Softening the blow of complex Geodata. Striving to ease up those spatial analysis we usually get stuck in! Alberto C blog on Data analysis
I have developed this NRT FLOOD RISK ASSESSMENT monitor in GEE that merges the power of RADAR and GHSL to detect impact over POPULATIONS under any weather conditions.
You can use geospatial intelligence to quickly transform satellite data into critical decisions during climate emergencies. GIS technology driving global resilience!
Hope you like it. If you need further explanation please dont hesitate to contact me.
Alberto C.
GIS Analyst
The USGS LiDAR Explorer, hosted via gishub.org, serves as a high-performance web gateway for interacting with the USGS 3D Elevation Program (3DEP) datasets. Developed by Qiusheng Wu, the platform leverages Cloud Optimized Point Clouds (COPC) and Entwine Point Tile (EPT) formats to enable seamless 3D visualization of massive LiDAR clouds directly in the browser without the need for local downloads.
By integrating data from Microsoft Planetary Computer and AWS Open Data, it provides an essential infrastructure for researchers and developers to search, visualize, and eventually pull high-resolution elevation data into R for sophisticated urban and environmental modeling.
Now we can start playing with LIDAR! No need to download anything, just save the names of your COPCs and you can put R to work right away.
First thing, go to this GITHUB repository https://github.com/opengeos/maplibre-gl-usgs-lidar, download code for the project (code>download ZIP), get connected with RStudio, save new project and open a script window… It’s all set up!
Cloud Optimized Point Clouds (COPC) represent a modernization of the standard LAZ format, specifically designed to handle massive LiDAR datasets within cloud-native environments. By reorganizing the data into a clustered octree structure, COPC allows software to stream only the specific portions or levels of detail needed for a task without downloading the entire file. This efficiency makes it the backbone of platforms like the USGS LiDAR Explorer, enabling high-speed 3D visualization and analysis directly from remote servers while maintaining full backward compatibility with traditional LAS/LAZ readers.
library(lidR)library(terra) # Necesaria para manejar el DSM como una imagen/superficie# Definimos la ruta exacta que proporcionasteruta_boston <- "C:/R/maplibre_lidar/maplibre-gl-usgs-lidar-main/lidar_sample/USGS_LPC_MA_Sndy_CMPG_2013_19TCG315920_LAS_2015.copc.laz"# Leemos el archivo (usamos un filtro para no saturar la RAM si es muy denso)las <- readLAS(ruta_boston, select = "xyzc")# Verificamos que se ha cargado correctamenteprint(las)-------------------# Generamos el DSM con una resolución de 1 metro# El algoritmo p2r(0.2) rellena huecos pequeños para que parezca sólidodsm <- rasterize_canopy(las, res = 1, algorithm = p2r(0.2))# Visualización rápida en 2D (Mapa de calor)plot(dsm, col = terrain.colors(50), main = "Modelo Digital de Superficie (DSM) - Boston")-------------# Convertimos el raster a un formato que RGL pueda renderizar como superficieplot_dtm3d(dsm, bg = "black")---------------------# Crear mapa de sombraspendiente <- terrain(dsm, v = "slope", unit = "radians")aspecto <- terrain(dsm, v = "aspect", unit = "radians")sombras <- shade(pendiente, aspecto, angle = 45, direction = 315)# Visualizar el DSM con sombrasplot(sombras, col = grey(0:100/100), legend = FALSE)plot(dsm, col = terrain.colors(50, alpha = 0.5), add = TRUE)-------------library(lidR)library(terra)# 1. Cargar el archivo completo (Ruta que confirmaste)ruta_boston <- "C:/R/maplibre_lidar/maplibre-gl-usgs-lidar-main/lidar_sample/USGS_LPC_MA_Sndy_CMPG_2013_19TCG315920_LAS_2015.copc.laz"las <- readLAS(ruta_boston, select = "xyzc")# 2. Elegimos una zona con edificios (puedes ajustar estos números tras ver el summary)# Estos valores suelen estar en el rango del summary(las)subset_las <- clip_rectangle(las, 331500, 4691500, 331700, 4691700)# 3. Generar el DSM (Superficie sólida) de alta resolución# Usamos res = 0.5 para que el zoom se vea nítidodsm_zoom <- rasterize_canopy(subset_las, res = 0.5, algorithm = p2r(0.3))--------------------library(lidR)library(terra)# 1. Cargar el archivo de Bostonruta_boston <- "C:/R/maplibre_lidar/maplibre-gl-usgs-lidar-main/lidar_sample/USGS_LPC_MA_Sndy_CMPG_2013_19TCG315920_LAS_2015.copc.laz"las <- readLAS(ruta_boston, select = "xyzc")# 2. Selección automática de la zona central (Cubo de 160m x 160m)x_mid <- mean(c(las@header@PHB$`Max X`, las@header@PHB$`Min X`))y_mid <- mean(c(las@header@PHB$`Max Y`, las@header@PHB$`Min Y`))subset_las <- clip_rectangle(las, x_mid - 80, y_mid - 80, x_mid + 80, y_mid + 80)# 3. Crear el DSM (Superficie continua)# El parámetro p2r(0.3) rellena los espacios vacíos entre rebotes del láserdsm_final <- rasterize_canopy(subset_las, res = 0.5, algorithm = p2r(0.3))# 4. Visualización 3D Sólidaplot_dtm3d(dsm_final, bg = "black", col = "lightblue")--------------install.packages("viridis")--------------------library(lidR)library(terra)library(viridis) # Ahora que ya está instalado# 1. Aseguramos que la superficie esté bien calculada# Usamos un radio de 0.4 para cerrar huecos sin crear "picos"dsm_raw <- rasterize_canopy(subset_las, res = 0.5, algorithm = p2r(0.4))# 2. Suavizado (El secreto de la visualización NEAT)# Aplicamos un filtro de media para que los techos se vean planos y lisosdsm_smooth <- focal(dsm_raw, w = matrix(1, 3, 3), fun = mean, na.rm = TRUE)# 3. Renderizado 3D Estilo Maqueta# 'border = NA' elimina las líneas negras que ensucian la imagen# 'col = viridis(256)' aplica el degradado profesionalplot_dtm3d(dsm_smooth, bg = "black", col = viridis(256), border = NA, lighting = TRUE)---------------- plot_dtm3d(dsm_smooth, bg = "white", col = "grey90", border = NA, lighting = TRUE)-----------------install.packages("magick")library(magick)-----------------library(rgl)# 1. Asegúrate de que la ventana de RGL esté abierta con tu modelo# 2. Definimos la función de giro (alrededor del eje Z)girar_camara <- spin3d(axis = c(0, 0, 1), rpm = 3) # 3 revoluciones por minuto# 3. Grabar la animación# Esto creará un archivo llamado "boston_360_02.gif" en tu carpeta de trabajomovie3d( f = girar_camara, duration = 20, # Duración en segundos dir = getwd(), # Se guarda donde tengas el proyecto movie = "boston_360_02", type = "gif", clean = TRUE # Borra los fotogramas sueltos al terminar)----------------------play3d(spin3d(axis = c(0, 0, 1), rpm = 10), duration = 10)---------------------movie3d( f = spin3d(axis = c(0, 0, 1), rpm = 3), duration = 10, dir = getwd(), movie = "boston_360_03", type = "gif", clean = TRUE)------------------- # Guarda la vista actual como una imagen PNG nítida snapshot3d("Boston_Maqueta_Pro.png", fmt = "png", width = 1200, height = 900)
Integrating LiDAR with Open Data in R transforms your POINTS into decisions by fusing i.e physical geometry (OSMs) with precise height (LIDARs), roof pitch, and volume of every structure in a neighborhood, which is essential for determining solar energy potential or tax assessments without manual surveys.
