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Get lender-ready feasibility reports in 10 minutes for $795. Zoning, flood & utilities data from FEMA, ArcGIS, TxDOT. Replace $10K studies.

Tegola Docs

Generate SDKs and Docs for APIs

Land Information Management System reference architecture introduction

Giraffe is *the* digital foundation for teams dealing with real assets. Whether you manage existing assets, design and develop new assets, or create policy; Giraffe puts serious digital capability in the hands of every member of your team.

Best Places to Live | Compare cost of living, crime, cities, schools and more.

Maplibre styles in React, data-dependent styling, zoom-dependent styling, dynamic layers…

Building maps that use dynamic tiles from the database is a lot of fun. You get the freshest data, you don't have to think about generating a static tile set, and you can do it with very minimal middleware, using pg_tileserv.

Rekha offers a step-by-step guide for deploying Varnish Cache for pg_tileserv using Kustomize and the Postgres Operator for Kubernetes on OpenShift.

Exploration of the implementation of simple features standard by the sf package for R

A completely FLOSS map stack.

Introduction The GitLab team handbook is the central repository for how we run the company. Printed, it consists of over 2,000 pages of text. As part of our value of being transparent the handbook is open to the world, and we welcome feedback. Please make a merge request to suggest improvements or add clarifications. Please use issues to ask questions. For a very specific set of internal information we also maintain an Internal Handbook

This is the main group for the GitLab Data Team.

Enterprise Data Warehouse Overview Architectural Overview The EDW is viewed as a series of layers. With five consecutive layers, where data progresses through the layers, and one development layer where data is explored and developed. Each layer has a purpose in the overall operation and effectiveness of the EDW. All data within the EDW will land in Landing. Subsequently all following layers are optional, with the remark that Tableau should connect only to prod database schemas.

Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on future (Bengtsson, 2024 doi:10.32614/CRAN.package.future) and mirai (Gao et al., 2025 doi:10.32614/CRAN.package.mirai) parallel back-ends, terra (Hijmans et al., 2025 doi:10.32614/CRAN.package.terra) and sf (Pebesma et al., 2024 doi:10.32614/CRAN.package.sf) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in terra or sf packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses exactextractr (Baston, 2023 doi:10.32614/CRAN.package.exactextractr), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided.

Organisations have been struggling to get their microservices boundaries right. Here's what they should do: instead of size, think about the flow.

Learn how to determine the land value of a property using tax records, apps, comparables, and professional methods with CRI Properties' expert guidance.

The Complete Guide to Appraising Vacant Land for Investment Purposes