Explore housing and development activity

This walkthrough asks: where are the cases in Tampa’s current development application map, and what statuses do its mapped records have? You will find a source, retrieve all its current records, check its fields, clean text values, and chart the neighborhoods named in the layer. A separate Pinellas County housing layer illustrates how a related question can require a different source. The package’s checked catalog is selective; these layers do not represent all housing or development in either county.

The catalog and schema chunks below run without network access when this page is built. Live chunks are shown but not run during package checks or the website build. Run them in order in an internet-connected R session. Their results and chart will reflect the publisher’s records at the time you run them. A dated, illustrative chart from a separate Pinellas analysis appears below; no record-level source data is bundled here.

Set up

Install the package from CRAN, knitr for the catalog table, and ggplot2 for the chart. The installation commands are not run when this page is built.

install.packages("tampaBayOpenData")
install.packages("knitr")               # for the catalog table
install.packages("ggplot2")             # optional tutorial dependency

library(tampaBayOpenData)

Find a suitable layer

source = "checked" searches bundled entries offline. Search several terms because a permit map, an active application view, and a housing program inventory answer different questions. The exact catalog results may change when the package is updated.

terms <- c("housing", "permit", "development")
matches <- lapply(terms, function(term) {
  found <- tbod_search_datasets(term, source = "checked")
  found$search_term <- term
  found[, c("search_term", "id", "title", "publisher", "category",
            "verified")]
})
catalog_hits <- do.call(rbind, matches)
knitr::kable(catalog_hits, row.names = FALSE)

search_term	id	title	publisher	category	verified
housing	pinellas-assisted-housing	Pinellas assisted housing	Pinellas County	Housing	2026-10-05
permit	construction-permits	Permits (active GIS view)	City of Tampa	Development	2026-09-29
development	development-cases	Active development coordination cases	City of Tampa	Development	2026-09-29
development	tbrpc-developments-of-regional-impact	Developments of regional impact	Tampa Bay Regional Planning Council	Development	2026-10-05
development	community-redevelopment	Community redevelopment areas	City of Tampa	Planning	2026-10-02
development	construction-permits	Permits (active GIS view)	City of Tampa	Development	2026-09-29
development	pinellas-assisted-housing	Pinellas assisted housing	Pinellas County	Housing	2026-10-05

This tutorial uses the checked City of Tampa development-cases entry, a point layer of active entitlement applications. The checked construction-permits entry is an active permit viewer with CREATEDDATE and LASTUPDATE, but no advertised permit issue-date field. Pinellas County’s pinellas-assisted-housing entry describes an assisted-housing program inventory, not every housing development. Read each entry’s scope and reuse terms before choosing a source.

case_info <- tbod_dataset_info("development-cases")
case_info[c("publisher", "title", "source_url", "verified",
            "scope_note", "terms")]
#> $publisher
#> [1] "City of Tampa"
#> 
#> $title
#> [1] "Active development coordination cases"
#> 
#> $source_url
#> [1] "https://arcgis.tampagov.net/arcgis/rest/services/Planning/ActiveEntitlementLocations/FeatureServer/0"
#> 
#> $verified
#> [1] "2026-09-29"
#> 
#> $scope_note
#> [1] "This is an active application view, not a historical ledger of all development cases. TENTATIVEHEARING and TENTATIVETIME are strings, despite their date-like names. No explicit dataset license was found on this exact FeatureServer service or in the selected public ArcGIS organization catalog. Do not infer the license of a separate MapServer layer."
#> 
#> $terms
#> [1] "No explicit dataset license was found for this exact FeatureServer; its service iteminfo licenseInfo is empty. No license is inferred from a separate MapServer or the GeoHub site item. Source: https://arcgis.tampagov.net/arcgis/rest/services/Planning/ActiveEntitlementLocations/FeatureServer/info/iteminfo?f=pjson. City Conditions and Use says data is generally available to copy or distribute, subject to specified exceptions; it does not guarantee accuracy, completeness, or adequacy, and maps are reasonable representations rather than official records. Source: https://www.tampa.gov/about-us/tampagov/conditions-and-use."

case_schema <- tbod_schema("development-cases")
case_fields <- c("OBJECTID", "RECORDID", "APPSTATUS", "NEIGHBORHOOD",
                 "CREATEDDATE", "LASTUPDATE")
stopifnot(all(case_fields %in% case_schema$fields$name))
case_schema$fields[match(case_fields, case_schema$fields$name),
                   c("name", "type", "alias")]
#> # A tibble: 6 × 3
#>   name         type                alias             
#>   <chr>        <chr>               <chr>             
#> 1 OBJECTID     esriFieldTypeOID    OBJECTID          
#> 2 RECORDID     esriFieldTypeString Record ID         
#> 3 APPSTATUS    esriFieldTypeString Application Status
#> 4 NEIGHBORHOOD esriFieldTypeString Neighborhood      
#> 5 CREATEDDATE  esriFieldTypeDate   Created Date      
#> 6 LASTUPDATE   esriFieldTypeDate   Last Update

verified dates the catalog’s endpoint check, not the freshness of each record. The saved schema lists CREATEDDATE and LASTUPDATE as ArcGIS date fields. APPSTATUS and NEIGHBORHOOD are text fields; do not assume their values are standardized. If you need sources beyond these checked entries, use tbod_search_datasets("housing", source = "live") and inspect the discovery result’s completeness and source terms as described in the discovery article.