Beyond architecture, this workflow excels in environmental management when you link city-managed tree inventories with point cloud segments. By isolating individual tree crowns in the LiDAR data and joining them to open databases via their geographic coordinates, R can report on the specific health, biomass, and carbon capture of different species across the city. You can also derive micro-topography from a LiDAR ground model and intersect it with open hydrography or sewage network data to simulate precise flood pathways during extreme weather, predicting exactly which street corners will flood based on the actual height of the curbs and pavement.
It’s a lot what you can do integrating different technologies and different kind of data…
Do you want me to surf your LIDAR dataset and extract, classify, integrate, merge or challenge it with other data? Let’s talk. I’m free and easy:-) and the most important, looking for interesting projects, well, a job:-)
Alberto C.
Geodata analyst
Sources:
https://github.com/opengeos/maplibre-gl-usgs-lidar
https://usgs-lidar.gishub.org/
La IA generativa aplicada a mapas está transformando profundamente la forma en que entendemos, producimos y comunicamos la información geográfica. A diferencia de los enfoques clásicos, que se basan en reglas fijas o en clasificación supervisada, la IA generativa es capaz de crear nuevas representaciones espaciales a partir de patrones aprendidos en grandes volúmenes de datos geoespaciales.
En el ámbito cartográfico, estos modelos permiten generar mapas sintéticos, completar zonas con datos faltantes, aumentar la resolución espacial (super-resolución) o simular escenarios futuros, como la expansión urbana o el impacto del cambio climático. Modelos como GANs, diffusion models o transformers espaciales ya se están utilizando para generar imágenes satelitales realistas, mapas de uso del suelo plausibles o distribuciones probabilísticas de fenómenos territoriales.
Uno de los grandes valores de la IA generativa en mapas es su capacidad para integrar múltiples fuentes (satélite, censos, movilidad, clima) y producir salidas coherentes desde el punto de vista espacial. Esto resulta especialmente útil en regiones con escasez de datos, donde los mapas generados pueden servir como apoyo a la planificación o a la toma de decisiones.
Sin embargo, su uso plantea retos importantes: interpretabilidad, validación y sesgo espacial. Un mapa generado no es necesariamente un mapa verdadero, sino una hipótesis basada en datos previos. Por ello, la IA generativa debe entenderse como una herramienta de apoyo al análisis territorial, no como un sustituto del conocimiento geográfico ni del criterio experto.
En este contexto, el papel del geógrafo y del analista geoespacial es clave: evaluar, contextualizar y validar los mapas generados por IA para que su uso sea responsable y científicamente sólido.
Segun la página mappinggis, la IA generativa está cambiando las reglas del juego. Antes, las visualizaciones en la planificación urbana y paisajística se asociaban estrechamente con licencias costosas y largos tiempos de procesamiento. Hoy en día, una fotografía aérea y un buen prompt son suficientes para obtener un fascinante «mapa» generado automáticamente por IA.
Este enfoque muestra un potencial significativo para la visualización cartográfica automatizada. Si bien aún no está listo para producción, los resultados demuestran que la IA puede comprender y traducir imágenes aéreas a estilos de mapas reconocibles con una precisión razonable… y sin duda ofrece un anticipo de lo que nos espera en los próximos meses (¡no años!).
David Oesch
Ejemplo 1 en Benidorm, España.
Ejemplo 2 en Benidorm, España.
Ejemplo 3 en Benidorm, España.
Este es el complejo promt que he usado, el propietario es David Oesch (citado en fuentes más abajo):
[COMPLETE FRESH START]You are generating a completely new cartographic map. This is NOT an edit or modification of a previous image. Disregard all conversation history and prior image states.OBJECTIVE: Transform the provided satellite image into a precise Swiss national topographic mapTRANSFORMATION REQUIREMENT: Complete replacement of all satellite imagery with vector-style cartographic symbolism. Maintain scale, proportions, and preserve the layout and spatial relationships from the source satellite image.OUTPUT GUARANTEE: The result must contain ZERO visible satellite photograph elementsPHASE 1 - IMAGE ANALYSIS:Identify all geographic features in the satellite image (buildings, roads, water, vegetation, terrain)PHASE 2 - CARTOGRAPHIC TRANSFORMATION:Replace every satellite image element with exact Swiss topographic symbologyPHASE 3 - VERIFICATION:Confirm the output is a pure cartographic map with no satellite imagery, no photographic texture, no aerial photographyRENDERING SPECIFICATIONS:- Orthographic projection (flat, top-down view)- Exact RGB color values as specified- Maintain scale- Professional, clean cartographic presentation suitable for official swisstopo standards## Background- Base color: rgb(253, 253, 254) (off-white)- Apply subtle gray hillshading for terrain relief## Buildings and Structures- Standard buildings: Fill with rgb(170, 172, 174) (medium gray)- Building outlines: Dark gray rgb(154, 156, 158) with line width 0.5-2pt- Roofs and cooling towers: Lighter fill rgb(196, 198, 200) with outline rgb(180, 182, 184)- Construction platforms: Light gray rgb(222, 220, 220)- Solar panels: Pale yellow rgb(245, 246, 189) with brownish outline rgb(152, 142, 132)- Render as solid filled polygons with clean, precise edges## Roads and Transportation Networks**IMPORTANT: All roads rendered as clean, continuous centerlines without surface vehicles. Apply vehicle occlusion removal to show underlying road surface only.**Unified Road Classification (Simplified Homogeneous System):**- **Major roads** (motorways, highways, trunk roads, routes): - Fill: rgb(248, 207, 117) (golden yellow/beige) - unified color for all major routes - Casing: rgb(70, 55, 30) (dark brown) - Width: 3-11pt fill with 4-14pt casing (scale by importance, not by type) - Apply consistent styling across all motorway and trunk classifications- **Secondary roads** (all secondary and tertiary routes): - Fill: rgb(250, 243, 158) (pale yellow) - single unified color - Casing: rgb(65, 65, 25) (olive brown) - Width: 2-9pt fill with 3.5-11pt casing - Homogeneous representation regardless of route number- **Minor roads and local streets** (service roads, residential, tracks): - Fill: rgb(255, 255, 255) (white) - unified appearance - Casing: rgb(60, 60, 60) (dark gray) - Width: 1.5-8pt fill with 2.5-10pt casing - Consistent styling for all local access roads- **Footpaths and trails:** - Color: rgb(60, 60, 60) (dark gray) - Style: Dashed line pattern with 16-40pt dashes, 2-4pt gaps - Width: 0.75-2pt- **Mountain trails:** - Color: