Use Case #9 — Validate Latitude and Longitude

Business Purpose

Propósito de negocio

Identify missing, impossible, and out-of-area coordinates for operational mapping.

Este caso continúa el flujo real de trabajo con solicitudes de servicios municipales y documenta cada transformación de forma trazable.

Input and Output

Entrada y salida

Results can vary because the source dataset was generated synthetically and randomly. Run the code to obtain the exact values from your local file.

Los resultados pueden variar porque el dataset de origen fue generado de forma sintética y aleatoria. Ejecuta el código para obtener los valores exactos de tu archivo local.

Step 1 — Load coordinate fields

import pandas as pd
from pathlib import Path

base = Path.home() / "Documents" / "Pandas_Use_Cases"
input_file = base / "contact_fields_validated.csv"
output_file = base / "coordinates_validated.csv"

df = pd.read_csv(input_file)

print(df[["Latitude", "Longitude"]].describe().to_string())

Descriptive statistics expose zeros, extreme values, and unusual ranges.

Step 2 — Validate global coordinate ranges

df["Valid_Global_Coordinates"] = (
    df["Latitude"].between(-90, 90)
    & df["Longitude"].between(-180, 180)
)

print(df["Valid_Global_Coordinates"].value_counts())

Impossible global coordinates are flagged immediately.

Step 3 — Validate a local service-area box

# Approximate South Florida practice boundary.
df["Inside_Service_Area"] = (
    df["Latitude"].between(25.0, 26.5)
    & df["Longitude"].between(-81.0, -79.5)
)

print(df["Inside_Service_Area"].value_counts())

Globally valid coordinates may still fall outside the expected municipal area.

Step 4 — Identify zero and missing coordinate pairs

df["Missing_Coordinates"] = (
    df["Latitude"].isna() | df["Longitude"].isna()
)

df["Zero_Coordinates"] = (
    df["Latitude"].eq(0) | df["Longitude"].eq(0)
)

print(df[["Missing_Coordinates", "Zero_Coordinates"]].sum())

Missing coordinates and placeholder zeros are measured separately.

Step 5 — Export coordinate validation

df.to_csv(output_file, index=False, encoding="utf-8-sig")
print(f"Saved: {output_file}")

Mapping-ready quality flags are added without deleting source coordinates.

Use Case #9 Completed

Caso de uso #9 completado

The output of this module becomes the controlled input for the next stage of the 20-case Pandas workflow.

La salida de este módulo se convierte en la entrada controlada para la siguiente etapa del flujo de 20 casos de Pandas.