The goal of
qualmap is to make it easy to enter data from qualitative
qualmap provides a set of functions for taking qualitative GIS
data, hand drawn on a map, and converting it to a simple features
object. These tools are focused on data that are drawn on a map that
contains some type of polygon features. For each area identified on the
map, the id numbers of these polygons can be entered as vectors and
Version v0.2 brings a number of changes:
qm_verify()as a means for verifying data data previously saved to disk prior to processing them with
qm_summarize()that returns counts of participants rather than counts of clusters associated with each feature
COUNTfrom what is returned with
qm_create()no longer adds a custom class
qm_is_cluster()can be used to check for the appropriate characteristics of objects, but no longer checks the class itself
Qualitative GIS outputs are notoriously difficult to work with because
individuals’ conceptions of space can vary greatly from each other and
from the realities of physical geography themselves.
qualmap builds on
a semi-structured approach to qualitative GIS data collection.
Respondents use a specially designed basemap that allows them free reign
to identify geographic features of interest and makes it easy to convert
their annotations into digital map features. This is facilitated by
including on the basemap a series of polygons, such as neighborhood
boundaries or census geography, along with an identification number that
can be used by
qualmap. A circle drawn on the map can therefore be
easily associated with the features that it touches or contains.
qualmap provides a suite of functions for entering, validating, and
sf objects based on these hand drawn clusters and their
associated identification numbers. Once the clusters have been created,
they can be summarized and analyzed either within R or using another
This approach provides an alternative to either unstructured qualitative GIS data, which are difficult to work with empirically, and to digitizing respondents’ annotations as rasters, which require a sophisticated workflow. This semi-structured approach makes integrating qualitative GIS with existing census and administrative data simple and straightforward, which in turn allows these data to be used as measures in spatial statistical models.
More details on the package and how it fits into the broader ecosystem of qualitative GIS are available in a pre-print on SocArXiv. All data associated with the pre-print are also available on Open Science Framework, and the code are available via Open Science Framework and GitHub.
You should check the
website for the latest details on
installing dependencies for that package. Instructions vary
significantly by operating system. For best results, have
before you install
qualmap. Other dependencies, like
leaflet, will be installed automatically with
qualmap if they are
not already present.
The easiest way to get
qualmap is to install it from CRAN:
You can install the development version of
Github with the
# install.packages("remotes") remotes::install_github("slu-openGIS/qualmap")
qualmap implements six primary verbs for working with mental map data:
qm_define()- create a vector of feature id numbers that constitute a single “cluster”
qm_validate()- check feature id numbers against a reference data set to ensure that the values are valid
qm_preview()- plot cluster on an interactive map to ensure the feature ids have been entered correctly (the preview should match the map used as a data collection instrument)
qm_create()- create a single cluster object once the data have been validated and visually inspected
qm_combine()- combine multiple cluster objects together into a single tibble data object
qm_summarize()- summarize the combined data object based on a single qualitative construct to prepare for mapping
The primary vignette contains an overview of the workflow for implementing these functions.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.