rgb(20, 20, 20) (nearly black) - Style: Dashed line - Width: 2-3.5pt- **Railways and transit:** - Color: rgb(203, 77, 77) (red-brown) - Width: 0.75-2pt solid lines- **Ferries:** - Color: rgb(77, 164, 218) (blue) - Style: Dashed lines (6-18pt dashes, 2-4pt gaps) - Width: 0.4-2.5pt**Road Surface Rendering:**- Remove all vehicles, cars, trucks, and moving objects from road surfaces using inpainting techniques- Reconstruct underlying pavement/surface texture where vehicles were present- Maintain road boundary precision and lane marking continuity- Show only static road infrastructure (pavement, markings, surfaces)## Water Bodies and Features- Remove all boats, ships in harbour or anchoring and moving objects from water surfaces using inpainting techniques- Lakes and oceans: - Fill: rgb(210, 238, 255) (light sky blue)- Rivers and streams: - Fill: rgb(181, 225, 253) (slightly darker blue)- Waterways (rivers, canals): - Line color: rgb(77, 164, 218) (medium blue) - Width: 0.25-10pt depending on water body size- Intermittent streams: - Line color: rgb(0, 0, 0) (black) - Opacity: 0.4 (40%) - Style: Dashed pattern (0.75-4pt dashes with gaps)- Water boundaries/shorelines: - Line color: rgb(77, 164, 218) (blue) - Width: 0.1-1.5pt - Blur: 0.25pt## Vegetation and Landcover- Forests and woodlands: - Primary: rgb(62, 168, 0) (vibrant green) - Alternative: rgb(62, 153, 10) (darker green) - Opacity: 0.1-0.25- Parks and green spaces: - Fill: rgb(211, 235, 199) (pale green) - Opacity: 0.25-0.35- Park boundaries: - Line color: rgb(112, 180, 70) (medium green) - Width: 1-4pt with 0.4pt blur- Residential/urban green: - Fill: rgb(246, 219, 164) transitioning to rgba(188, 186, 185, 0.4) (beige to gray)- Sports fields/pitches: - Fill: rgb(231, 243, 225) (very pale green)- Sand/beach areas: - Fill: rgb(239, 176, 87) (sandy orange)- Glaciers and ice: - Fill: rgb(205, 232, 244) (ice blue) - Opacity: 0.1-0.3## Terrain and Elevation- Hillshading (shadows): - Gray scale gradient from rgb(173, 188, 199) (darkest) to rgb(251, 252, 252) (lightest) - Apply based on luminosity values -15 to 0 - Opacity: 0.5-1.0- Sunny slopes: - Tint: rgb(255, 235, 5) (yellow) - Opacity: 0.04 (very subtle)- Contour lines: - Standard: rgba(180, 110, 13, 0.35) (brown, semi-transparent) - Width: 0.75-3pt (major contours thicker) - Blur: 0.25-0.4pt- Scree, barren and rock fields: - Apply stippled/textured patterns in gray tones - Opacity: 0.25-0.35## Special Features- Parking areas: - Fill: rgb(255, 255, 255) (white) - Outline: rgb(60, 60, 60) (dark gray) - **Remove parked vehicles** - show empty parking spaces only- Dams and weirs: - Fill: rgb(196, 198, 200) (light gray) - Outline: rgb(154, 156, 158) (medium gray)- Retaining walls: - Line: rgb(132, 132, 132) (medium gray) - Width: 0.5-3pt## Overall Technical Specifications- Line blur: 0.25-0.4pt for smooth appearance- Line cap: Round for roads, butt for boundaries- Line join: Round for curves, miter for sharp angles- Projection: Top-down orthographic (flat, no perspective)- Antialiasing: Smooth edges for all features except terrain fills- Layering order (bottom to top): Background → Hillshading → Water → Landcover → Roads → Buildings → Boundaries ## Color Accuracy Notes- Use exact RGB values provided (no approximations)- Maintain opacity/transparency as specified- Apply subtle blurring (0.25-0.4pt) to soften hard edges## Vehicle Removal Specifications- Detect and mask all vehicles (cars, trucks, buses, motorcycles, trains, boats, ships, airplanes) on roads, traintracks, airports and parking areas- Apply deep learning-based inpainting to reconstruct road surface beneath vehicles- Preserve road markings, lane boundaries, and surface texture continuity- Ensure seamless integration of reconstructed areas with surrounding pavement- Maintain edge fidelity and structural features of roads during vehicle removal**Output Requirements:** Vector-style cartographic map with Swiss precision, clean geometry, harmonious homogeneous road classification, vehicle-free representation, proper layering hierarchy, and professional presentation quality suitable for official topographic analysis.Luego, imaginando una situación en la que varios layers fueran necesarios, he tratado de integrar algunas capas como POIs o modelos 3D realistas como helicopteros, tiendas de campaña de emergencias, coches colapsados, etc
Lo que funciona bien
Precisión geométrica: La IA mantiene una alta precisión posicional, colocando correctamente las características geográficas. Reconocimiento de estilo: Fuerte fidelidad a las convenciones cartográficas (colores, símbolos, capas). Extracción de información: Buena detección de carreteras, árboles y edificios. Viabilidad de automatización: El flujo de trabajo es reproducible y puede procesar múltiples mosaicos sistemáticamente.
Limitaciones actuales
Desafíos de consistencia: Las interpretaciones generadas varían entre los mosaicos, lo que crea discontinuidades visuales. Procesamiento incompleto: no todos los elementos de imágenes aéreas se interpretan de manera consistente: vegetación y cuerpos de agua (pequeños estanques verdosos). Variaciones de estilo: la IA no siempre aplica el mismo estilo cartográfico en toda el área. Objetos transitorios: los automóviles y las características temporales no siempre se eliminan automáticamente.
Realmente, están pasando muchas cosas a todos los niveles relacionados con la IA, la visualuización, el GIS, los datos en general. Esto no es sino un intento de comprender desde dentro cómo puede estar afectando a la parte que me toca a mí.
Sources
https://gemini.google.com/
https://mappinggis.com/2025/10/generar-mapas-topograficos-automaticamente-con-ia/
https://github.com/davidoesch/ai-topographic-maps/blob/main/prompt.txt
Un registro constante: más de 11 años (desde Agosto 2014 hasta hoy) con más de 1,150 sesiones documentadas. Un proyecto vital; no son solo números, es la cronología de mi disciplina. Puedo decir de nuevo que R me ha roto mis esquemas de geógrafo de ArcGIS, de Global Mapper y QGIS, ahora no todo pasa por el filtro de tener coordenadas, por ejemplo estos insights no tienen coordenadas pero son analizables y se pueden tomar conclusiones que te permiten tomar decisiones rápidas… Echemos un vistazo a mis carreras los últimos años.
Esta década de kilómetros no es solo una base de datos; es una narrativa de superación :-). Para descifrarla, he usado el rigor estadístico de R, que me permite “limpiar” el ruido del esfuerzo diario mediante el uso de percentiles, transformando variables caóticas en tendencias de rendimiento.
El recorrido que he diseñado sigue una secuencia lógica de descubrimiento:
Antes de empezar, mi último 2025 que será por cierto el último de este tipo. Los años no pasan en balde y mi próximo proyecto running personal tendrá mucho que ver con el GIS (Python, R) pero eso os lo contaré otro día!
