The goal of qualmap
is to make it easy to enter data from qualitative maps. 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 transformed using qualmap
.
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()
qm_summarize()
that returns counts of participants rather than counts of clusters associated with each featureCOUNT
from what is returned with qm_create()
dplyr
v1.0 release:
qm_create()
no longer adds a custom classqm_is_cluster()
can be used to check for the appropriate characteristics of objects, but no longer checks the class itselfQualitative 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 creating 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 tool.
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 sf
package website for the latest details on installing dependencies for that package. Instructions vary significantly by operating system. For best results, have sf
installed before you install qualmap
. Other dependencies, like dplyr
and 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 qualmap
from Github with the remotes
package:
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 validqm_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 inspectedqm_combine()
- combine multiple cluster objects together into a single tibble data objectqm_summarize()
- summarize the combined data object based on a single qualitative construct to prepare for mappingThe 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.