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. My hypothesis is that the socioeconomic conditions of the urban fabric can be a breeding ground for the location of betting shops, or in other words, I am attempting to Detect Urban Vulnerability to Gambling Harm.
Category Archives: IA
Testing GEMINI for 3D environments. From SketchUp to an unlikely future!
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
Mapping Something Unthinkable: Flood Risk in Madrid using Open Data
Dont get wrong if you see the IA background showing our handsome major almost showing his beautiful smile in Cibeles/Correos it’s only to get your attentions (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 (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-) and urban expansion presents a scenario of significant risk, particularly when analyzed through the lens of shared high-resolution geospatial data. 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.
Precision Elevation Data for Forest Giants: LiDAR vs ETH Global Canopy Height in Mata do Buçaco (Portugal)
High‑resolution elevation data underpins almost every spatial analysis we do in GIS—especially in forests where vertical structure defines habitat, biomass, wind exposure, fire behavior, hydrology, and the microclimates that sustain rare species. In rugged or densely vegetated environments, a coarse or biased elevation model propagates error everywhere: orthorectification drifts, hillshades mislead, slope/aspect misclassify, and canopy metrics saturate. The result is decisions made on blurred terrain that hides the very patterns we seek to manage. Precision elevation—derived from airborne LiDAR (Light Detection and Ranging)—solves this by separating the ground from the vegetation and delivering both a bare‑earth Digital Terrain Model (DTM) and a Digital Surface Model (DSM). Subtracting DTM from DSM gives a Canopy Height Model (DHM) that captures the true vertical architecture of the forest at sub‑meter resolution.
REBATE A TU CUÑADO SOBRE “LLUVIA”
¿Cuántas veces has oído a tu cuñado (o cuñada) decir en una comida familiar frases tipo:
Antes llovía más, se está desertificando todo!”
“¡Yo ya lo noto, desde que era niño no ha vuelto a llover igual!”
En lugar de entrar en debates circulares, te propongo usar Google Earth Engine (GEE), una plataforma gratuita (si demuestras que no vas a usarlo comercialmente, claro) de computación geoespacial en la nube, y una fuente de datos de primer nivel: CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data).
Agricultura de Precisión. Uso del Satélite para la toma de decisiones en el campo
Quieres conocer cuál es el momento óptimo para plantar? Para fumigar? Para recolectar?. Sabías que dos de cada tres agricultores no cosechan en la fase de madurez adecuada?. Aquí abajo te describo un método completamente automatizado mediante el uso combinado de varios índices de vegetación como NDVI, NDWI, SAVI y EVI que podemos extraer del Satétile SENTINEL-2 en la plataforma COPERNICUS de la UE para conocer exactamente y anticipar las mejores decisiones de intervención sobre tus tierras.
Spider diagrams in GEE and QGIS
Hace unos días, caminando por una calle concreta —no muy lejos de mi casa, la cual recorro con regularidad— me pregunté cuál era realmente la estación de metro más cercana. Tiendo a pensar que siempre es Puerta del Ángel… y en realidad, lo es. Pero aún así, quise comprobarlo quantitativamente, empíricamente. ¿Y si no fuera tan evidente?
En este tutorial se explica cómo crear un efecto visual dinámico en QGIS que simula una «araña» (ragnetto) que conecta automáticamente los 8 puntos más cercanos al cursor del mouse. Esta funcionalidad permite visualizar relaciones espaciales de forma interactiva y resulta especialmente útil para análisis de proximidad.
Geovisualization.net 