My current aim is to leverage spatial data science to drive sustainability-focused initiatives through data-driven insights and nature-based solutions. To achieve this, I specialize in cutting-edge techniques such as remote sensing, spatial analysis, machine learning (ML), and artificial intelligence (AI).
My work includes creating frameworks and analyses to assist in water network management, civil engineering projects, and environmental consultancy studies. I am passionate about developing open-source tools that simplify and enhance geospatial analysis.
Web-Application to visualize monthly and annualy climatological data at any part of the world.
Powered by Google Earth Engine.
This web app retrieves rainfall estimates from rain gauges and satellite observations throughout CHIRPS dataset. Evapotranspiration data is sourced from the MODIS dataset, a satellite-based Earth observation product that measures overall evapotranspiration using a spectroradiometer. The app is designed to assist in civil engineering projects by estimating monthly and annual rainfall and evapotranspiration flows for any geographic boundary worldwide.
While there is ongoing debate regarding the accuracy of these datasets compared to in-situ measurements, as discussed here, the estimates provided by the app are quickly accessible. They offer a valuable starting point and enhance understanding of the water cycle in the area of interest.
Dublin has been improving its cycle lanes over the last few years. The quays of the River Liffey are receiving more preferential and exclusive cycle bike lanes due to the increasing demand from bikers and the pressure to become a greener city with zero carbon footprint. Additionally, shared bike schemes are available options for commuting in and around Dublin city. However, aside from infrastructure challenges, the city faces a lack of safety for bike owners. The number of bike thefts is rising, with more than 3,000 bikes stolen in the first half of 2023.
Dublin Bikes' campaign has been gathering information from bike users to improve mobility and has launched a database with the geolocations of bike thefts in the city.
I used the dataset provided by Dublin Cycling Campaign
to analyze the hotspots of thefts in the city during 2023, with more than nine hundred bike thefts registered.
Powered by ESRI.
Analysis of Airbnb in Mallorca - Spain
Mallorca's Airbnb spatial analysis unveils the strategic distribution of short-term rentals across the island, mapping their concentration in urban centers and coastal zones.
These properties have been reshaping the housing landscape, significantly driving up local housing costs and forcing native residents out of their traditional neighborhoods. The stark economic transformation echoes the poignant question raised by Majorca Daily Bulletin::
"Will there be any neighbourhood left without gentrification?". With Airbnb rates substantially exceeding local rental prices, the tourism-driven market threatens the island's social fabric.
This complex phenomenon not only generates substantial tourism revenue but also challenges Mallorca's long-term community sustainability, underscoring the urgent need for balanced regulatory interventions and community dialogue.
Interactive geospatial visualization powered by Kepler gl. Data is sourced from Inside Airbnb.
In Maintenance...
Mapping solutions and geovisualizations
3D visualization of Ireland's Land Cover and Land Use
On the 29 October, Valencia received a year's worth of rain in just 8 hours. This deluge caused several flash floods. These images were acquired from Sentinel-2 satellite and illustrate the scale of the disaster, with images from the median compositian of the october and the acquisited image on 31 October.
The Spectral Index geo-app aims to leveraging the monthly and annualy composes of the most well-knowing remote sensing index. Such as Normalized Difference Vegetation index (NDVI), Normalized Difference Water Index (NDWI).
The Contour Lines geo-app creates 10-meters contour lines using interpolation of Digital Elevation Models (DEM) over the area of interest. The dataset is sourced from USGS.
