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.
qualmap
makes use of tidy evaluation, meaning that key and category references may be
either quoted or unquoted (i.e. bare).
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 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.
There are six key verbs introduced in qualmap
:
define: create a vector of feature id numbers that constitute a single "cluster"
validate: check feature id numbers against a reference data set to ensure that the values are valid
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)
create: create a single cluster object once the data have been validated and visually inspected
combine: combine multiple cluster objects together into a single tibble data object
summarize: summarize the combined data object based on a single qualitative construct to prepare for mapping
Useful links: