Package: geosnap
The Geospatial Neighborhood Analysis Packagegeosnapprovides 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 fromgeodemographics,regionalization,spatial data science, andsegregation analysisto support social science research, public policy analysis, and urban planning. It provides a simple interface tailored to formal analysis of spatiotemporal urban data.Main Featuresfast, efficient tooling for standardizing data from multiple time periods into a shared geographic representation appropriate for spatiotemporal analysisanalytical methods for understanding sociospatial structure in neighborhoods, cities, and regions, using unsupervised ML from scikit-learn and spatial optimization fromPySALclassic and spatial analytic methods for diagnosing model fit, and locating (spatial) statistical outliersnovel 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 futurequick access toa large databaseof commonly-used neighborhood indicators from U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the cloud thanks toquiltand the highly-performantgeoparquetfile format.WhyUnderstanding neighborhood context is critical for social science research, public policy analysis, and urban planning. The social meaning, formal definition, and formal operationalization of"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 themconceptually, 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 changeTo 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), or the Neighborhood Change Database(NCDB), it can also incorporate a wide variety of datasets, atanyspatial resolution, as long as the user understands the implications of the interpolation process.Research QuestionsThe 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?InstallationThe recommended method for installing geosnap is withanaconda. In particular, we recommend themambaforge distributionmambainstall-cconda-forgegeosnapgeosnapis alaso available on PyPI and can be install with pip viapipinstallgeosnapUser GuideSee theUser Guidefor a
gentle introduction to usinggeosnapfor neighborhood researchAPI DocumentationSee theAPI docsfor a thorough explanation ofgeosnap's core functionalityDevelopmentgeosnap development is hosted ongithubTo get started with the development version,
clone this repository or download it manually thencdinto the directory and run the
following commands:condaenvcreate-fenvironment.yml
condaactivategeosnappipinstall-e.--no-depsThis will download the appropriate dependencies and install geosnap in its own conda environment.Bug reportsTo search for or report bugs, please see geosnap’sissuesLicense informationSee the file “LICENSE.txt” for information on the history of this software, terms &
conditions for usage, and a DISCLAIMER OF ALL WARRANTIES.CitationFor a generic citation of geosnap, we recommend the following:@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 onZenodoFundingThis project is supported by NSF Award #1733705,Neighborhoods in Space-Time Contexts