Geospatial data science with Julia
juliaearth.github.io
Geospatial data science with Julia
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Re: Geospatial data science with Julia
#2Geospatial Data Science with Julia presents a fresh approach to data science with geospatial data and the Julia programming language. It contains best practices for writing clean, readable and performant code in geoscientific applications involving sophisticated representations of the (sub)surface of the Earth such as unstructured meshes made of 2D and 3D geometries.
Re: Geospatial data science with Julia
#3Geospatial Data Science with Julia presents a fresh approach to data science with geospatial data and the Julia programming language. It contains best practices for writing clean, readable and performant code in geoscientific applications involving sophisticated representations of the (sub)surface of the Earth such as unstructured meshes made of 2D and 3D geometries.
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Re: Geospatial data science with Julia
#4I have a passion project 4x4anarchy.com that operates with a Python-MariaDB system for querying map data by latitude and longitude, transforming it into GeoJSON for map display. The website deals with sizable tables, approximately 1 GB in size. I've made extensive optimizations, relying on well-structured indexes, caching mechanisms, and query optimization to enhance performance.
Given these circumstances, how might the incorporation of Julia and some geospatial DB (PostGIS) contribute to further optimizing geospatial data retrieval and presentation, especially when dealing with large datasets and intricate geospatial operations?
Re: Geospatial data science with Julia
#5I have a passion project 4x4anarchy.com that operates with a Python-MariaDB system for querying map data by latitude and longitude, transforming it into GeoJSON for map display. The website deals with sizable tables, approximately 1 GB in size. I've made extensive optimizations, relying on well-structured indexes, caching mechanisms, and query optimization to enhance performance. Given these circumstances, how might…
Cool site! Any chance of a adding a simple KMZ export for offline use for a given area of interest?
Re: Geospatial data science with Julia
#6I have a passion project 4x4anarchy.com that operates with a Python-MariaDB system for querying map data by latitude and longitude, transforming it into GeoJSON for map display. The website deals with sizable tables, approximately 1 GB in size. I've made extensive optimizations, relying on well-structured indexes, caching mechanisms, and query optimization to enhance performance. Given these circumstances, how might…
Cool site! Any chance of a adding a simple KMZ export for offline use for a given area of interest?
Yeah, I can do that. Will get to it tomorrow!
Re: Geospatial data science with Julia
#7Earlier quoted context omitted.
Cool site! Any chance of a adding a simple KMZ export for offline use for a given area of interest?
Yeah, I can do that. Will get to it tomorrow!
Awesome - getting KMZs of 4x4 routes is way harder than it should be. All the Colorado data is there but extracting it is challenging.
Re: Geospatial data science with Julia
#8I have a passion project 4x4anarchy.com that operates with a Python-MariaDB system for querying map data by latitude and longitude, transforming it into GeoJSON for map display. The website deals with sizable tables, approximately 1 GB in size. I've made extensive optimizations, relying on well-structured indexes, caching mechanisms, and query optimization to enhance performance. Given these circumstances, how might…
Interesting! I work on a very similar product.
I don't know Julia well, but I definitely would suggest exploring whether PostGIS can help improve the speed of your DB queries.
I'd also consider how you deliver your geospatial data to your clients -- I'm not sure GeoJSON is your best bet. Protobuf tiles might be better for your use-case (e.g. the Mapbox Vector Tiles spec).
Re: Geospatial data science with Julia
#9Nice thing about Julia is that you randomly find cool projects like this.
Re: Geospatial data science with Julia
#10In the preface you list:
- Generate high-performance code
- Specialize on multiple arguments
- Evaluate code interactively
- Exploit parallel hardware
> This list of requirements eliminates Python, R and other mainstream languages used for data science.
Can you elaborate on why/how? Awesome work by the way