| Port details |
- py-geosnap Geospatial Neighborhood Analysis Package
- 0.14.0_1 graphics
 =2      Package not present on quarterly.This port was created during this quarter. It will be in the next quarterly branch but not the current one. - Maintainer: sunpoet@FreeBSD.org
- Port Added: 2024-08-04 17:24:36
- Last Update: 2024-08-28 06:54:30
- Commit Hash: 1708999
- People watching this port, also watch:: jdictionary, py311-Automat, py311-python-gdsii, py39-PyOpenGL, p5-Sane
- Also Listed In: python
- License: BSD3CLAUSE
- WWW:
- https://oturns.github.io/geosnap-guide/
- https://github.com/oturns/geosnap
- Description:
- geosnap provides a suite of tools for exploring, modeling, and visualizing the
social context and spatial extent of neighborhoods and regions over time. It
brings together state-of-the-art techniques from geodemographics,
regionalization, spatial data science, and segregation analysis to support
social science research, public policy analysis, and urban planning. It provides
a simple interface tailored to formal analysis of spatiotemporal urban data.
Main Features:
- fast, efficient tooling for standardizing data from multiple time periods into
a shared geographic representation appropriate for spatiotemporal analysis
- analytical methods for understanding sociospatial structure in neighborhoods,
cities, and regions, using unsupervised ML from scikit-learn and spatial
optimization from PySAL
- classic and spatial analytic methods for diagnosing model fit, and locating
(spatial) statistical outliers novel techniques for understanding the
evolution of neighborhoods over time, including identifying hotspots of local
neighborhood change, as well as modeling and simulating neighborhood
conditions into the future
- quick access to a large database of commonly-used neighborhood indicators from
U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the
cloud thanks to quilt and the highly-performant geoparquet file format.
  ¦  ¦  ¦  ¦ 
- Manual pages:
- FreshPorts has no man page information for this port.
- pkg-plist: as obtained via:
make generate-plist - There is no configure plist information for this port.
- Dependency lines:
-
- ${PYTHON_PKGNAMEPREFIX}geosnap>0:graphics/py-geosnap@${PY_FLAVOR}
- To install the port:
- cd /usr/ports/graphics/py-geosnap/ && make install clean
- To add the package, run one of these commands:
- pkg install graphics/py-geosnap
- pkg install py311-geosnap
NOTE: If this package has multiple flavors (see below), then use one of them instead of the name specified above.
NOTE: This is a Python port. Instead of py311-geosnap listed in the above command, you can pick from the names under the Packages section.- PKGNAME: py311-geosnap
- Package flavors (<flavor>: <package>)
- distinfo:
- TIMESTAMP = 1722711129
SHA256 (geosnap-0.14.0.tar.gz) = 3ef75ef510934e5eb30b0e9c80cd94952a1266a05a5987ae06945781e97eac1f
SIZE (geosnap-0.14.0.tar.gz) = 29913517
Packages (timestamps in pop-ups are UTC):
- Dependencies
- NOTE: FreshPorts displays only information on required and default dependencies. Optional dependencies are not covered.
- Build dependencies:
-
- py311-setuptools>=61.0 : devel/py-setuptools@py311
- py311-setuptools-scm>=6.2 : devel/py-setuptools-scm@py311
- py311-wheel>=0 : devel/py-wheel@py311
- python3.11 : lang/python311
- py311-build>=0 : devel/py-build@py311
- py311-installer>=0 : devel/py-installer@py311
- Test dependencies:
-
- python3.11 : lang/python311
- Runtime dependencies:
-
- py311-contextily>=0 : graphics/py-contextily@py311
- py311-fsspec>= : devel/py-fsspec@py311
- py311-geopandas>=0.9 : graphics/py-geopandas@py311
- py311-giddy>=2.2.1 : graphics/py-giddy@py311
- py311-libpysal>=0 : science/py-libpysal@py311
- py311-mapclassify>=0 : graphics/py-mapclassify@py311
- py311-matplotlib>=0 : math/py-matplotlib@py311
- py311-numpy>=0,1 : math/py-numpy@py311
- py311-pandana>=0 : graphics/py-pandana@py311
- py311-pandas>=0,1 : math/py-pandas@py311
- py311-platformdirs>=0 : devel/py-platformdirs@py311
- py311-pooch>=0 : devel/py-pooch@py311
- py311-proplot>=0.9 : graphics/py-proplot@py311
- py311-pyarrow>=0.14.1 : databases/py-pyarrow@py311
- py311-pyproj>=3 : graphics/py-pyproj@py311
- py311-quilt3>=3.6 : www/py-quilt3@py311
- py311-s3fs>=0 : devel/py-s3fs@py311
- py311-scikit-learn>=0 : science/py-scikit-learn@py311
- py311-seaborn>=0 : math/py-seaborn@py311
- py311-segregation>=2.1 : science/py-segregation@py311
- py311-spopt>=0.3.0 : math/py-spopt@py311
- py311-tobler>=0.8.2 : science/py-tobler@py311
- py311-tqdm>=0 : misc/py-tqdm@py311
- py311-xlrd>=0 : textproc/py-xlrd@py311
- python3.11 : lang/python311
- There are no ports dependent upon this port
Configuration Options:- No options to configure
- Options name:
- graphics_py-geosnap
- USES:
- python
- FreshPorts was unable to extract/find any pkg message
- Master Sites:
|
Number of commits found: 2
| Commit History - (may be incomplete: for full details, see links to repositories near top of page) |
| Commit | Credits | Log message |
|---|
0.14.0_1
28 Aug 2024 06:54:30
 |
Loïc Bartoletti (lbartoletti) |
graphics/py-geopandas: Update to 0.14.4 |
0.14.0
04 Aug 2024 17:13:52
|
Po-Chuan Hsieh (sunpoet) |
graphics/py-geosnap: Add py-geosnap 0.14.0
geosnap provides a suite of tools for exploring, modeling, and visualizing the
social context and spatial extent of neighborhoods and regions over time. It
brings together state-of-the-art techniques from geodemographics,
regionalization, spatial data science, and segregation analysis to support
social science research, public policy analysis, and urban planning. It provides
a simple interface tailored to formal analysis of spatiotemporal urban data.
Main Features:
- fast, efficient tooling for standardizing data from multiple time periods into
a shared geographic representation appropriate for spatiotemporal analysis
- analytical methods for understanding sociospatial structure in neighborhoods,
cities, and regions, using unsupervised ML from scikit-learn and spatial
optimization from PySAL
- classic and spatial analytic methods for diagnosing model fit, and locating
(spatial) statistical outliers novel techniques for understanding the
evolution of neighborhoods over time, including identifying hotspots of local
neighborhood change, as well as modeling and simulating neighborhood
conditions into the future
- quick access to a large database of commonly-used neighborhood indicators from
U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the
cloud thanks to quilt and the highly-performant geoparquet file format. |
Number of commits found: 2
|
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