%global _empty_manifest_terminate_build 0
Name: python-geosnap
Version: 0.11.0
Release: 1
Summary: Geospatial Neighborhood Analysis Package
License: BSD
URL: https://spatialucr.github.io/geosnap-guide
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/cb/a6/2d33f464036916ee252ba33454a0ddc299423dfc3ae02118d7a00cf96a69/geosnap-0.11.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-geopandas
Requires: python3-matplotlib
Requires: python3-scikit-learn
Requires: python3-seaborn
Requires: python3-libpysal
Requires: python3-mapclassify
Requires: python3-giddy
Requires: python3-xlrd
Requires: python3-cenpy
Requires: python3-appdirs
Requires: python3-tqdm
Requires: python3-quilt3
Requires: python3-pyarrow
Requires: python3-contextily
Requires: python3-descartes
Requires: python3-scikit-plot
Requires: python3-tobler
Requires: python3-spopt
Requires: python3-fsspec
Requires: python3-s3fs
Requires: python3-segregation
Requires: python3-proplot
Requires: python3-versioneer
Requires: python3-pyproj
Requires: python3-pandana
Requires: python3-sphinx
Requires: python3-sphinxcontrib-bibtex
Requires: python3-sphinx-bootstrap-theme
Requires: python3-numpydoc
Requires: python3-coverage
Requires: python3-coveralls
Requires: python3-matplotlib
Requires: python3-pytest
Requires: python3-pytest-mpl
Requires: python3-pytest-cov
Requires: python3-hdbscan
Requires: python3-twine
Requires: python3-jupyter
Requires: python3-descartes
Requires: python3-pygraphviz
Requires: python3-cenpy
%description
The Geospatial Neighborhood Analysis Package
[](https://github.com/spatialucr/geosnap/actions/workflows/unittests.yml)
[](https://codecov.io/gh/spatialucr/geosnap)
[](https://doi.org/10.5281/zenodo.3526163)
`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](https://en.wikipedia.org/wiki/Geodemography), [regionalization](https://www.sciencedirect.com/topics/earth-and-planetary-sciences/regionalism), [spatial data science](https://geographicdata.science/book), and [segregation analysis](https://github.com/pysal/segregation) 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](https://pysal.org)
* 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](https://open.quiltdata.com/b/spatial-ucr) of commonly-used neighborhood indicators from U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the cloud thanks to [quilt](https://quiltdata.com/) and the highly-performant [geoparquet](https://carto.com/blog/introducing-geoparquet-geospatial-compatibility/) file format.
## Why
Understanding neighborhood context is critical for social science research, public policy analysis, and urban planning. The social meaning, formal definition, and formal operationalization of ["neighborhood"](https://www.cnu.org/publicsquare/2019/01/29/once-and-future-neighborhood) depends on the study or application, however, so neighborhood analysis and modeling requires both flexibility and adherence to a formal pipeline. Maintaining that balance is challenging for a variety of reasons:
* many different physical and social data can characterize a neighborhood (e.g. its proximity to the urban core, its share of residents with a high school education, or the median price of its apartments) so there are countless ways to model neighborhoods by choosing different subsets of attributes to define them
* conceptually, neighborhoods evolve through both space and time, meaning their socially-construed boundaries can shift over time, as can their demographic makeup.
* geographic tabulation units change boundaries over time, meaning the raw data are aggregated to different areal units at different points in time.
* the relevant dimensions of neighborhood change are fluid, as are the thresholds that define meaningful change
To address those challenges, geosnap incorporates tools from the PySAL ecosystem and scikit-learn along with internal data-wrangling that helps keep inputs and outputs simple for users. It operates on long-form geodataframes and includes logic for common transformations, like harmonizing geographic boundaries over time, and standardizing variables within their time-period prior to conducting pooled geodemographic clustering.
This means that while geosnap has native support for commonly-used datasets like the Longitudinal Tract Database [(LTDB)](https://www.brown.edu/academics/spatial-structures-in-social-sciences/ltdb-following-neighborhoods-over-time), or the Neighborhood Change Database [(NCDB)](https://geolytics.com/products/normalized-data/neighborhood-change-database), it can also incorporate a wide variety of datasets, at _any_ spatial resolution, as long as the user understands the implications of the interpolation process.
## Research Questions
The package supports social scientists examining questions such as:
- Where are the socially-homogenous districts in the city?
- Have the composition of these districts or their location shifted over time?
- What are the characteristics of prototypical neighborhoods in city or region X?
