Ordnance Survey’s Post

Ordnance Survey data can be used to tell stories at all scales. NGD Land data contains information about the physical surface of the land across Great Britain. I've taken a few fairly random subsets of the NGD Land data and combined some together according to my own arbitrary tastes; see comments for which classifications I'm using for "trees", "gardens", and "heath and scrub". By aggregating to 10km grid cells across Great Britain, we see that Scotland is home to a lot of our heath and scrub land, gardens are (obviously) clustered towards our urban centres, and trees are found surprisingly uniformly across the country. By aggregating to 1km grid cells within the ceremonial county of Hampshire, the New Forest stands out as the home of most of the county's heath and scrub and patches of woodlands stand out. By aggregating to 100m grid cells and focusing on the Southampton Built-Up Area, the structure of the city's neighbourhoods and parks stand out clearly. All analysis performed in Databricks with pyspark, and the visual was put together with matplotlib.

My #tidytuesday plot for the week of October 10, 2023: Code: https://lnkd.in/gzYMUVUm Supernatural States A map plot with states filled by number of hauntings and points of cities scaled to size by number of hauntings. I filtered cities with >10 hauntings. Notably, the Eastern U.S. is the most haunted region of the U.S. but CA is by far the most haunted state (1069 hauntings!!). The data is haunted places in the U.S.: https://lnkd.in/gZ7328ZW As a side note, I used the Supernatural Knight font from the show Supernatural. #tidyverse #R #ggplot2 #dataviz #datavisualization #datanalysis #rstats #datascience #knowledgesharing

tech keeps evolving by each second

2024 #30DayChartChallenge | day 16 | relationships | weather . The data comes from NOAA—National Centers for Environmental Information, Global Surface Summary of the Day—GSOD. This #viz shows the monthly temperatures for PR and NJ in 2022. . 📂: https://lnkd.in/ez_gAkp6 .  #rstats | #dataviz | #rstats | #ggplot2

Create this map using R 👇 Check out my last post to create this awesome map using R, and my package in development {forestdata} to access the EU-Trees4F dataset. https://lnkd.in/db9UZdVy #rvis #rstats #gis

Another intensive knowledge shock day! I attended a spatial data science one-day bootcamp covering various topics: 1. Geoparque, a geometry data file format. 2. H3, a geometry indexing system using polygons, which is fundamental to geo graphs. 3. Discussion on the Geopandas library for processing geodata, including a couple of geometry weight algorithms used to determine weights between two geopoints. 4. geospatial neighborhood analysis. 5. Introduction to GIS DB and the Carto low-code platform for visualization.

The Mariana Trench is the deepest part of the world's oceans, reaching a maximum known depth of about 10,994 meters in the Challenger Deep. 🌊 As the #30DayMapChallenge is also about learning, I used this prompt as an opportunity for my first dive into bathymetric data and the {marmap} package. And while the majority of maps so far have been created with {ggplot2}, for this map I used base R plot functionality. #30DayMapChallenge | Day 16: Oceania #rstats #dataviz #datavisualization #maps #gis

Analyzing large vector datasets on Source Cooperative with #DuckDB 🦆Using the 75 GB National Wetlands Inventory as an example. It used to take me hours to get summary statistics of the dataset, now it takes seconds with DuckDB 🚀 Dataset: https://lnkd.in/ehJ649YQ Colab: https://lnkd.in/ddByAFPF #geospatial #dataviz #opendata #gis #datascience #datavisualization

Any of my geo friends start playing with the #overture map data yet. Wonder if there are any "places" with more than just a "name". I am trying to find geo referenced objects that contain paragraphs (or more) of text data. I guess it could also be image data. The hard part seems to be to either: 1. Find big text/image data that already has coords attached 2. Find geo data that has more than integers and short strings for data

Running queries on geographical data stored in a database can be quite slow, especially at very high data volumes. Dakshin K. introduces the power of Geo-indices and deep dives into a case study on how they can help speed up most queries by up to 80%, bringing down the time from a couple of minutes to a few seconds. Read here - https://lnkd.in/g34BKSTG