Mapping Tampa capital-project locations • tampaBayOpenData

Introduction

Capital improvement projects are public investments in things such as streets, water systems, parks, and public buildings. A map shows where the City has recorded this work and which project types appear in different places.

In this tutorial, you will use the tampaBayOpenData package to find the City of Tampa’s public capital-project layer, download its mapped locations, and color them by the project’s type as recorded by the City. You will also make a small table of those types and check whether multiple source rows share a project identifier. The finished map shows locations, while the table counts source feature rows. Neither is automatically a count of distinct projects.

You need basic R data-frame skills, but no GIS experience. The sf package holds shapes and coordinates, and ggplot2 draws the map. The live-data steps are shown for you to run in order in an internet-connected R session. They do not run during ordinary package checks or website builds. If you have a local copy of this repository and want to render the article with current City data, run Sys.setenv(TAMPA_OPEN_DATA_LIVE = "true") in R before calling rmarkdown::render("vignettes/mapping-capital-improvements.Rmd"). Your results may differ as the City updates its data.

Find the data

Install the package and the two optional mapping packages if you need them. knitr formats tables, and rmarkdown renders the article.

install.packages("tampaBayOpenData")
install.packages(c("sf", "ggplot2", "knitr", "rmarkdown"))

Search the package’s checked catalog. This reads a bundled list, so it does not contact a data publisher. The jurisdiction argument keeps the search focused on Tampa.

library(tampaBayOpenData)

project_matches <- tbod_search_datasets(
  "capital", jurisdiction = "tampa", source = "checked"
)
knitr::kable(
  project_matches[, c("id", "title", "publisher", "geometry_type")],
  caption = "Checked Tampa capital-project data"
)

Checked Tampa capital-project data
id	title	publisher	geometry_type
capital-projects	Capital improvement projects	City of Tampa	esriGeometryPoint

The capital-projects entry is the City’s public-facing capital-project point layer. A point marks an individual location. A line could trace a road or pipe; a polygon could outline a work area. The checked Tampa catalog does not currently identify matching line or polygon capital-project layers. The checked city-boundary polygon is useful as a background, but it is not another set of projects. Even when several shapes depict the same underlying work, they should not be added together as separate projects.

Check the source note and saved field names before using them. The verified date is when the package checked the endpoint, not when each project was updated.

project_info <- tbod_dataset_info("capital-projects")
project_info[c("publisher", "source_url", "verified", "scope_note")]
#> $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."

project_fields <- c("OBJECTID", "projid", "projtype", "Neighborhood")
project_schema <- tbod_schema("capital-projects")
stopifnot(all(project_fields %in% project_schema$fields$name))
project_schema$fields[
  match(project_fields, project_schema$fields$name),
  c("name", "alias", "type")
]
#> # A tibble: 4 × 3
#>   name         alias              type               
#>   <chr>        <chr>              <chr>              
#> 1 OBJECTID     OBJECTID           esriFieldTypeOID   
#> 2 projid       Project Identifier esriFieldTypeString
#> 3 projtype     Project Type       esriFieldTypeString
#> 4 Neighborhood Neighborhood       esriFieldTypeString

OBJECTID identifies a map feature, projid is a source project identifier, projtype contains the City’s type label, and Neighborhood is the City’s neighborhood label. We will use only these fields and the geometry, which keeps the download small.

Download the data

tbod_get_dataset() retrieves the current records. spatial = TRUE asks for an sf object: an ordinary table with a geometry column. out_sr = 4326 requests latitude and longitude coordinates for both layers. We retrieve all matching records because a limited preview would give misleading category shares. The checked project layer had hundreds of features in the repository’s dated example, rather than a large regional inventory.

capital_projects <- tbod_get_dataset(
  "capital-projects",
  fields = project_fields,
  spatial = TRUE,
  out_sr = 4326,
  total_timeout = 180
)

tampa_boundary <- tbod_get_dataset(
  "city-boundary", fields = "OBJECTID",
  spatial = TRUE, out_sr = 4326
)

Stop with a clear message if either source has no features or if the download is incomplete. The package records its source and retrieval time; keep that information when sharing results. A server failure or changed field name also stops the example rather than creating a substitute dataset.

