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Simplifies your WebMap geojson geometry with Mapsharper and LeafletJS

Simplifies your WebMap geojson geometry with Mapsharper and LeafletJS

1 sec to 120ms without excessive loss of quality! This is a short tutorial to speed up loading of a Webmap, optimizing the shape of gemetrie, which often contain “too many points” Useful resources: – Map example (http://cityplanner.it/webapp/tutorial…) – Online Tool to simplify the GeoJSON (http://www.mapshaper.org/) Credits: – Fast by irene hoffman from the Noun Project – Leafletjs.com logo

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Using Python to Build a GIS Data Pipeline for Rural-Urban Classification

Using Python to Build a GIS Data Pipeline for Rural-Urban Classification

This talk will introduce the use of Python and several related modules (GDAL, Shapely, Fiona etc) to build a GIS-based data processing pipeline to retrieve the road network information required to build a rural-urban classification scheme for a region of interest. The speaker will highlight the main concepts used to build the data pipeline and run a simple demo to ...

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ArcGIS 10.3 for Developers

ArcGIS 10.3 for Developers

The ArcGIS platform includes Runtime Software Development Kits (SDKs) that empower developers to build mapping and GIS applications for web, mobile and desktop clients to support common workflows for their customers. ArcGIS Runtime SDKs make it easy to create compelling custom applications that integrate geospatial data and GIS capabilities. ArcGIS 10.3 has support for QML and QT 5.3 deployed across ...

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MapillaryJS – Integrando mapillary en tu aplicación

MapillaryJS – Integrando mapillary en tu aplicación

Pau Gargallo nos habla de Mapillary y de como integrarlo en aplicaciones web usando la librería mapillary-js. Mapillary es un servicio de fotos tomadas a pie de calle que pretende visualizar todo el mundo usando fotos tomadas por usuarios. Es abierto, todos podemos contribuir con fotos y las fotos están disponibles bajo licencia CC BY-SA. Slides: https://docs.google.com/presentation/… Repo: https://github.com/mapillary

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Reproducible spatial analysis with ArcPy and R using Jupyter Notebook

Reproducible spatial analysis with ArcPy and R using Jupyter Notebook

Reproducibility, the ability of an entire study to be replicated, is one of the core concepts in data science. Although preparing data analyses so they are reproducible is not a trivial task, it can bring many benefits and make a researcher’s life much easier: it can help to save time by allowing reuse of code and results from past studies ...

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