Empecé a darle vueltas a qué hacer y después de mucho tiempo (una semana, juasssss) me pareció que combinar mis carreras con mis análisis GIS podía ser una buena idea, sobre todo ahora que estoy moviendo mi portfolio para encontrar nuevas oportunidades laborales (trabajo, vamos).
El archivo csv contiene columnas clave como un ID, el trimestre, la fecha exacta en formato dd/mm/yyyy, la distancia total (en km), el ritmo (pace, en min/km), el esfuerzo (cociente entre distancia y ritmo), el esfuerzo que toma en cuenta el desnivel recorrido (effort_h) y la clasificación desde la primera hasta la última entrada.
Para empezar a darles valor a estos datos y ayudarte con este proyecto, lo primero es quedarse mirando el CSV durante al menos 10minutos, no hacer nada más… una vez (y solo entonces realizado este primera pasito) empezar con los demás 🙂
Empecemos a analizar, ¿han sido todos los años iguales? Lo primero que vemos es una constancia y una regularidad infinita, eso es bueno cuando hablamos de coherencia pero hoy no vamos a hablar de coherencia, vamos a hablar de comprensión, de tomar de perspectiva rápida, de toma de decisiones. En lugar de running puedes aplicarlo con casi cada tema. Paso a visualizar todos los años a la vez, todavía no veo mucho pero empiezo a comprender más de la base de datos…
Puntos con Presencia: Al subir el tamaño a 1.5, cada sesión individual ahora tiene peso visual, permitiéndote ver si la línea de tendencia realmente está representando bien la “nube” de ese año.
Claridad en la Leyenda (Label): He añadido el texto el “esfuerzo” (columna effort_h, que mide la relacion entre el tiempo, la distancia y los metros de desnivel positivo) en la parte superior de cada cuadro.
Orden Cronológico Vertical: Puedes ver año tras año cómo la “montaña” de los ritmos se mueve. Si el pico de 2025 está más a la derecha que el de 2014, hay progreso real.
Consistencia vs. Dispersión: Si una montaña es muy alta y estrecha, ese año fui un reloj (siempre al mismo ritmo). Si es baja y plana, fui más irregular.
El Color es el Esfuerzo: El granate oscuro marca visualmente los años donde “me exprimí” más, permitiéndome ver si ese esfuerzo se tradujo en ser más rápido (montaña más a la derecha).
Cero Solapamiento Sucio: Al estar una encima de otra pero ligeramente desplazadas, se puede ver todo sin que nada se tape.
Labels “Ghost”: Las distancias máximas (ej. 21.1k o 42.2k) aparecen en un gris suave (gray40) y con transparencia. Esto permite que el dato esté ahí si lo buscas, pero no ensucie la visión general de la montaña. Cronología Perfecta: El eje X corre de 2014 a 2025 sin saltos.
Referencia de Maratón: He añadido el hito de los 42.2k en el eje de distancias. Cada maratón, verás el “pico” o el label asomando por esa zona en el año correspondiente (arriba del todo están mis tres maratones, mi orgullo máximo, el 2017 en Valencia, el 2019 en Madrid y 2022 en Nantes).
Hitos de secuencia: marcados discretamente el “Inicio” y el “Actual” para dar contexto temporal a la evolución.
Nº de carreras (runs): Da el contexto del volumen. No es lo mismo un año con 100% de intensidad sobre 10 carreras que sobre 100.
ø (Media): Te da el valor exacto del esfuerzo medio anual (effort_h).
Leyenda Descriptiva: He titulado la leyenda de forma más técnica (“Nivel de Esfuerzo”) para que quede claro que los colores representan la variable effort_h.
Limpieza: El uso de theme_void() asegura que toda la atención se centre en la forma y el color de tus datos.
Al usar x = 0.5 en el comando annotate, los números de los años ahora actúan como una “cremallera” que separa el final y el principio de cada ciclo anual, dejando que los colores de todos los meses brillen.
Mapa de Intensidad Completo: La inclusión del Verde permite identificar tus ritmos “crucero” (aeróbicos), diferenciándolos claramente de los días de recuperación (Amarillo) y los de máxima exigencia (Rojo).
Visión 360°: El gráfico ahora parece un instrumento de precisión. Puedes ver perfectamente cómo en ciertos años “conquistaste” la zona roja y en otros te mantuviste en la zona verde/amarilla de base.
Aquí el código del último gráfico:
library(tidyverse)library(lubridate)# 1. CARGA Y FILTRADO (PERCENTILES 10-90)df_radar <- read_delim("run_r.csv", delim = ";", locale = locale(decimal_mark = ","), show_col_types = FALSE) %>% mutate( date = dmy(date), anio = year(date), mes = month(date, label = TRUE, abbr = TRUE), pace_decimal = case_when( ID == 181 ~ 5.516, str_detect(as.character(pace), ":") ~ { p <- str_split(as.character(pace), ":", simplify = TRUE) as.numeric(p[1]) + as.numeric(p[2])/60 }, TRUE ~ as.numeric(str_replace(as.character(pace), ",", ".")) ) )limites <- quantile(df_radar$pace_decimal, probs = c(0.10, 0.90), na.rm = TRUE)# 2. PROCESAMIENTO MENSUALdf_radar_f <- df_radar %>% filter(anio >= 2014, anio <= 2025) %>% group_by(anio, mes) %>% summarise( ritmo_medio = mean(pace_decimal), esfuerzo_total = sum(effort_h), .groups = "drop" )# 3. GRÁFICO CON LABELS ENTRE MESES Y TRICOLORggplot(df_radar_f, aes(x = mes, y = as.factor(anio), fill = ritmo_medio)) + # Celdas de ritmo geom_tile(color = "white", size = 0.2) + # Círculos de esfuerzo (blancos, con tamaño dinámico) geom_point(aes(size = esfuerzo_total), color = "white", alpha = 0.6) + coord_polar() + # ESCALA TRICOLOR: Rojo (Rápido) -> Verde -> Amarillo (Lento) scale_fill_gradientn( colors = c("#C0392B", "#27AE60", "#F1C40F"), # Rojo -> Verde -> Amarillo name = "Ritmo Medio", limits = c(limites[1], limites[2]), oob = scales::squish, labels = function(x) sprintf("%d:%02d", floor(x), round((x - floor(x)) * 60)) ) + scale_size_continuous(name = "Volumen Esfuerzo", range = c(0.5, 9)) + # LABELS DE AÑO: Situados en el borde entre meses (x = 0.5 es entre Dic y Ene) annotate("text", x = 0.5, y = as.factor(2014:2025), label = 2014:2025, color = "gray30", size = 2.8, fontface = "bold", hjust = 0.5) + labs( title = "EVOLUCIÓN DE RENDIMIENTO 2014-2025", subtitle = "Rojo: Velocidad | Verde: Aeróbico | Amarillo: Recuperación\nLos años se indican en la línea divisoria para no tapar los datos", x = "", y = "" ) + theme_minimal(base_size = 14) + theme( panel.grid.major = element_line(color = "gray94", linetype = "dotted"), axis.text.x = element_text(face = "bold", size = 12, color = "black"), axis.text.y = element_blank(), plot.title = element_text(face = "bold", size = 20, hjust = 0.5), plot.subtitle = element_text(hjust = 0.5, size = 11, color = "gray40"), legend.position = "right" )El análisis exhaustivo de esta década de entrenamiento muestra mi capacidad para sostener cargas de trabajo elevadas (y yo no lo sabía!), que ha evolucionado hacia una mayor inteligencia biológica o comprensíón del cuerto de uno mismo, permitiéndome identificar y explotar tus ventanas de máxima eficiencia con una precisión que los registros brutos no alcanzaban a mostrar. Esto es lo que buscaba!