project_source <- tbod_provenance(capital_projects)
boundary_source <- tbod_provenance(tampa_boundary)

if (!isTRUE(project_source$complete) ||
    !isTRUE(boundary_source$complete)) {
  stop("A source download was incomplete; try again later.")
}
if (nrow(capital_projects) == 0 || nrow(tampa_boundary) == 0) {
  stop("A source returned no map features; try again later.")
}
project_source[c("publisher", "source_url", "retrieved_at")]

The two layers must use the same coordinate reference system, or CRS, to line up on the map. EPSG:4326 is the common latitude-longitude CRS requested above.

if (!isTRUE(sf::st_crs(capital_projects)$epsg == 4326) ||
    !isTRUE(sf::st_crs(tampa_boundary)$epsg == 4326)) {
  stop("The map layers were not returned in EPSG:4326.")
}

Prepare and categorize projects

Use the publisher’s projtype values as categories. Trimming spaces makes labels consistent without guessing what a description means. Missing or blank values get an explicit (Missing source type) label. A check prevents that label from merging with an actual source value. Add the source ID so it stays clear where these records came from if you later combine data.

capital_projects$project_category <- trimws(
  as.character(capital_projects$projtype)
)
missing_type <- is.na(capital_projects$project_category) |
  capital_projects$project_category == ""
missing_label <- "(Missing source type)"
if (missing_label %in% capital_projects$project_category[!missing_type]) {
  stop("The source uses the missing-type display label; choose another label.")
}
capital_projects$project_category[missing_type] <- missing_label
capital_projects$source_dataset <- "capital-projects"

sort(unique(capital_projects$project_category))

The project identifier deserves a separate check. Blank identifiers are treated as missing. Repeated nonblank values suggest that one project may have several mapped points. Some nonblank values could also be placeholder codes; the City would need to confirm their meaning before anyone uses IDs to count distinct projects.

capital_projects$project_id <- trimws(as.character(capital_projects$projid))
blank_id <- is.na(capital_projects$project_id) |
  capital_projects$project_id == ""
capital_projects$project_id[blank_id] <- NA_character_

known_ids <- capital_projects$project_id[
  !is.na(capital_projects$project_id)
]
id_frequency <- table(known_ids)

data.frame(
  check = c("Source feature rows", "Rows missing an ID", "IDs on multiple rows"),
  value = c(nrow(capital_projects),
            sum(is.na(capital_projects$project_id)),
            sum(id_frequency > 1))
)

These are checks on feature rows and ID values, not a project total. We do not combine this point layer with other project geometries or remove rows merely because an ID repeats.

Create an infrastructure project map

First remove points with missing geometry from the map. Keep them in the summary table: a record without coordinates still has a source category.

mapped_projects <- capital_projects[
  !sf::st_is_empty(capital_projects),
]
if (nrow(mapped_projects) == 0) {
  stop("No capital-project locations are available to map.")
}

To keep the legend short, show the five most common named source types among points with coordinates in their own colors. Other named types share an (Other types on map) color, while missing types retain their own label. This groups labels for display only; the table below retains every source type.

visible_type_counts <- as.data.frame(
  table(mapped_projects$project_category),
  stringsAsFactors = FALSE
)
names(visible_type_counts) <- c("project_category", "plotted_locations")
visible_type_counts <- visible_type_counts[
  order(visible_type_counts$plotted_locations, decreasing = TRUE),
]
named_types <- visible_type_counts$project_category[
  visible_type_counts$project_category != missing_label
]
top_types <- utils::head(named_types, 5)

other_label <- "(Other types on map)"
if (other_label %in% mapped_projects$project_category) {
  stop("The source uses the map grouping label; choose another label.")
}
mapped_projects$map_category <- mapped_projects$project_category
other_type <- !mapped_projects$map_category %in%
  c(top_types, missing_label)
mapped_projects$map_category[other_type] <- other_label

The boundary is a light background. Project points sit on top and are colored by the source type. ggplot2 handles the shared CRS, and the legend names the displayed categories.

project_map <- ggplot2::ggplot() +
  ggplot2::geom_sf(data = tampa_boundary, fill = "#F1F4F5",
                   color = "#8A9BA4", linewidth = 0.3) +
  ggplot2::geom_sf(data = mapped_projects,
                   ggplot2::aes(color = map_category),
                   size = 1.8, alpha = 0.8) +
  ggplot2::scale_color_brewer(palette = "Dark2", name = "Source project type") +
  ggplot2::labs(
    title = "Where are Tampa's mapped capital improvements?",
    subtitle = "Public project locations from the City of Tampa",
    caption = "Each dot is a mapped feature; one project can have several dots."
  ) +
  ggplot2::theme_minimal(base_size = 12) +
  ggplot2::theme(axis.title = ggplot2::element_blank())
print(project_map)

