Explore infrastructure and capital projects • tampaBayOpenData

Which kinds of capital work appear on a public map, and where are their locations? This tutorial uses the checked City of Tampa capital improvement projects layer to explore those questions. The layer contains mapped features with project type, phase, status, location, dates, and cost fields. Its service description identifies a public-facing selection; it is not the City’s full capital ledger. A map feature is also not necessarily one project.

The catalog and field-inspection chunks below run entirely offline. Retrieval, summaries, and the ggplot2 chart are shown as code to run in your own R session. They do not contact the publisher during vignette or website builds, so this page makes no claim about current counts or project status.

Set up and find a source

Install the package from CRAN if needed. knitr formats the tables and ggplot2 draws the chart; neither is a required package import.

install.packages("tampaBayOpenData")
install.packages("knitr")        # for the summary tables
install.packages("ggplot2")      # optional chart dependency

Search the bundled checked catalog without sending a request. The search term can surface projects from more than one publisher; the example selects the Tampa layer by its stable package ID.

library(tampaBayOpenData)

choices <- tbod_search_datasets("project", source = "checked")
knitr::kable(choices[, c("id", "title", "publisher", "category")],
             caption = "Checked project-related layers in this package")

Checked project-related layers in this package
id	title	publisher	category
capital-projects	Capital improvement projects	City of Tampa	Infrastructure
stpete-public-works-projects	St. Petersburg mapped public works projects	City of St. Petersburg	Infrastructure

Checked entries are selected references, not a full inventory of either publisher’s projects. source = "live" can discover other indexed layers, but portal searches are bounded and may miss items; see live discovery. The Data Coverage dashboard explains what “checked” verifies.

Read the source and scope note before analyzing records. The verified date is an endpoint check, not the date of the most recent project update.

info <- tbod_dataset_info("capital-projects")
info[c("title", "publisher", "source_url", "verified", "scope_note")]
#> $title
#> [1] "Capital improvement projects"
#> 
#> $publisher
#> [1] "City of Tampa"
#> 
#> $source_url
#> [1] "https://tampa.maps.arcgis.com/home/item.html?id=2226a20de08641e38cedd1c2d7cac69e"
#> 
#> $verified
#> [1] "2026-09-29"
#> 
#> $scope_note
#> [1] "The service description says its definition query selects Show to Public = Yes, while the layer metadata does not expose that definition expression. Coordinates use ArcGIS WKID 103023 / latestWKID 6443, in feet, rather than the Web Mercator CRS used by the other selected layers."

Inspect the available fields

tbod_schema() reads the package’s saved schema without network access. The table shows the exact ArcGIS field names used below. When you retrieve the layer, the package compares current metadata with this saved schema and stops if an incompatible change would make the analysis unsafe.

schema <- tbod_schema("capital-projects")
fields <- c("OBJECTID", "projid", "projname", "projtype", "projphase",
            "status", "Neighborhood", "estcost", "actcost", "planstart",
            "actend")
field_guide <- schema$fields[
  schema$fields$name %in% fields,
  c("name", "type", "alias")
]
knitr::kable(field_guide, caption = "Saved Tampa capital-project field schema")

Saved Tampa capital-project field schema
name	type	alias
OBJECTID	esriFieldTypeOID	OBJECTID
projid	esriFieldTypeString	Project Identifier
projname	esriFieldTypeString	Project Name
projtype	esriFieldTypeString	Project Type
planstart	esriFieldTypeDate	Planned Start Date
estcost	esriFieldTypeDouble	Estimated Cost
projphase	esriFieldTypeString	Project Phase
actend	esriFieldTypeDate	Actual End Date
actcost	esriFieldTypeDouble	Actual Cost
status	esriFieldTypeString	Status
Neighborhood	esriFieldTypeString	Neighborhood

OBJECTID identifies a map feature. projid is a source project identifier, but one ID can occur on several features. projtype, projphase, and status are publisher labels; inspect their actual values before combining categories. estcost and actcost are source attributes, not audited budget or payment transactions. planstart and actend are declared ArcGIS dates; inspect their returned classes before using them. Depending on the layer’s time-zone metadata, the package may keep unknown-zone dates as raw epoch milliseconds rather than interpreting them as UTC instants.