- Have the locations of different neighborhood prototypes changed over time? e.g:
- do central-city neighborhoods show signs of gentrification?(and/or does poverty appear to be suburbanizing?)
- is there equitable access to fair housing in high-opportunity neighborhoods (or a dearth of resources in highly-segregated neighborhoods)?
- Which neighborhoods have experienced dramatic change in several important variables? (and are they clustered together in space?)
- If spatial and temporal trends hold, how might we expect neighborhoods to look in the future?
- how does the region look differently if units 1,2, and 3 are changed to a different type in the current time period?
- Has the region become more or less segregated over time?
- at which spatial scales?
- is the change statistically significant?
## Installation
The recommended method for installing geosnap is with
[anaconda](https://www.anaconda.com/download/).
```bash
conda install -c conda-forge geosnap
```
`geosnap` is alaso available on PyPI and can be install with pip via
```bash
pip install geosnap
```
## User Guide
See the [User Guide](https://spatialucr.github.io/geosnap-guide/) for a
gentle introduction to using `geosnap` for neighborhood research
## API Documentation
See the [API docs](https://spatialucr.github.io/geosnap/api.html) for a thorough explanation of `geosnap`'s core functionality
## Development
geosnap development is hosted on [github](https://github.com/spatialucr/geosnap)
To get started with the development version,
clone this repository or download it manually then `cd` into the directory and run the
following commands:
```bash
conda env create -f environment.yml
conda activate geosnap
python setup.py develop
```
This will download the appropriate dependencies and install geosnap in its own conda environment.
## Bug reports
To search for or report bugs, please see geosnap’s
[issues](http://github.com/spatialucr/geosnap/issues)
## License information
See the file “LICENSE.txt” for information on the history of this software, terms &
conditions for usage, and a DISCLAIMER OF ALL WARRANTIES.
## Citation
For a generic citation of geosnap, we recommend the following:
```latex
@misc{Knaap2019,
author = {Knaap, Elijah and Kang, Wei and Rey, Sergio and Wolf, Levi John and Cortes, Renan Xavier and Han, Su},
doi = {10.5281/ZENODO.3526163},
title = {{geosnap: The Geospatial Neighborhood Analysis Package}},
url = {https://zenodo.org/record/3526163},
year = {2019}
}
```
If you need to cite a specific release of the package, please find the appropriate version on [Zenodo](https://zenodo.org/record/3526163)
## Funding
This project is supported by NSF Award #1733705,
[Neighborhoods in Space-Time Contexts](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733705\&HistoricalAwards=false)
%package -n python3-geosnap
Summary: Geospatial Neighborhood Analysis Package
Provides: python-geosnap
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-geosnap
The Geospatial Neighborhood Analysis Package
[](https://github.com/spatialucr/geosnap/actions/workflows/unittests.yml)
[](https://codecov.io/gh/spatialucr/geosnap)
[](https://doi.org/10.5281/zenodo.3526163)
`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](https://en.wikipedia.org/wiki/Geodemography), [regionalization](https://www.sciencedirect.com/topics/earth-and-planetary-sciences/regionalism), [spatial data science](https://geographicdata.science/book), and [segregation analysis](https://github.com/pysal/segregation) 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](https://pysal.org)
* 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](https://open.quiltdata.com/b/spatial-ucr) of commonly-used neighborhood indicators from U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the cloud thanks to [quilt](https://quiltdata.com/) and the highly-performant [geoparquet](https://carto.com/blog/introducing-geoparquet-geospatial-compatibility/) file format.
## Why
Understanding neighborhood context is critical for social science research, public policy analysis, and urban planning. The social meaning, formal definition, and formal operationalization of ["neighborhood"](https://www.cnu.org/publicsquare/2019/01/29/once-and-future-neighborhood) depends on the study or application, however, so neighborhood analysis and modeling requires both flexibility and adherence to a formal pipeline. Maintaining that balance is challenging for a variety of reasons:
* many different physical and social data can characterize a neighborhood (e.g. its proximity to the urban core, its share of residents with a high school education, or the median price of its apartments) so there are countless ways to model neighborhoods by choosing different subsets of attributes to define them
* conceptually, neighborhoods evolve through both space and time, meaning their socially-construed boundaries can shift over time, as can their demographic makeup.
* geographic tabulation units change boundaries over time, meaning the raw data are aggregated to different areal units at different points in time.
