geohexviz 1.0.2

A library for the visualization of hexagonally binned data sets.

Welcome to GeoHexViz!

Geospatial visualization is often used in military operations research to convey analyses to both analysts and decision makers. For example, it has been used to help commanders coordinate units within a geographic region [feibush2000a], to depict how terrain impacts vehicle performance [laskey2010a], and inform training decisions in order to meet mission requirements [goodrich2019a]. When such analyses include a large amount of point-like data, combining geospatial visualization and binning - in particular, hexagonal binning given its properties such as having the same number of neighbours as sides, the centre of each hexagon being equidistant from the centres of its neighbours, and that hexagons tile densely on curves surfaces [carr1992a] [sinha2019a] - is an effective way to summarize and communicate the data. Recent examples in the military and public safety domains include assessing the impact of infrastructure on Arctic operations [hunter2021a] and communicating the spatial distribution COVID-19 cases [shaito2021a] respectively.

However, creating such visualizations may be difficult for many since it requires in-depth knowledge of both Geographic Information Systems and analytical techniques, not to mention access to software that may require a paid license, training, and in some cases knowledge of a programming language such as Python or JavaScript. To help reduce these barriers, GeoHexViz - which produces publication-quality geospatial visualizations with hexagonal binning - is a Python package that provides a simple interface, requires minimal in-depth knowledge, and either limited or no programming. The result is an analyst being able to spend more time doing analysis and less time producing visualizations.

For more information of the design of GeoHexViz, see [abouzeidan2021a].

Example Usage

GeoHexViz allows a user to generate hexagonally binned geospatial visualizations with two different methods. Method 1 concerns using the GeoHexSimple package’s script to run a file containing plot structure. Method 2 concerns using Python code to interact with the functions within the package. Method 2 method has two categories:

1. Using the functions that the GeoHexSimple script uses

2. Using the PlotBuilder object from the GeoHexViz package

Please refer to the examples directory for additional examples that go into great depth (for both methods). Note that each example must be executed in its respective directory.

Method 1 Example Usage

The GeoHexViz distribution includes a module that can allow the reading of JSON files for quick and easy plots.

{
  "hexbin_layer": {
    "data": "<sample csv file>",
    "hex_resolution": 4
  },
  "output": {
    "filepath": "<sample filepath>",
    "width": 600,
    "height": 400
  },
  "display": true
}

Running the JSON script will allow you to input a JSON file via command-line. The GeoHexSimple command-line script was created using argparse and is very robust. Running the help command provides the following:

>geohexsimple --help
usage: geohexsimple [options]

Input plot property files to make hexagonally binned plots.

optional arguments:
  -h, --help            show this help message and exit
  -p PATH, --path PATH  path to json file or directory containing json files (required if no gui is used)
  -g, --gui             enable command-line gui (set to true if no path is provided)
  -nf, --nofeedback     turn off feedback while plotting
  -v, --verbose         whether to raise all errors or not

Running your plot properties file may look something like:

>geohexsimple --path <path to file>
exit

Or something like:

>geohexsimple

✨=================GeoHexSimple================✨
 A script for the simple creation of
 hexagonally binned geospatial visualizations.
✨=============================================✨
✨Main Menu✨
Please input the location of your parameterized
builder file (JSON, YAML) or a directory containing
builder files.
Options: file path, help, exit.
<path to file>

Method 2

As previously mentioned there are two ways to use the GeoHexViz library in Python code. Method 2a concerns using the functions that the GeoHexSimple script uses to create plots from pre-existing plot parameter files. Method 2b concerns using the functions from the GeoHexViz package to create plots.

Method 2a Example Usage

You can use the functions that the GeoHexSimple script uses to create a plot from a pre-existing plot parameter file. A simple example of this method is given below.

from geohexviz.utils.file import run_json

run_json("<filepath here>")

Method 2b Example Usage

You can use the functions and objects within GeoHexViz to create a plot from scratch. A simple example of this method is given below.

from pandas import DataFrame
from geohexviz.builder import PlotBuilder

# Creating an example dataset
inputdf = DataFrame(dict(
    latitude=[17.57, 17.57, 17.57, 19.98, 19.98, 46.75],
    longitude=[10.11, 10.11, 10.12, 50.55, 50.55, 31.17],
    value=[120, 120, 120, 400, 400, 700]
))

# Instantiating builder
builder = PlotBuilder()
builder.set_hexbin(inputdf, hexbin_info=dict(binning_fn='sum', binning_field='value'))

builder.finalize(raise_errors=False)
builder.display(clear_figure=True)

# A mapbox map
builder.set_mapbox('<ACCESS TOKEN>')
builder.finalize()
builder.display(clear_figure=True)

Behind the Scenes

When the hexbin layer is set, the data is processed in the following steps:

Data:

index	lats	lons	value
0	17.57	10.11	120
1	17.57	10.11	120
2	17.57	10.12	120
3	19.98	50.55	400
4	19.98	50.55	400
5	46.75	31.17	700

1. Coordinate columns are converted into geometry (if applicable)

index	value	geometry
0	120	POINT(17.57, 10.11)
1	120	POINT(17.57, 10.11)
2	120	POINT(17.57, 10.12)
3	400	POINT(19.98, 50.55)
4	400	POINT(19.98, 50.55)
5	700	POINT(46.75, 31.17)