Espero que os haya gustado. En breve más!
Alberto C.
(Geo)data Analyst
I am a geographer by training, and my professional career has always had a predominantly geospatial focus. Having recently completed a forty-hour course in R, using RStudio and GitHub, I feel that a whole new world of analysis has opened up before me. This work represents the meeting point between my basic geographical instinct and the technical capabilities of statistical programming. It is important to emphasise that I have invented this data and model entirely, so the results have no real meaning and contain inevitable biases. Their sole purpose is to learn and demonstrate the capabilities of this language. I believe that geographical knowledge and code are interdependent, as one without the other would not function successfully. It is precisely this symbiosis that I hope will make a difference in my current job search.
In terms of visual results, the analysis integrates layers of complexity that facilitate understanding of the territory. Using logarithmic scaling, we have managed to bring cities with very different data volumes together on the map, allowing us to identify small talent hubs that would normally be hidden by large capital cities. Furthermore, we do not simply place points on the map, but use colour gradients to link average age with location, making it easier to detect groups of young talent as opposed to more senior profiles. The inclusion of a histogram allows us to validate the general distribution of the data in relation to its geographical dispersion. This exercise demonstrates that the combination of territorial analysis and programming can transform data into a clear and strategic visual narrative.
Let us imagine an HR company that seeks to understand when its clients, with certain educational backgrounds and occupations, are most likely to decide to move to another city. In other words, is the profile of those willing to move mostly young or older? Urban or rural? With low or high levels of education? Where are they concentrated?
The database is a CSV file with only nine fields, thoroughly cleaned up so we can start playing around with the tidyverse library.
Once the project has been set up, which can actually be one of the most complex parts (directories, files with appropriate separators, overall consistency of information, etc.), we start to get our hands on the code (please don’t look too closely, I’m just a humble geographer… and blond!). Below, I begin to understand the basics and create a histogram of the number of people broken down by age.
But, what is an analysis without a map to overlay it?? 🙂
Inserting a map, adding labels, playing with sizes, colors, alphas, shapes… I have a lot to thank to my “geography visualization” teachers at the UVA in Valladolid!
A little detour, let’s focus on Andalucía…
Here comes the fun. Adding colors to disaggreagate profiles and size to rapidly get to understand where is the bigger amount of people willing to move…
¿Do I need to disaggregate by bigger regions (CCAA in Spain)?
Please don’t blame me for the incoherence… this is FULLY RANDOM!!!! 🙂 Do we understand if we use a color fade for average age?
Should we try a violin-like diagram showing that I don’t just make pretty maps or graphs, but that you establish aKPI? Any HR recruiter can see which groups are ‘aged’ or which are the ‘youngest’ in relation to the total workforce. A Senior Geodata Analyst should know how to interpret the social reality behind the data 🙂
Or even better, a disaggregation and highlight of MAX-MIN cases per level of education.
And the final result today (so far): the main cities, the age factor, the average candidate profile. Everything has an explanation:-)
Urban Concentration: The map shows that willingness to relocate is not uniform; it is concentrated heavily in Madrid, Barcelona, Seville, and Valencia. The larger circles in these areas confirm that they are the main ‘talent engines’.
The Age Factor: The trend line and histogram reveal a clear negative correlation: the younger the age, the greater the willingness. Younger talent (light/yellow colours) is the most flexible, while from the age of 45-50 (dark colours), the intention to move falls dramatically.
Geographical Balance: Thanks to logarithmic scaling, we see that although capital cities dominate, there is a constant flow of profiles in medium-sized cities, suggesting that talent is distributed but needs incentives depending on the stage of life.
Candidate Profile: The histogram confirms that the bulk of those interested are between 25 and 38 years old. Outside this range, mobility becomes exceptional.
Borders: The visualisation by autonomous community allows us to identify that regions such as Andalusia and Catalonia have a network of secondary cities with high mobility, unlike other regions where everything is concentrated in a single point.
In conclusion: the profile of those willing to relocate is mostly young, urban and highly educated, concentrated in the country’s major economic hubs.
I hope you enjoyed it. If you can think of any other scenarios where we don’t have to make up the data, I’ll give it some thought and write another post!
Alberto C.
Geospatial analyst and someone who is looking for a job. ¿Do you have one?
Urban Atlas: Precision Geoespacial en el Corredor de Copernicus. Usos del Suelo combinados con estimaciones de población sobre cada una de las clases, un paso más en combinación de fuentes de Datos Abiertos en la nube.
Este análisis representa un pequeño test rápido desarrollado por Alberto C (Geovisualization.net) para mostrar las potencialidades de uso de un asset externo en la plataforma Google Earth Engine (GEE) mediante JavaScript. Se trata de un mapa de usos del suelo (LULC / Clutter) de media-alta resolución que ofrece una precisión temática y espacial muy precisa (significativamente superior a la de Corine Land Cover) a lo que añadimos una estimación de población con una base de datos transnacional como WOLDPOP 100m y otra en paralelo GHSL 100m. Este flujo de trabajo, que integra capas externas con datasets globales de computación en la nube, se ha ejecutado íntegramente en unos pocos minutos, demostrando la agilidad operativa de las herramientas cloud actuales.
Urban Atlas (UA) representa el estándar de oro dentro del Copernicus Land Monitoring Service (CLMS) para el análisis de la morfología urbana en Europa. A diferencia de Corine Land Cover, UA ofrece una resolución temática y espacial drásticamente superior (Unidad Mínima de Mapeo de 0.25 ha para clases urbanas), permitiendo discriminar entre tejidos urbanos continuos y discontinuos con una precisión de densidad del 10% al 80%.
En la actualidad, el Urban Atlas se ha consolidado como la capa base para modelos críticos:
La verdadera potencia del Urban Atlas se libera al integrarse en motores de Cloud Computing como GEE. Pasar de un análisis local por municipio a un análisis continental es ahora una cuestión de código, no de capacidad de hardware.
Ventajas de la automatización en GEE:
reduceRegions, se pueden extraer perfiles de uso de suelo para miles de ciudades en segundos, cruzándolos con datos de población..remap() instantáneo permite simplificar las 27 clases originales de UA en indicadores binarios (Gris vs. Verde) para generar histogramas de resiliencia en tiempo real.Ejemplo de flujo lógico en GEE:
JavaScript
// Agregación de clases para análisis de infraestructura verde
var greenSpace = ua_image.remap([14100, 14200, 31000], [1, 1, 1], 0);
var stats = greenSpace.reduceRegion({
reducer: ee.Reducer.mean(),
geometry: region_interes,
scale: 10
});
El siguiente paso que estamos viendo en la industria es el uso de Urban Atlas como Ground Truth (verdad terreno) para entrenar redes neuronales convolucionales (CNN) sobre imágenes de muy alta resolución (VHR), permitiendo actualizar los mapas de uso de suelo de forma continua sin esperar a los ciclos de actualización trienales de Copernicus.