Retrieve and inspect current cases

Omitting limit requests the full matching layer. This matters for a neighborhood comparison: an arbitrary first page would not represent all mapped active cases. A three-minute operation budget bounds the example. tryCatch() adds context if the service is unavailable or its schema has changed; the code stops rather than making a chart from partial data.

cases <- tryCatch(
  tbod_get_dataset("development-cases", fields = case_fields,
                   total_timeout = 180),
  error = function(e) {
    stop("Could not retrieve Tampa active development cases: ",
         conditionMessage(e), call. = FALSE)
  }
)

case_source <- tbod_provenance(cases)
if (!isTRUE(case_source$complete) ||
    !isTRUE(case_source$matched_rows == nrow(cases))) {
  stop("The result is incomplete; do not interpret the chart.",
       call. = FALSE)
}
case_source[c("source_url", "retrieved_at", "matched_rows",
              "returned_rows", "integrity", "complete")]
utils::str(cases[, case_fields])
head(cases[, case_fields])

RECORDID is the source’s case identifier and OBJECTID identifies a mapped feature row. They may not have the same grain. The following diagnostics keep missing labels and repeated nonblank case IDs visible. We count mapped rows, not unique projects or new housing units.

Clean and summarize

Trim whitespace and turn blank source labels into missing values. ArcGIS date fields arrive as R date-time values; use the service’s advertised Eastern time zone when deriving a local calendar date. CREATEDDATE describes creation of a record in this active view, not a permit issue or housing completion date.

clean_label <- function(x) {
  out <- trimws(as.character(x))
  out[is.na(out) | !nzchar(out)] <- NA_character_
  out
}

case_data <- data.frame(
  case_id = clean_label(cases$RECORDID),
  status = clean_label(cases$APPSTATUS),
  neighborhood = clean_label(cases$NEIGHBORHOOD),
  created_date = as.Date(cases$CREATEDDATE, tz = "America/New_York"),
  stringsAsFactors = FALSE
)

nonblank_ids <- case_data$case_id[!is.na(case_data$case_id)]
quality <- data.frame(
  check = c("Mapped rows", "Missing case ID", "Repeated nonblank case ID",
            "Missing status", "Missing neighborhood", "Missing created date"),
  rows = c(nrow(case_data), sum(is.na(case_data$case_id)),
           sum(duplicated(nonblank_ids)), sum(is.na(case_data$status)),
           sum(is.na(case_data$neighborhood)),
           sum(is.na(case_data$created_date)))
)
quality

status_counts <- sort(table(case_data$status, useNA = "ifany"),
                      decreasing = TRUE)
status_counts

Do not automatically remove repeated case IDs: a case can have more than one mapped row, and dropping one could erase location information. Investigate those IDs and the source’s record definitions before claiming a count of distinct applications.

Chart the mapped neighborhoods

This chart uses the ten neighborhoods with the most mapped rows in the current active view. It excludes rows with no neighborhood label, whose count remains in quality. It is a count of source features, not a population-adjusted rate, a citywide total of development, or a measure of construction completed.

if (!requireNamespace("ggplot2", quietly = TRUE)) {
  stop("Install ggplot2 to draw the neighborhood chart.", call. = FALSE)
}

named_neighborhoods <- case_data$neighborhood[
  !is.na(case_data$neighborhood)
]
if (!length(named_neighborhoods)) {
  message("The retrieved records have no neighborhood labels to chart.")
} else {
  neighborhood_counts <- as.data.frame(table(named_neighborhoods),
                                       stringsAsFactors = FALSE)
  names(neighborhood_counts) <- c("neighborhood", "mapped_rows")
  neighborhood_counts <- neighborhood_counts[
    order(-neighborhood_counts$mapped_rows,
          neighborhood_counts$neighborhood), , drop = FALSE
  ]
  top_neighborhoods <- head(neighborhood_counts, 10)
  top_neighborhoods$neighborhood <- factor(
    top_neighborhoods$neighborhood,
    levels = rev(top_neighborhoods$neighborhood)
  )

  print(top_neighborhoods)
  neighborhood_plot <-
    ggplot2::ggplot(top_neighborhoods,
                    ggplot2::aes(x = neighborhood, y = mapped_rows)) +
      ggplot2::geom_col(fill = "#277DA1") +
      ggplot2::coord_flip() +
      ggplot2::labs(
        title = "Mapped active development cases by Tampa neighborhood",
        subtitle = paste("Top 10 labels; retrieved",
                         format(case_source$retrieved_at, "%Y-%m-%d", tz = "UTC")),
        x = NULL, y = "Mapped feature rows",
        caption = "Source: City of Tampa active development coordination cases"
      ) +
      ggplot2::theme_minimal(base_size = 12)
  print(neighborhood_plot)
}

The plotted bars describe the records available at retrieval time. Cases can enter or leave the active view, and some neighborhoods may have missing labels or different mapping practices. Larger counts do not prove more homes were built. A chart of CREATEDDATE among today’s active cases would also exclude earlier cases that have left the view, so it would not show a complete historical trend in applications.