Retrieve a complete feature set

Run this chunk when you want a current analysis. It requests all mapped features, checks retrieval provenance, and stops if the publisher cannot return a complete result. There is no limit = 100 preview because a preview would make category shares misleading.

projects <- tryCatch(
  tbod_get_dataset(
    "capital-projects",
    fields = fields,
    total_timeout = 180
  ),
  error = function(e) {
    stop("Capital-project retrieval failed; no summary was produced: ",
         conditionMessage(e), call. = FALSE)
  }
)
source <- tbod_provenance(projects)
if (!isTRUE(source$complete) ||
    !isTRUE(source$returned_rows == nrow(projects)) ||
    !isTRUE(source$matched_rows == nrow(projects))) {
  stop("The capital-project result is incomplete or its row counts disagree; ",
       "do not summarize it.")
}
if (nrow(projects) == 0L) {
  stop("The publisher returned no mapped capital-project features.")
}
source[c("publisher", "source_url", "retrieved_at", "matched_rows",
         "returned_rows", "complete", "integrity")]

The package checks returned object IDs for missing or repeated feature records during retrieval. The live service may still change attributes between requests. Save the provenance alongside any result you plan to reuse:

dir.create("analysis-output", showWarnings = FALSE)
saveRDS(list(data = projects, provenance = source),
        "analysis-output/tampa-capital-projects.rds")

Inspect the returned classes, a few rows, and missing values before making a chart. In particular, status and projphase are separate source fields; an actend value alone is not proof that a project was accepted as complete.

str(projects[c("OBJECTID", "projid", "projtype", "projphase", "status",
               "Neighborhood", "estcost", "actcost", "planstart", "actend")])
utils::head(projects[c("OBJECTID", "projid", "projname", "projtype",
                       "projphase", "status")], 8)
vapply(projects[c("projid", "projtype", "projphase", "status", "estcost",
                  "actcost", "actend")], function(x) sum(is.na(x)), integer(1))

Clean labels and summarize mapped activity

Keep unknown labels visible. The helper changes only missing or blank text to (Missing); it does not merge source categories with similar names. It also sets aside blank and suspected placeholder IDs (N/A, NA, 0000000) for a later ID audit. Those placeholder rules are an analytical choice based on a dated exploration, not an official publisher codebook.

label_or_missing <- function(x) {
  value <- trimws(as.character(x))
  value[is.na(value) | value == ""] <- "(Missing)"
  value
}

projects$project_type <- label_or_missing(projects$projtype)
projects$phase_label <- label_or_missing(projects$projphase)
projects$status_label <- label_or_missing(projects$status)
projects$neighborhood_label <- label_or_missing(projects$Neighborhood)

working_id <- trimws(as.character(projects$projid))
blank_id <- is.na(working_id) | working_id == ""
placeholder_id <- !blank_id & toupper(working_id) %in%
  c("N/A", "NA", "0000000")
working_id[blank_id | placeholder_id] <- NA_character_
projects$working_id <- working_id

Count feature rows by the source’s project type and phase. This answers what the public map contains, without claiming a count of distinct projects. The neighborhood table uses the publisher’s Neighborhood attribute; it does not assign points to a new geographic boundary.

type_counts <- as.data.frame(table(projects$project_type),
                             stringsAsFactors = FALSE)
names(type_counts) <- c("project_type", "mapped_features")
type_counts <- type_counts[
  order(-type_counts$mapped_features, type_counts$project_type), , drop = FALSE
]
knitr::kable(type_counts, caption = "Mapped feature rows by source project type")

phase_counts <- as.data.frame(
  table(projects$project_type, projects$phase_label),
  stringsAsFactors = FALSE
)
names(phase_counts) <- c("project_type", "phase", "mapped_features")
phase_counts <- phase_counts[phase_counts$mapped_features > 0L, , drop = FALSE]
phase_counts <- phase_counts[
  order(-phase_counts$mapped_features, phase_counts$project_type), , drop = FALSE
]
knitr::kable(utils::head(phase_counts, 15),
             caption = "Most common type and phase combinations, by feature row")

status_counts <- as.data.frame(table(projects$status_label),
                               stringsAsFactors = FALSE)
names(status_counts) <- c("source_status", "mapped_features")
status_counts <- status_counts[
  order(-status_counts$mapped_features, status_counts$source_status), , drop = FALSE
]
knitr::kable(utils::head(status_counts, 15),
             caption = "Most common source status labels, by feature row")

neighborhood_counts <- as.data.frame(table(projects$neighborhood_label),
                                     stringsAsFactors = FALSE)
names(neighborhood_counts) <- c("neighborhood", "mapped_features")
neighborhood_counts <- neighborhood_counts[
  order(-neighborhood_counts$mapped_features, neighborhood_counts$neighborhood),
  , drop = FALSE
]
knitr::kable(utils::head(neighborhood_counts, 10),
             caption = "Top source neighborhood labels, by mapped feature row")

The chart uses the same feature-level counts. It is generated from your live retrieval when you run it; the website does not display invented or stale type counts.

