Global Water Security Center’s Post

I have a background in data science and a passion for smart mobility. I have been working on various projects involving transportation datasets and smart mobility solutions. To enhance my skills and knowledge in this sector, I will be reviewing some concepts and tools related to GIS, Python, and data analysis. 📖 I will also be sharing some valuable notes and resources here for those who are interested in this topic and want to learn more about this field. 📢 Stay tuned and feel free to contact me if you have any suggestions or requests for specific topics or projects that you would like me to discuss here. #datascience #GIS #dataanalysis #pyhton #smartmobility #researcher

📖 A Beginner’s Guide to Handling Geospatial Data Using Python After the earthquake that struck Morocco last week, I spent all my free time pondering geospatial data because I had never worked with it before. This dataset was the first one I came across, and I invested so much time in researching and learning about geospatial data that when I found a more interesting dataset for my analysis, I couldn't simply discard this one. It had been instrumental in teaching me how to handle Google Earth files (KML/KMZ). Consequently, I decided to share my experience in an article. link to the article: https://lnkd.in/eg7Mmi57 I hope you'll find it informative! NOAA Global Systems Laboratory #geospatial #data #EDA #datascience #earthquake

Data science with python

[IN-PERSON COURSE IN BUDAPEST] I am launching a zero-to-hero course in person in geospatial data science in Python in the heart of Budapest, located at the Central European University, with a limited number of seats. 𝐃𝐞𝐭𝐚𝐢𝐥𝐬 𝐚𝐧𝐝 𝐫𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: https://lnkd.in/g9AsqSqE 𝐎𝐮𝐭𝐥𝐢𝐧𝐞: 𝘋𝘢𝘺 1: 𝘐𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘵𝘰 𝘎𝘦𝘰𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘈𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘸𝘪𝘵𝘩 𝘗𝘺𝘵𝘩𝘰𝘯 - Historical context and significance of geospatial analytics. - Introduction to Python for geospatial analysis, focusing on basic concepts and tools. - Working with key geospatial libraries in Python (e.g., Geopandas, Rasterio). - Data visualization techniques for spatial data, including interactive maps. 𝘋𝘢𝘺 2: 𝘔𝘢𝘱𝘱𝘪𝘯𝘨 𝘜𝘳𝘣𝘢𝘯 𝘈𝘳𝘦𝘢𝘴 - Getting familiar with free data sources, such as demographics data dn OpenStreetMap - Download and quantify urban areas based on OSM - Visualizing and processing road network and public transport data - Exploratory data analysis for geospatial datasets 𝘋𝘢𝘺 3: 𝘎𝘦𝘰𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘗𝘳𝘰𝘧𝘪𝘭𝘪𝘯𝘨 - Advanced map visualization with Python - Overlaying various raster and urban data sets, such as population grid - Create detailed feature profiles of urban areas - The concept of spatial indexing 𝘋𝘢𝘺 4: 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘪𝘯 𝘚𝘱𝘢𝘵𝘪𝘢𝘭 𝘈𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 - Introduction to machine learning for geospatial data - Spatial clustering of topical POIs - Classification and regression on geospatial problems 𝘋𝘢𝘺 5: 𝘍𝘪𝘯𝘢𝘭 𝘗𝘳𝘰𝘫𝘦𝘤𝘵 𝘗𝘳𝘦𝘴𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯 #GIS #spatialanalytics #geospatialdata #geospatial #datascience #datavisualization

[IN-PERSON COURSE IN BUDAPEST] I am launching a zero-to-hero course in person in geospatial data science in Python in the heart of Budapest, located at the Central European University, with a limited number of seats. 𝐃𝐞𝐭𝐚𝐢𝐥𝐬 𝐚𝐧𝐝 𝐫𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: https://lnkd.in/dqNzcUNh 𝐎𝐮𝐭𝐥𝐢𝐧𝐞: 𝘋𝘢𝘺 1: 𝘐𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘵𝘰 𝘎𝘦𝘰𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘈𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘸𝘪𝘵𝘩 𝘗𝘺𝘵𝘩𝘰𝘯 - Historical context and significance of geospatial analytics. - Introduction to Python for geospatial analysis, focusing on basic concepts and tools. - Working with key geospatial libraries in Python (e.g., Geopandas, Rasterio). - Data visualization techniques for spatial data, including interactive maps. 𝘋𝘢𝘺 2: 𝘔𝘢𝘱𝘱𝘪𝘯𝘨 𝘜𝘳𝘣𝘢𝘯 𝘈𝘳𝘦𝘢𝘴 - Getting familiar with free data sources, such as demographics data dn OpenStreetMap - Download and quantify urban areas based on OSM - Visualizing and processing road network and public transport data - Exploratory data analysis for geospatial datasets 𝘋𝘢𝘺 3: 𝘎𝘦𝘰𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘗𝘳𝘰𝘧𝘪𝘭𝘪𝘯𝘨 - Advanced map visualization with Python - Overlaying various raster and urban data sets, such as population grid - Create detailed feature profiles of urban areas - The concept of spatial indexing 𝘋𝘢𝘺 4: 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘪𝘯 𝘚𝘱𝘢𝘵𝘪𝘢𝘭 𝘈𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 - Introduction to machine learning for geospatial data - Spatial clustering of topical POIs - Classification and regression on geospatial problems 𝘋𝘢𝘺 5: 𝘍𝘪𝘯𝘢𝘭 𝘗𝘳𝘰𝘫𝘦𝘤𝘵 𝘗𝘳𝘦𝘴𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯 #GIS #spatialanalytics #geospatialdata #geospatial #datascience #datavisualization

