evidence, adjust weights to test priorities, and change the planting share to scope an intervention.

Why this approach

Simple and Explore separate routine review from advanced controls. Units and basemaps change presentation only. Contextual guide links preserve the map in its original tab, so checking a method does not interrupt the scenario.

Planning note Your mix of priorities and the map layer are kept for this browser tab: a reload lands where you were, and the home button returns to the start screen without resetting them. A new tab starts fresh at the start screen. Planting shares last for the current page session.

Use it to

Read the same ranked list from a different angle: the most people first, the hottest ground first, the least tree cover first, the longest walk first. Filters narrow the list to areas that meet a threshold; search finds one area by name or cell number.

Why this approach

The rank badge is the answer to the question set under What matters most, so it stays fixed. Sorting is a reading aid, which is why the count line says what the list is sorted by and in which direction whenever it is not in rank order.

Planning note A sort is not a ranking. Use What matters most to change what the score rewards; use sort and filters to find areas within the ranking you already have.

Use it to

Share a shortlist or one area with colleagues, keep a record of the weights and planting share behind a recommendation, and reopen the same view. A CSV opens in a spreadsheet; a JSON file reloads into this map. Nothing is uploaded: files are generated in the browser and saved on your computer.

Ranked list — the Export & share section

Save scenario (JSON) saves the ranked list with the full scenario header, for reopening. Spreadsheet (CSV) saves every ranked cell in the order the current weights put them, one row per cell, with plain column names (see the column key below). Open a saved scenario… reads a scenario file back in and restores the weights, population weighting, any planting shares you set and the selected area; the status line under the buttons says what was loaded and whether the data version matches.

Selected area — in the area panel

Save this area (JSON) and Spreadsheet row (CSV) save one record for the cell you have open: its metrics, where it ranks under the weights in force and under the shipped defaults, the sources and coverage behind each number, and the planting scenario at the share you chose (trees, canopy gain in percentage points, indicative cost, illustrative cooling). Reloading an area file restores the weights and reopens that area.

Why this approach

A ranking only means something with its weights, assumptions and data version attached, so every export carries them: the weights in force, the population multiplier, the preset if one matches, the planting constants, per-cell source and coverage flags, and the SHA-256 fingerprint of the data file that was loaded. Reloading says plainly if the data version differs.

Column key

Spreadsheet columns use the plain names in the first column; scenario (JSON) files use the internal keys in the second and carry this key as field_key.

Planning note An export documents an exploratory scenario. It does not verify planting sites, costs or facility status, and it carries the same limitations as the map it came from.

Field checks Each ranked area has a Field check box in its detail panel: a status (To visit; Checked · feasible, partly feasible or not feasible; Planted) and a free-text note. They are saved in the browser you typed them in, per city, and are never uploaded. Every area and shortlist export carries them as field_check_status, field_check_note and field_check_updated, and the printed briefing shows them under each shortlisted area, so a colleague can see what was verified on the ground and what was seen. Clearing the browser’s site data clears them; export first.

Use it to

Compare living vegetation with tree cover to identify areas worth investigating for shade. A greener area can contain grass or shrubs and still need trees; the two layers help staff ask that question without confusing greenness with canopy.

Why this approach

Satellite greenness supplies broad vegetation context across the grid, including outside aerial canopy coverage. It is publicly available in Explore, leaving the default priority workflow focused. The active planting calculator uses mapped street length, not the buffered right-of-way area fields.

Planning note Greenness does not establish percent cover or planting feasibility; exported street-buffer fields do not establish ownership or plantability.