if (!requireNamespace("ggplot2", quietly = TRUE)) {
  stop("Install ggplot2 to draw this chart: install.packages('ggplot2')")
}
top_types <- utils::head(type_counts, 12)
print(ggplot2::ggplot(
  top_types,
  ggplot2::aes(x = stats::reorder(project_type, mapped_features),
               y = mapped_features)
) +
  ggplot2::geom_col(fill = "#276678", width = 0.72) +
  ggplot2::coord_flip() +
  ggplot2::labs(
    title = "Mapped Tampa capital-project features by type",
    subtitle = paste("Source retrieved",
                     format(source$retrieved_at, "%Y-%m-%d %H:%M UTC",
                            tz = "UTC")),
    x = NULL, y = "Mapped feature rows",
    caption = "City of Tampa public-facing capital-project layer; types are source labels"
  ) +
  ggplot2::theme_minimal(base_size = 12) +
  ggplot2::theme(panel.grid.major.y = ggplot2::element_blank()))

A taller bar indicates more mapped locations carrying that source type, not more unique projects or a larger budget. The top-ten neighborhood table is likewise a count of mapped feature rows with those source labels. Multiple locations may share a project ID, and blank locations or labels can affect both views.

This archived map gives a geographic example while keeping the vignette build offline. It was generated from the City’s public capital-project layer and shoreline-adjusted boundary on October 3, 2026, using the repository’s map script. The plotted 394 features are a dated snapshot, not a current count or a project-completion map.

Archived map of City of Tampa capital-project feature locations on October 3, 2026, over a shoreline-adjusted city boundary.

Source: City of Tampa public ArcGIS capital-project and boundary layers, both recorded as CC0 in the checked catalog. The boundary is map context, not a legal survey. Multiple points can represent one project.

Check the unit before interpreting cost or completion

Start by seeing whether the cost fields contain values. This is a diagnostic summary of feature-row attributes; do not read its quartiles or totals as a project budget distribution. A zero actcost could reflect the source’s reporting convention rather than proof of zero spending.

summary(projects[c("estcost", "actcost")])

Review ID repetition and disagreement across rows. The table below identifies project IDs that require further source review; it never sums estcost or actcost across map features. A single numeric value repeated on several points is one recorded value, not several payments. Different values under one ID should not be resolved by arbitrarily taking the first row.

usable <- !is.na(projects$working_id)
rows_by_id <- split(which(usable), projects$working_id[usable])

if (length(rows_by_id) == 0L) {
  warning("No usable project IDs; project-level cost review is unavailable.")
} else {
  id_audit <- do.call(rbind, lapply(names(rows_by_id), function(id) {
    rows <- projects[rows_by_id[[id]], , drop = FALSE]
    estimates <- unique(rows$estcost[is.finite(rows$estcost)])
    actuals <- unique(rows$actcost[is.finite(rows$actcost)])
    phases <- setdiff(unique(rows$phase_label), "(Missing)")
    statuses <- setdiff(unique(rows$status_label), "(Missing)")
    data.frame(
      working_id = id,
      mapped_features = nrow(rows),
      distinct_estimates = length(estimates),
      distinct_actual_costs = length(actuals),
      distinct_phases = length(phases),
      distinct_statuses = length(statuses)
    )
  }))

  checks <- data.frame(
    measure = c("Mapped feature rows", "Rows without a usable project ID",
                "Distinct usable IDs", "IDs on multiple feature rows",
                "IDs with multiple nonmissing estimates",
                "IDs with multiple nonmissing actual costs"),
    count = c(nrow(projects), sum(!usable), nrow(id_audit),
              sum(id_audit$mapped_features > 1L),
              sum(id_audit$distinct_estimates > 1L),
              sum(id_audit$distinct_actual_costs > 1L))
  )
  knitr::kable(checks, caption = "Checks before any project-level interpretation")

  needs_review <- id_audit[
    id_audit$distinct_estimates > 1L |
      id_audit$distinct_actual_costs > 1L |
      id_audit$distinct_phases > 1L |
      id_audit$distinct_statuses > 1L,
    , drop = FALSE
  ]
  needs_review <- needs_review[
    order(-needs_review$mapped_features, needs_review$working_id), , drop = FALSE
  ]
  knitr::kable(utils::head(needs_review, 10),
               caption = "Example IDs with differing source attributes")
}

If you need a canonical project count or total budget, first obtain a publisher-supported definition of a project ID, a rule for multiple locations and conflicting values, and the relevant financial ledger. projphase, status, actend, and actcost are source attributes for follow-up; none alone establishes verified completion, final expenditure, or when money was paid. The public-facing selection may omit unmapped or unpublished projects.

Reproducibility and next steps

The example uses the checked capital-projects ID and the City of Tampa ArcGIS item. The catalog records that item’s CC0 statement and the City’s source terms; consult tbod_dataset_info("capital-projects")$terms before redistributing data. Record your package version, retrieval timestamp, source URL, fields, and provenance with any analysis. Rerunning the live code may produce different labels, rows, costs, or statuses as the publisher updates the service.

For a complementary housing and permitting workflow, see Housing and development activity. For observations with a very different sampling structure, see Water quality around Tampa Bay. The Data Coverage dashboard distinguishes checked catalog entries from current endpoint verification and explains the catalog’s known gaps.