Las estimaciones de población se obtuvieron mediante la intersección espacial de los datos de población en cuadrículas de WorldPop 2020 con clases categóricas de uso del suelo y la agregación de los recuentos de población por clase.
Cambiamos fácilmente la leyenda puesto que necesitamos unos colores más más adecuados
Las clases de uso del suelo se obtuvieron del Atlas Urbano Copernicus y se agruparon en categorías temáticas siguiendo la nomenclatura oficial del Atlas Urbano. El scrip de GEE saca directamente esta tabla en formato CSV.
Si bien no hay correspondencia con la población real del distrito esto es porque por un lado tenemos una fuente vectorial de media-alta resolución (urban atlas) mientras que los datos de población vienen de una fuente continua de 100m (100 veces menor). Este análisis advierte de las limitaciones del estudio mientras que se enfoca en las potencialidades de uso de fuentes en la nube que con toda lógica, deben de hacerse coincidir en aras de una completa coherencia.
Si te interesa el tema, pídeme el ASSET de URBAN Atlas (lo puedes ver en las fuentes abajo del post) o el ASSET de población sobre el AOI para que puedas importarlo en tu workspace o si no quieres replicarlo simplemente dime qué te parece este enfoque!
Un saludo!
Alberto C.
Geodata Analyst
Sources:
https://code.earthengine.google.com/afe5dbf9b75c53dda2b82e4cad6d0b4e
https://land.copernicus.eu/en/products/urban-atlas/urban-atlas-2018#download
https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop?hl=es-419
https://human-settlement.emergency.copernicus.eu/
https://hub.worldpop.org/project/categories?id=3
https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_V
Theoretical framework: This GIS study applies Geographically Weighted Regression (GWR) to investigate the spatial relationship between Purchasing Power Index (PPI) and the distribution of gambling-related retail establishments within the city of Madrid. My aim is to account for spatially varying relationships driven by local urban contexts, under the assumption that the relationship between socioeconomic conditions and the presence of gambling venues varies across urban space and his socioeconomic patterns. My hypothesis is that this socioeconomic conditions of the urban fabric I mention, can be a breeding ground for the location of Betting Shops/Gambling Stores (AKA bookmaker in UK or bookie in the US), or in other words, I am attempting to Detect Urban Vulnerability to Gambling Harm.
I have chosen this Purchasing Power Index (PPI) as it’s a standardized socioeconomic indicator that represents the relative capacity of households to spend and consume goods and services within a given geographic area. It typically integrates information on income levels, employment, and demographic structure, and is expressed as a relative measure rather than an absolute monetary value, allowing comparisons across spatial units such as census sections. The logic says: The higher the PPI, the lower the vulnerability (where ) thus the more likely to find Gambling Stores around.
A potential relationship between purchasing power and the location of Gambling Stores arises from commercial location strategies and socioeconomic vulnerability dynamics. Gambling operators may preferentially locate in areas where household purchasing power and consumption patterns maximize demand, or conversely in areas with lower purchasing power where gambling expenditure may function as a substitute consumption behavior. As a result, the spatial distribution of gambling establishments may reflect underlying socioeconomic gradients within the urban fabric.
From a spatial analysis perspective, traditional global regression models (e.g., Ordinary Least Squares) impose a single, spatially invariant relationship across the entire study area. However, urban socioeconomic processes are inherently heterogeneous, especially in large metropolitan areas such as Madrid, where neighborhood-level dynamics, urban morphology, and socioeconomic gradients differ significantly between districts. GWR is therefore selected as the most appropriate method to capture local variations in model coefficients and to provide geographically explicit insights.
Sources: The analysis integrates two primary datasets, both harmonized at the census section level (sección censal), which represents the finest administrative unit for socioeconomic statistics in Spain:
The census section is adopted as the spatial unit of analysis to ensure statistical consistency between datasets and to align with official demographic and economic reporting standards.
I used ArcMap 10.6.1 Spatial Statistics Tool /Modelling Spatial Relationships /Geographically Weighted Regression. Also visualized and geoprocessed in Global Mapper 26.2.
Geographically Weighted Regression is a local regression technique that extends classical linear regression by allowing model parameters to vary spatially. Instead of estimating a single global coefficient, GWR computes a separate regression equation for each spatial unit, calibrated using nearby observations weighted by their geographic proximity.
I would like to mention that, at this point, having fed the tool (using ArcMap 10.6.1) with the necessary inputs (dependent and explanatory variables), I am noting down the results and adding them to the article in order to systematize my understanding (and perhaps that of some of you), but my explanatory capacity is still limited. I continue thou.
Formally, the GWR model can be expressed as:
where:
This formulation allows the strength and direction of the relationship between purchasing power and gambling store presence to vary across Madrid.
And these were the results/output
OBJECT_ID / VARNAME / VARIABLE / DEFINITION
1 / Bandwidth / 6563,230379
2 / Residual Squares / 513,203175
3 / Effective Number / 12,898945
4 / Sigma / 0,45955
5 / AICc / 3141,813059
6 / R2 / 0,016602
7 / R2 Adjusted / 0,011787
8 / Dependent Field / 0 / Join_Count (Amount of Gambling Stores in the same Census Section)
9 / Explanatory Field / 1 / PPIDX_CY (Purchase Power Index)
The geographically weighted regression model shows a very low explanatory power (adjusted R² = 0,011787), indicating that purchasing power alone does not meaningfully explain the spatial distribution of betting shops, even when allowing for spatially varying relationships.
A critical component of GWR is the definition of the spatial weighting scheme, which determines how neighboring census sections influence each local regression. In this analysis:
This adaptive approach ensures that densely populated urban areas benefit from a more localized calibration, while peripheral areas incorporate information from a broader neighborhood when necessary.
The GWR model produces several spatially explicit outputs of analytical relevance:
Rather than seeking a single city-wide conclusion, the emphasis is placed on geographic patterns, such as clusters of census sections where lower purchasing power is more strongly associated with higher concentrations of gambling establishments, or conversely, areas where this relationship is weak or absent.
By adopting a geographically weighted approach, this analysis explicitly acknowledges that urban socioeconomic processes are spatially contingent. The resulting maps of local coefficients provide actionable insights for urban policy, public health, and regulatory frameworks, allowing stakeholders to identify areas where gambling availability may be more closely linked to socioeconomic vulnerability.
From the standpoint of a spatial analyst, GWR serves not only as a statistical tool but as an exploratory framework that integrates spatial thinking directly into the modeling process. It enables an interpretation of the urban landscape of Madrid, grounded in official data sources and aligned with best practices in spatial econometric analysis.
Despite the analytical advantages of Geographically Weighted Regression in capturing spatial heterogeneity, several ***assumptions and limitations**** must be acknowledged to ensure a transparent and rigorous interpretation of the results.
The results of this study should be interpreted as associational rather than causal. GWR identifies spatially varying relationships between purchasing power and gambling store distribution, but it does not establish causal directionality. The observed patterns may reflect a combination of regulatory frameworks, commercial location strategies, historical land-use patterns, and unobserved socioeconomic factors.