A related housing source answers a different question

Pinellas County’s checked pinellas-assisted-housing layer reports fields such as CITY, HOUSINGPROGRAM, TOTALUNITS, and ASSISTEDUNITS. You can inspect its current records with the same retrieval and provenance checks, then decide whether your question concerns mapped program records or reported units. The code below is deliberately a source inspection, not a sum of units: multiple polygons or repeated parcel identifiers may represent the same site.

housing_info <- tbod_dataset_info("pinellas-assisted-housing")
housing_info[c("publisher", "source_url", "scope_note", "terms")]
#> $publisher
#> [1] "Pinellas County"
#> 
#> $source_url
#> [1] "https://pinellas-egis.maps.arcgis.com/home/item.html?id=2277d597855b4fba9e4c3e892bc5ddd7"
#> 
#> $scope_note
#> [1] "Program participation, assisted-unit counts, and subsidy expiration may change. This layer does not establish current eligibility, availability, or a complete affordable-housing inventory."
#> 
#> $terms
#> [1] "The official County item offers data as-is for reference, says it is not of survey precision, and does not name an unrestricted reuse license. Source: https://pinellas-egis.maps.arcgis.com/sharing/rest/content/items/2277d597855b4fba9e4c3e892bc5ddd7?f=pjson"

housing_fields <- c("OBJECTID", "PID", "DEVELOPMENT", "CITY",
                    "HOUSINGPROGRAM", "TOTALUNITS", "ASSISTEDUNITS")
housing_schema <- tbod_schema("pinellas-assisted-housing")
stopifnot(all(housing_fields %in% housing_schema$fields$name))
housing_schema$fields[match(housing_fields, housing_schema$fields$name),
                      c("name", "type", "alias")]
#> # A tibble: 7 × 3
#>   name           type                      alias           
#>   <chr>          <chr>                     <chr>           
#> 1 OBJECTID       esriFieldTypeOID          OBJECTID        
#> 2 PID            esriFieldTypeString       Parcel Number   
#> 3 DEVELOPMENT    esriFieldTypeString       Development Name
#> 4 CITY           esriFieldTypeString       City            
#> 5 HOUSINGPROGRAM esriFieldTypeString       Housing Program 
#> 6 TOTALUNITS     esriFieldTypeSmallInteger Total Units     
#> 7 ASSISTEDUNITS  esriFieldTypeSmallInteger Assisted Units

housing <- tryCatch(
  tbod_get_dataset("pinellas-assisted-housing",
                   fields = housing_fields, total_timeout = 180),
  error = function(e) {
    stop("Could not retrieve the Pinellas assisted-housing layer: ",
         conditionMessage(e), call. = FALSE)
  }
)
housing_source <- tbod_provenance(housing)
if (!isTRUE(housing_source$complete) ||
    !isTRUE(housing_source$matched_rows == nrow(housing))) {
  stop("The housing inventory result is incomplete.", call. = FALSE)
}
head(housing[, housing_fields])

Archived scatterplot of reported total and assisted units in mapped Pinellas housing records, with a diagonal marking equal counts.

Illustrative snapshot from the Pinellas County assisted-housing layer, retrieved October 6, 2026. Each point is a mapped record with usable unit counts, not necessarily a distinct housing site. This image is historical; run the live code above to inspect current records. Source: Pinellas County ArcGIS layer.

The separate Pinellas housing exploration checks unit-count pairs and repeated parcel IDs in more detail. That report uses live data and records the date of its observations; rerun it for a current analysis.

Reproducibility and limits

Keep case_source with any derived table or image; it records the endpoint, retrieval time, row counts, and completeness check. The checked schema and catalog are dated snapshots, while the worked neighborhood chart uses live City of Tampa records when run. The Pinellas chart above is a dated image from another analysis; no record-level source data is bundled with this tutorial.

The Tampa layer is an active application view rather than a historical ledger of all development cases. It does not establish approvals, housing starts, completed construction, or units delivered. The Pinellas layer is a program inventory; it does not establish current eligibility, vacancies, or a complete affordable-housing inventory. Neither layer covers all of its publisher’s data, and the package’s checked catalog does not measure complete publisher coverage. See Data Coverage for verification status and selection limits. Continue with water quality or infrastructure and capital projects for other source-specific analysis patterns.