Geospatial data analysis is critical in urban planning, environmental research, agriculture, transportation industries, and more. The growing need has led to an increase in the use of Python packages for various geographic data analysis requirements, such as analyzing climate patterns or investigating urban development. Evaluating and selecting the right tools with quick processing, modification, and visualization capabilities is essential to effectively analyze and visualize geospatial data. Deep-dive into the topic:  https://lnkd.in/gXatX_PA #python #geocoding #datainsights #KDnuggets #geoinformation #geointelligence #geospatial #geospatialtechnology #locationintelligence #spatialdata #geospatialdata #datavisualization #gissystems #Quarticle #mapping #Qarta #GIS

We are happy to announce our next online workshop: Title: Spatial Data Visualization with Python Date: Thursday, March 21st, 18:00 - 20:00 CET (Rome, Berlin, Paris timezone) Speaker: Marcin Szwagrzyk is a geographer with a Ph.D. in geography, acquired at Jagiellonian University in Cracow (Poland). His thesis was focusing on modeling future land use changes. Over the years, he has accumulated extensive experience in various aspects of the geospatial industry, dabbling in flood risk modeling, air quality measurements, and spatial data analytics for financial institutions. Description: In this workshop, participants will gain a comprehensive understanding of the fundamental principles behind representing the Earth's surface on a flat, two-dimensional plane. Participants will get to know the most popular cartographic projections, their pros and cons. Emphasis will be placed on the selection of a cartographic projection tailored to specific objectives. Equipped with this expertise, attendees will craft two professionally polished, publication-ready maps. The raw data, acquired from the freely available sources will be processed and analyzed with the use of the geopandas Python library. Subsequently, we will employ the matplotlib library to visualize the data - by leveraging popular cartographic techniques, including graduated colors and proportional symbols. Minimal registration fee: 20 euro (or 20 USD or 800 UAH) Registration details: https://lnkd.in/ey-BSfik #python #spatialdata #visulization

Applied Geospatial Data Science with Python

A great book titled 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 by 「BONNY MCCLAiN」 “rub some data in it” -- and thank you for the supply! Published by O'Reilly in 2022, a highly recommended piece for anyone starting to take steps from the world of ArcGIS to the realm of Python! A little appetizer: 𝘐𝘯 𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘥𝘢𝘵𝘢 𝘴𝘤𝘪𝘦𝘯𝘤𝘦, 𝘵𝘩𝘪𝘯𝘨𝘴 𝘪𝘯 𝘤𝘭𝘰𝘴𝘦𝘳 𝘱𝘳𝘰𝘹𝘪𝘮𝘪𝘵𝘺 𝘵𝘰 𝘰𝘯𝘦 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘭𝘪𝘬𝘦𝘭𝘺 𝘩𝘢𝘷𝘦 𝘮𝘰𝘳𝘦 𝘪𝘯 𝘤𝘰𝘮𝘮𝘰𝘯 𝘵𝘩𝘢𝘯 𝘵𝘩𝘪𝘯𝘨𝘴 𝘵𝘩𝘢𝘵 𝘢𝘳𝘦 𝘧𝘢𝘳𝘵𝘩𝘦𝘳 𝘢𝘱𝘢𝘳𝘵. 𝘞𝘪𝘵𝘩 𝘵𝘩𝘪𝘴 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 𝘣𝘰𝘰𝘬, 𝘨𝘦𝘰𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴, 𝘥𝘢𝘵𝘢 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵𝘴, 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘢𝘯𝘢𝘭𝘺𝘴𝘵𝘴, 𝘨𝘦𝘰𝘨𝘳𝘢𝘱𝘩𝘦𝘳𝘴, 𝘨𝘦𝘰𝘭𝘰𝘨𝘪𝘴𝘵𝘴, 𝘢𝘯𝘥 𝘰𝘵𝘩𝘦𝘳𝘴 𝘧𝘢𝘮𝘪𝘭𝘪𝘢𝘳 𝘸𝘪𝘵𝘩 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘢𝘯𝘥 𝘷𝘪𝘴𝘶𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘸𝘪𝘭𝘭 𝘭𝘦𝘢𝘳𝘯 𝘵𝘩𝘦 𝘧𝘶𝘯𝘥𝘢𝘮𝘦𝘯𝘵𝘢𝘭𝘴 𝘰𝘧 𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘢 𝘥𝘦𝘦𝘱𝘦𝘳 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨 𝘰𝘧 𝘵𝘩𝘦𝘪𝘳 𝘥𝘢𝘵𝘢 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴. 𝐂𝐨𝐧𝐭𝐞𝐧𝐭: - Understand the importance of applying spatial relationships in data science - Select and apply data layering of both raster and vector graphics - Apply location data to leverage spatial analytics - Design informative and accurate maps - Automate geographic data with Python scripts - Explore Python packages for additional functionality - Work with atypical data types such as polygons, shape files, and projections - Understand the graphical syntax of spatial data science to stimulate curiosity 𝐆𝐞𝐭 𝐲𝐨𝐮𝐫 𝐜𝐨𝐩𝐲: https://lnkd.in/dEU9QJgf #gis #spatialanalytics #geospatialdata #arcgis #python #datascience