Moreover, areas exhibiting strong local relationships should not be automatically interpreted as zones of direct vulnerability without complementary qualitative, behavioral, or health-related data. Gambling harm is a multidimensional phenomenon, and the presence of gambling establishments constitutes only one potential exposure factor.
Finally, this analysis focuses exclusively on the relationship between purchasing power and the spatial distribution of Gambling Stores. Other relevant dimensions of urban vulnerability—such as age structure, unemployment, educational attainment, or proximity to schools—are not explicitly modeled. As such, the results should be viewed as a partial and exploratory assessment of urban vulnerability, intended to inform further multivariate and multi-scalar analyses rather than provide a definitive evaluation
Anyway, my aim writing this post is start understanding this potentional multivariable correlation and this is only the first step. Hope you are still reading this and come back every now and then to keep reading 🙂
Sources:
https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=66665cde99be2410VgnVCM1000000b205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD
https://livingatlas.arcgis.com/en/home/
https://www.txalaparta.eus/es/libros/ludomorfina?srsltid=AfmBOop4ZFCax6eeJt223I94jsH4iLDEKRtJT6Xlrhip7WuS7agtOowR
https://pro.arcgis.com/en/pro-app/3.4/tool-reference/spatial-statistics/how-geographicallyweightedregression-works.htm
https://www.publichealth.columbia.edu/research/population-health-methods/geographically-weighted-regression
https://www.sciencedirect.com/topics/earth-and-planetary-sciences/geographically-weighted-regression
https://carto.com/blog/how-geographically-weighted-regression-works
Alberto C.
GIS Analyst and ex-freelancer
The exercise shows how a simple SketchUp 3D volume, defined solely by its basic geometry, can be transformed into a complex architectural proposal. Starting from the initial schematic model, the system interprets proportions, levels, and shapes, and converts them into a fully developed building, complete with textures, vegetation, lighting, and an urban context.
It is a good test to evaluate the extent to which AI understands spatial structure and is capable of generating a coherent and rich interpretation from a minimal geometric skeleton.
Adding a bit of perspective to this post, myself I have always loved 3D and I have worked over it in the past using it from different approaches, starting from architectural modelling to massive QC of LOD1 buildings anywhere in the world. That’s why I see these Gemini features as something kind on interesting and related to my world.
Some links related to 3D available in Geovisualization.net thus created by me in the past :-):
https://geovisualization.net/tag/3d/
https://geovisualization.net/2010/06/12/gisarchitechtural-scenaries-awesome/
https://geovisualization.net/2009/08/11/vicalvaro-buildings-3d-madrid-spain/
https://geovisualization.net/2014/05/02/rsme-comparing-lidar-data-with-a-third-partys-3d-dataset/
https://geovisualization.net/2013/04/04/solar-shadows-analysis-on-rue-massillon-nantes-france/
https://geovisualization.net/2013/04/03/shadow-analysis-en-puerta-de-arganda-madrid/
https://geovisualization.net/2010/02/18/ever-tried-google-earth-3d-building-maker/
https://geovisualization.net/2009/08/15/one-more-3d-building-avenida-de-la-vega-28108-restaurante-asiatico-sheng-madrid-spain/
Let me know what do you think about it!
Alberto C.
Geodata Analyst, ex 3D designer and always a curious person 🙂
Dont get wrong if you see the IA background showing our handsome major showing his beautiful smile in Cibeles/Correos, it’s only to get your precious attention (only if you need it thou!). Flooding in urban environments is not a speculative hazard but something we can quantify. In the case of Madrid, the intersection of pretty mountainous terrain and urban expansion presents a scenario of significant risk, particularly when analyzed through the lens of shared high-resolution geospatial data (it might surprise you there are 2000m difference between the highest spot in Madrid province, Pico Peñalara -2428m- and the Alberche river environment in some areas -430m-).
This study integrates the buildings from BTN (Base Topográfica Nacional) provided by the Spanish “IGN”, the CNIG with the official flood hazard maps for a 100-year return period (T=100), published by the Ministry for the Ecological Transition and the Demographic Challenge (MITECO). The T=100 scenario is the most representative for evaluating long-term flood exposure, as it reflects events with a 1% annual probability—rare but not improbable, and certainly not negligible.
If we measure preliminarily, only 0.999% (8097/810134) of all buildings (available in our latest BTN buildings provided by CNIG) would be affected but but if we go deeper, this means a lot in specific spots: Aranjuez for instance would be very affected by the Tajo river flood.
The spatial overlay of flood-prone zones with demographic and land-use data reveals a concerning concentration of residential population within areas designated as ARPSIs (Áreas de Riesgo Potencial Significativo de Inundación). These zones include dense urban districts along the Manzanares River and low-lying areas in Arganzuela, Usera, and parts of Puente de Vallecas.
While precise population figures vary depending on the granularity of census data, preliminary estimates suggest that tens of thousands of residents could be directly affected by a flood event of this magnitude. The implications extend beyond displacement and property damage, encompassing public health risks, disruption of essential services, and long-term socioeconomic instability.
Economic activities within these flood zones are diverse and structurally significant. Central districts host a high concentration of retail, hospitality, and cultural institutions, while peripheral zones near the M-30 corridor accommodate logistics, warehousing, and industrial operations. The exposure of these sectors to flood risk implies not only direct financial losses but also cascading effects on employment, supply chains, and urban mobility. Moreover, the presence of public infrastructure—transport nodes, administrative buildings, and emergency services—within these vulnerable areas raises questions about the resilience of the city’s operational backbone.
*Retail and hospitality in central districts (e.g., Lavapiés, La Latina)
*Logistics and warehousing near the M-30 corridor
*Cultural and tourism assets, including museums and heritage sites
*Public infrastructure, such as metro stations, bus depots, and administrative buildings
Disruption in these zones could result in multi-million euro losses, not only from direct damage but also from prolonged service interruptions.
I have uploaded a small sample over Aranjuez to my GEE interface (is not that I am from this place, I only find it representative!). You can quickly tune the JavaScript code to be able to count the amount of buildings affected, the area of all of them and the DTM range of the flood DTM REM (Relative Elevation Model) over your actual screen.
https://code.earthengine.google.com/1d7e907283e33dc4b468ab3adf578840
Of particular concern is the identification of facilities regulated under Annex I of Directive 96/61/EC, which governs integrated pollution prevention and control.
*Fuel storage and distribution centers
*Waste treatment plants
*Industrial facilities with hazardous materials
These include fuel depots, waste treatment centers, and industrial sites handling hazardous substances. In the event of flooding, such facilities pose a risk of accidental contamination, with potential impacts on protected zones defined in Annex IV of Directive 2000/60/EC. These zones include drinking water abstraction points, Natura 2000 habitats, and recreational waters. The spatial proximity of these sensitive areas to flood-prone industrial sites underscores the need for integrated risk assessment that goes beyond hydrological modeling and incorporates environmental and public health dimensions.