* the relevant dimensions of neighborhood change are fluid, as are the thresholds that define meaningful change
To address those challenges, geosnap incorporates tools from the PySAL ecosystem and scikit-learn along with internal data-wrangling that helps keep inputs and outputs simple for users. It operates on long-form geodataframes and includes logic for common transformations, like harmonizing geographic boundaries over time, and standardizing variables within their time-period prior to conducting pooled geodemographic clustering.
This means that while geosnap has native support for commonly-used datasets like the Longitudinal Tract Database [(LTDB)](https://www.brown.edu/academics/spatial-structures-in-social-sciences/ltdb-following-neighborhoods-over-time), or the Neighborhood Change Database [(NCDB)](https://geolytics.com/products/normalized-data/neighborhood-change-database), it can also incorporate a wide variety of datasets, at _any_ spatial resolution, as long as the user understands the implications of the interpolation process.
## Research Questions
The package supports social scientists examining questions such as:
- Where are the socially-homogenous districts in the city?
- Have the composition of these districts or their location shifted over time?
- What are the characteristics of prototypical neighborhoods in city or region X?
- Have the locations of different neighborhood prototypes changed over time? e.g:
- do central-city neighborhoods show signs of gentrification?(and/or does poverty appear to be suburbanizing?)
- is there equitable access to fair housing in high-opportunity neighborhoods (or a dearth of resources in highly-segregated neighborhoods)?
- Which neighborhoods have experienced dramatic change in several important variables? (and are they clustered together in space?)
- If spatial and temporal trends hold, how might we expect neighborhoods to look in the future?
- how does the region look differently if units 1,2, and 3 are changed to a different type in the current time period?
- Has the region become more or less segregated over time?
- at which spatial scales?
- is the change statistically significant?
## Installation
The recommended method for installing geosnap is with
[anaconda](https://www.anaconda.com/download/).
```bash
conda install -c conda-forge geosnap
```
`geosnap` is alaso available on PyPI and can be install with pip via
```bash
pip install geosnap
```
## User Guide
See the [User Guide](https://spatialucr.github.io/geosnap-guide/) for a
gentle introduction to using `geosnap` for neighborhood research
## API Documentation
See the [API docs](https://spatialucr.github.io/geosnap/api.html) for a thorough explanation of `geosnap`'s core functionality
## Development
geosnap development is hosted on [github](https://github.com/spatialucr/geosnap)
To get started with the development version,
clone this repository or download it manually then `cd` into the directory and run the
following commands:
```bash
conda env create -f environment.yml
conda activate geosnap
python setup.py develop
```
This will download the appropriate dependencies and install geosnap in its own conda environment.
## Bug reports
To search for or report bugs, please see geosnap’s
[issues](http://github.com/spatialucr/geosnap/issues)
## License information
See the file “LICENSE.txt” for information on the history of this software, terms &
conditions for usage, and a DISCLAIMER OF ALL WARRANTIES.
## Citation
For a generic citation of geosnap, we recommend the following:
```latex
@misc{Knaap2019,
author = {Knaap, Elijah and Kang, Wei and Rey, Sergio and Wolf, Levi John and Cortes, Renan Xavier and Han, Su},
doi = {10.5281/ZENODO.3526163},
title = {{geosnap: The Geospatial Neighborhood Analysis Package}},
url = {https://zenodo.org/record/3526163},
year = {2019}
}
```
If you need to cite a specific release of the package, please find the appropriate version on [Zenodo](https://zenodo.org/record/3526163)
## Funding
This project is supported by NSF Award #1733705,
[Neighborhoods in Space-Time Contexts](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733705\&HistoricalAwards=false)
%package help
Summary: Development documents and examples for geosnap
Provides: python3-geosnap-doc
%description help
The Geospatial Neighborhood Analysis Package
[](https://github.com/spatialucr/geosnap/actions/workflows/unittests.yml)
[](https://codecov.io/gh/spatialucr/geosnap)
[](https://doi.org/10.5281/zenodo.3526163)
`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](https://en.wikipedia.org/wiki/Geodemography), [regionalization](https://www.sciencedirect.com/topics/earth-and-planetary-sciences/regionalism), [spatial data science](https://geographicdata.science/book), and [segregation analysis](https://github.com/pysal/segregation) 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](https://pysal.org)
* 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](https://open.quiltdata.com/b/spatial-ucr) of commonly-used neighborhood indicators from U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the cloud thanks to [quilt](https://quiltdata.com/) and the highly-performant [geoparquet](https://carto.com/blog/introducing-geoparquet-geospatial-compatibility/) file format.