The live code above creates a color-coded map from the checked catalog layer. The map below provides a dated offline example using the City’s related capital-project dashboard point layer, saved in a public GeoJSON archive on August 23, 2026 (retrieval note). Its 192 points and their projtype labels were saved together. As in the live code, the five most frequent types have their own colors, and the remaining types share an (Other types on map) color. The dashboard is a separate City service from the checked capital-projects endpoint, so its feature IDs and counts should not be combined with a current download. The light backdrop is the checked Tampa city boundary.

Dated map of 192 City of Tampa dashboard capital-project point features colored by the five most frequent project-type labels and an other-types group, over the shoreline-adjusted city boundary.

Summarize the results

The table counts source feature rows by the City’s project-type label. It includes records without coordinates, so its counts may exceed the number of points on your live map. The first row identifies the most frequent label in your download; the answer can change as the publisher updates the layer.

category_summary <- as.data.frame(
  table(capital_projects$project_category), stringsAsFactors = FALSE
)
names(category_summary) <- c("project_category", "feature_rows")
category_summary <- category_summary[
  order(category_summary$feature_rows, decreasing = TRUE),
]
knitr::kable(
  category_summary,
  row.names = FALSE,
  caption = "City project-type labels by source feature row"
)

For a fixed example visible in this offline article, the small table below counts the 192 dashboard point features in the August 23 archive used for the color map. These are not counts from the checked catalog endpoint. Run the code above for current values from that endpoint.

Source project type	Feature rows
Transportation	55
Water	46
Wastewater	43
Stormwater Engineering	23
Parks & Recreation	17
Facility Management	4
Contract Administration	4

Transportation has the most source feature rows in this snapshot (55), followed by Water (46) and Wastewater (43). The bar chart repeats those counts; the color map shows where the same archived features were recorded.

Bar chart of the 192 City of Tampa dashboard capital-project source feature rows by project type on August 23, 2026.

In the dated map, many dots cluster in central Tampa. Transportation and Wastewater points also appear farther north and south. These are visual impressions of published feature locations, not measured investment levels.

The City’s Neighborhood field offers another simple view of concentration. The first row in this table has the most source feature rows with that label. These labels are not a spatial calculation, and some may be blank or missing.

capital_projects$source_neighborhood <- trimws(
  as.character(capital_projects$Neighborhood)
)
missing_neighborhood <- is.na(capital_projects$source_neighborhood) |
  capital_projects$source_neighborhood == ""
missing_neighborhood_label <- "(Missing neighborhood)"
if (missing_neighborhood_label %in%
    capital_projects$source_neighborhood[!missing_neighborhood]) {
  stop("The source uses the missing-neighborhood display label; choose another.")
}
capital_projects$source_neighborhood[missing_neighborhood] <-
  missing_neighborhood_label
neighborhood_summary <- as.data.frame(
  table(capital_projects$source_neighborhood), stringsAsFactors = FALSE
)
names(neighborhood_summary) <- c("source_neighborhood", "feature_rows")
neighborhood_summary <- neighborhood_summary[
  order(neighborhood_summary$feature_rows, decreasing = TRUE),
]
knitr::kable(utils::head(neighborhood_summary, 10), row.names = FALSE)

Look at the map to see whether some category colors are concentrated or spread across Tampa. A cluster means more features are mapped there, not necessarily more distinct projects or dollars. A project may have several locations, and source coverage may vary by area. The map does not show funding amounts, construction progress, or completed infrastructure.

Limitations and next steps

This is the City’s public-facing capital-project layer, not a complete capital ledger. The checked catalog’s verification date does not measure the freshness of every record. Labels, identifiers, and locations can be missing or updated; one project may have multiple point features. The boundary is a shoreline-adjusted map representation, not a legal survey. Geographic concentration alone cannot establish whether investment is fair: population, need, project size, cost, and unmapped work would matter too.

To adapt this workflow, search the checked catalog for another topic or publisher, inspect its source note and schema, then change the dataset ID and field names. A true line or polygon project layer would need its own source review and map symbol. Before combining layers, check whether their features share project identifiers. The infrastructure article explores ID, status, phase, and cost fields in more detail.