(i) Drinking water abstraction points
(iii) Habitats designated under Natura 2000
(v) Recreational waters and sensitive ecosystems
The visual simulations accompanying this analysis are intentionally exaggerated. They do not represent predictive models but serve as heuristic devices to provoke reflection and debate. By depicting iconic Madrid landmarks submerged under chaotic floodwaters, these images challenge the viewer to confront the consequences of urban planning decisions that disregard hydrological constraints. They are not intended to alarm, but to illustrate the scale of disruption that could result from a statistically plausible event.
Please note these features have been taken out of the scope of this preliminary approach for analysis Flood in Madrid province using Open Data.
Again, this study/first approach would not have been possible without access to open geospatial data. The availability of national datasets such as the Base Topográfica Nacional and flood hazard maps from MITECO exemplifies the transformative potential of public data infrastructures. However, the mere existence of data is insufficient. What is required is a culture of proactive use—by planners, policymakers, and civil society—where risk is treated not as an abstract probability but as a concrete design constraint.
The presence of vulnerable populations, critical infrastructure, and environmentally sensitive facilities within flood-prone zones constitutes a non-assumable risk. It is a risk that could be mitigated through better zoning, stricter regulation, and investment in adaptive infrastructure. The cost of inaction is not only economic but ethical. Avoidable losses—whether of property, livelihoods, or ecosystems—are a failure of foresight, not fate.
For full maps, methodology, and downloadable layers, visit geovisualization.net.. Flood Risk Maps T=100 (MITECO): https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/agua/riesgo-inundacion-fluvial-t100.html
Sources:
https://centrodedescargas.cnig.es/CentroDescargas/listaFicheros
https://www.miteco.gob.es/es/agua/temas/gestion-de-los-riesgos-de-inundacion/snczi.html
Software used: ArcGIS Pro 3.6 + Global Mapper 26.2 + Photoshop X + Copilot (IA cover) (+GEE)
It is a fact that a betting shop (AKA bookmaker in UK or bookie in the US) should not be close to a secondary school. Its obvious the impact on population ranging something like 12-18 could be higher than in other age thresholds. How close? 100m? 300m? 500m? Euclidean distance (a straight line) or following the street network?. In any case, if I decide choosing for instance a range of 500m, for example, 81% of betting shops in Madrid have secondary schools within that distance (258 out of 316). Looking at it from the secondary schools’ point of view, almost 60% of secondary schools have betting shops within 500m (171/291). This is undoubtedly an issue that needs to be addressed.
The spatial analysis reveals a moderately negative correlation (r = -0.39) between the distance to the nearest betting shop and the number of betting shops within a 500-meter radius of each secondary school. This means that, on average, the shorter the distance between a school and the nearest betting house, the greater the number of betting houses found within a short walking distance (for example, within 500 meters). In other words, schools that are close to one betting house are very often close to several. Conversely, schools located farther from any betting house tend to be in areas where these establishments are much less common or even absent. This pattern does not imply a perfect one-to-one relationship — some exceptions exist — but the overall trend is clear: betting houses are not randomly distributed in the city. Instead, they tend to form clusters, and those clusters often appear in the same parts of the city where many schools are located. Although the relationship is not perfectly linear, it might indicate a potential spatial association between the two types of locations.
From the heat map, this relationship becomes more tangible. 171 out of 291 secondary schools in Madrid — approximately 60% of the total — are situated within 500 meters of at least one betting shop. The density surface highlights four clear hotspot zones: Usera, Carabanchel, Centro, and Tetuán. These districts concentrate the majority of the spatial overlap between High Schools and betting shops , forming well-defined high-intensity clusters. The pattern aligns with socio-economic and demographic realities: these are traditionally dense urban districts with younger populations and, in several cases, lower average income levels, which have historically attracted a higher concentration of gambling venues.
By contrast, the richest districts — such as Salamanca, Retiro or Chamberí — show a more dispersed and lower-intensity pattern. This uneven distribution reinforces the idea that the proximity of betting shops to High School educational institutions is not random, but rather follows a spatial logic influenced by the urban and social fabric of the city.
In practical terms, the combination of a negative correlation and the hotspot clustering suggests that High Schools and betting shops in Madrid exhibit a clear spatial association. The proximity of many High Schools to gambling establishments may have implications for urban planning, youth protection policies, and spatial regulation of gambling activities. The results provide a quantitative foundation for further policy discussions on how the city’s gambling landscape interacts with its educational network.
Methodology: Open Data Madrid provided ‘Censo de locales, sus actividades y terrazas de hostelería y restauración. Histórico’ which I cleaned and cross-referenced to obtain the venues dedicated to gambling and having an active licence (2020). Then I downloaded Madrid High Schools (those offering public and private secondary education). Based on Madrid districts I was able to measure distances and highlight the most affected areas. I used GEE to performed the Pearson Correlation (you have the code below). Maps where created using Global Mapper v26.2.
What if I can get a GeoJSON just by feeding the system with the required threshold? (i.e this code returns the 171 secondary schools in Madrid which have betting houses closer than a 500m radius from them). It’s crazy 171/291!! (almost 60%)?:
https://code.earthengine.google.com/e15e0ee2c0269d8b3e4a199d444620d3
TRY IT YOURSELF!!!
What if I can get a GeoJSON just by asking the minimum distance to each other? (i.e this code returns the 98 bookies (betting shops) which are closer than 100m to other bookie?:
https://code.earthengine.google.com/662af463a6c4a4b3eca9d9dbcb6fd5fc
TRY IT YOURSELF!!!
Additional considerations:
The regulations governing the distance between betting shops and educational establishments such as secondary schools vary depending on the autonomous community. Some communities have implemented or proposed minimum distances, such as a 300-metre radius in some regional laws, while others continue to apply shorter distances or are in the process of reviewing their regulations. It is essential to consult the specific legislation of each community to find out the exact regulations and how they are applied. Examples of regulations by autonomous community (CCAA):
*Community of Madrid: Established a minimum distance of 100 metres between gaming halls and schools/secondary schools in 2019. Although extensions were proposed, the current regulation remains at 100 metres, with the possibility of some existing premises being exempt, according to El Mundo.
*Galicia: Its gaming law establishes a minimum distance of 300 metres from educational centres for new gaming establishments, increasing the previous linear distance of 150 metres, according to GaliciaPress.
*Andalusia: The proposal to extend the distance to 500 metres has been taken to Parliament, although the current regulation may be different, as reported in ABC.
*Other regions: Some cities, such as Talavera de la Reina in Castilla-La Mancha, have included a minimum distance of 300 metres, backed by court rulings, according to different sources.
Distance can be measured in a straight line or in radius, and this difference is important when applying the regulations.
Premises that were already in operation before the regulations were implemented in some regions are often exempt.
In the case of Madrid, proposals to ban betting shops from being located less than 500 metres from educational establishments have even been analysed, as reported by Telemadrid.
Sources:
https://gestiona.comunidad.madrid/wleg_pub/secure/normativas/contenidoNormativa.jsf?opcion=VerHtml&nmnorma=12618&eli=true#no-back-button
https://www.ordenacionjuego.es/
https://www.telemadrid.es/programas/cronicas-subterraneas/investigaciones/apuestas/programa-completo-2-2072812740–20181202113000.html
https://code.earthengine.google.com/3c977b75e9f966a7c0ec3a8dd96588c6?noload=true
Alberto C.
Geodata Analyst
Geovisualization.net 