## Why
Understanding neighborhood context is critical for social science research, public policy analysis, and urban planning. The social meaning, formal definition, and formal operationalization of ["neighborhood"](https://www.cnu.org/publicsquare/2019/01/29/once-and-future-neighborhood) depends on the study or application, however, so neighborhood analysis and modeling requires both flexibility and adherence to a formal pipeline. Maintaining that balance is challenging for a variety of reasons:
* many different physical and social data can characterize a neighborhood (e.g. its proximity to the urban core, its share of residents with a high school education, or the median price of its apartments) so there are countless ways to model neighborhoods by choosing different subsets of attributes to define them
* conceptually, neighborhoods evolve through both space and time, meaning their socially-construed boundaries can shift over time, as can their demographic makeup.
* geographic tabulation units change boundaries over time, meaning the raw data are aggregated to different areal units at different points in time.
* the relevant dimensions of neighborhood change are fluid, as are the thresholds that define meaningful change
To address those challenges, geosnap incorporates tools from the PySAL ecosystem and scikit-learn along with internal data-wrangling that helps keep inputs and outputs simple for users. It operates on long-form geodataframes and includes logic for common transformations, like harmonizing geographic boundaries over time, and standardizing variables within their time-period prior to conducting pooled geodemographic clustering.
This means that while geosnap has native support for commonly-used datasets like the Longitudinal Tract Database [(LTDB)](https://www.brown.edu/academics/spatial-structures-in-social-sciences/ltdb-following-neighborhoods-over-time), or the Neighborhood Change Database [(NCDB)](https://geolytics.com/products/normalized-data/neighborhood-change-database), it can also incorporate a wide variety of datasets, at _any_ spatial resolution, as long as the user understands the implications of the interpolation process.
## Research Questions
The package supports social scientists examining questions such as:
- Where are the socially-homogenous districts in the city?
- Have the composition of these districts or their location shifted over time?
- What are the characteristics of prototypical neighborhoods in city or region X?
- Have the locations of different neighborhood prototypes changed over time? e.g:
- do central-city neighborhoods show signs of gentrification?(and/or does poverty appear to be suburbanizing?)
- is there equitable access to fair housing in high-opportunity neighborhoods (or a dearth of resources in highly-segregated neighborhoods)?
- Which neighborhoods have experienced dramatic change in several important variables? (and are they clustered together in space?)
- If spatial and temporal trends hold, how might we expect neighborhoods to look in the future?
- how does the region look differently if units 1,2, and 3 are changed to a different type in the current time period?
- Has the region become more or less segregated over time?
- at which spatial scales?
- is the change statistically significant?
## Installation
The recommended method for installing geosnap is with
[anaconda](https://www.anaconda.com/download/).
```bash
conda install -c conda-forge geosnap
```
`geosnap` is alaso available on PyPI and can be install with pip via
```bash
pip install geosnap
```
## User Guide
See the [User Guide](https://spatialucr.github.io/geosnap-guide/) for a
gentle introduction to using `geosnap` for neighborhood research
## API Documentation
See the [API docs](https://spatialucr.github.io/geosnap/api.html) for a thorough explanation of `geosnap`'s core functionality
## Development
geosnap development is hosted on [github](https://github.com/spatialucr/geosnap)
To get started with the development version,
clone this repository or download it manually then `cd` into the directory and run the
following commands:
```bash
conda env create -f environment.yml
conda activate geosnap
python setup.py develop
```
This will download the appropriate dependencies and install geosnap in its own conda environment.
## Bug reports
To search for or report bugs, please see geosnap’s
[issues](http://github.com/spatialucr/geosnap/issues)
## License information
See the file “LICENSE.txt” for information on the history of this software, terms &
conditions for usage, and a DISCLAIMER OF ALL WARRANTIES.
## Citation
For a generic citation of geosnap, we recommend the following:
```latex
@misc{Knaap2019,
author = {Knaap, Elijah and Kang, Wei and Rey, Sergio and Wolf, Levi John and Cortes, Renan Xavier and Han, Su},
doi = {10.5281/ZENODO.3526163},
title = {{geosnap: The Geospatial Neighborhood Analysis Package}},
url = {https://zenodo.org/record/3526163},
year = {2019}
}
```
If you need to cite a specific release of the package, please find the appropriate version on [Zenodo](https://zenodo.org/record/3526163)
## Funding
This project is supported by NSF Award #1733705,
[Neighborhoods in Space-Time Contexts](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733705\&HistoricalAwards=false)
%prep
%autosetup -n geosnap-0.11.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-geosnap -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot - 0.11.0-1
- Package Spec generated