2. Hex cells are then placed over the data

hex	value	geometry
83595afffffffff	120	POINT(17.57, 10.11)
83595afffffffff	120	POINT(17.57, 10.11)
83595afffffffff	120	POINT(17.57, 10.11)
835262fffffffff	400	POINT(19.98, 50.55)
835262fffffffff	400	POINT(19.98, 50.55)
831e5dfffffffff	700	POINT(46.75, 31.17)

(hex resolution = 3)

3. The data is grouped together by hex, and hex geometry is added

hex	items	value_field	geometry
83595afffffffff	(120,120,120)	360	POLYGON ((30.57051 46.80615, 30.47843 46.19931…
835262fffffffff	(400, 400)	800	POLYGON ((49.90903 20.19437, 49.74835 19.60088…
831e5dfffffffff		700	POLYGON ((9.44614 17.39197, 9.49704 16.75205, …

(binning function = sum of grouped values)

When the data is eventually plotted, a GeoJSON format of the data is passed alongside plotly properties are passed to the Plotly graphing library.

Installation

GeoHexViz requires the installation of GeoPandas, and this is most easily done through the use of Anaconda. Thus, to install GeoHexViz there are two options.

Option 1: Install from PyPI

This option requires the manual creation of a conda environment, installation of GeoPandas (GeoHexViz was developed with version 0.8.1 (build py_0)), and the installation of GeoHexViz from PyPI.

conda env create --name geohexviz python<=3.8
conda activate geohexviz
conda install -c conda-forge geopandas
pip install geohexviz

Option 2: Install from GitHub

This option requires that GeoHexViz be cloned from GitHub. Doing so will enable all dependencies, including GeoPandas, to be installed automatically.

git clone https://github.com/mrempel/geohexviz.git
cd geohexviz
conda env create -f environment.yml
conda activate geohexviz
python setup.py install

Further Documentation

The official documentation for GeoHexViz can be found at this page, in particular the API documentation for Python users. A Defence Research and Development Canada reference document has also been published alongside this package.

Limitations

This package uses GeoJSON format to plot data sets. With GeoJSON comes difficulties when geometries cross the 180th meridian . The issue appears to cause a color that bleeds through the entire plot and leaves a hexagon empty. In the final plot, this issue may or may not appear as it only occurs at certain angles of rotation. In this package a simple solution to the problem is implemented, in the future it would be best to provide a more robust solution. The solution that is used works generally, however, when hexagons containing either the north or south pole are present, the solution to the 180th meridian issue persists. This pole issue can be seen below.

There also exists some issues with the generation of discrete color scales under rare circumstances. These circumstances include generating discrete color scales with not enough hues to fill the scale, and generating diverging discrete colorscales with the center hue in a weird position. These issues have been noted and will be fixed in the near future.

There exists issues with the positioning and height of the color bar with respect to the plot area of the figure. Although the user is capable of altering the dimensions and positioning of the color bar, this should be done automatically as it is a common feature of publication quality choropleth maps.

Contributing

For major changes, please open an issue first to discuss what you would like to change. For more details, click here.

Citing

If you use geohexviz in your work, please cite our Defence Research and Development Canada report:

Abou Zeidan, T. & Rempel, M. (2021). GeoHezViz—Geospatial visualization using hexagonal binning software: Design reference and instruction manual. Defence Research and Development Canada, DRDC-RDDC-2021-D183. https://cradpdf.drdc-rddc.gc.ca/PDFS/unc381/p814091_A1b.pdf

Acknowledgements

Thank you to Nicholi Shiell for his input in testing, and providing advice for the development of this package.

Contact

For any questions, feedback, bug reports, feature requests, etc. please first present your thoughts via GitHub issues. For further assistance please contact [e-mail].

README References

[abouzeidan2021a]

Abou Zeidan, M. & Rempel, M. (2021). GeoHezViz—Geospatial visualization using hexagonal binning software: Design reference and instruction manual. Defence Research and Development Canada, DRDC-RDDC-2021-D183. https://cradpdf.drdc-rddc.gc.ca/PDFS/unc381/p814091_A1b.pdf

[feibush2000a]

Feibush, E., Gagvani, N., & Williams, D. (2000). Visualization for situational awareness. IEEE Computer Graphics and Applications, 20 (5), 38–45. https://doi.org/10.1109/38.865878

[laskey2010a]

Laskey, K. B., Wright, E. J., & da Costa, P. C. G. (2010). Envisioning uncertainty in geospatial information. International Journal of Approximate Reasoning, 51 (2), 209–223. https://doi.org/10.1016/j.ijar.2009.05.011

[goodrich2019a]

Goodrich, D. C., Heilman, P., Guertin, D., Levick, L. R., Burns, I., Armendariz, G., & Wei, H. (2019). Automated geospatial watershed assessment (AGWA) to aid in sustaining military mission and training. USDA-ARS Southwest Watershed Research Center (SWRC) Tucson United States. https://apps.dtic.mil/sti/citations/AD1092333

[carr1992a]

Carr, D. B., Olsen, A. R., & White, D. (1992). Hexagon mosaic maps for display of univariate and bivariate geographical data. Cartography and Geographic Information Systems, 19 (4), 228–236. https://doi.org/10.1559/152304092783721231

[sinha2019a]

Sinha, A. (2019). Spatial modelling tidbits: Honeycomb or fishnets? Towards Data Science. https://towardsdatascience.com/spatial-modelling-tidbits-honeycomb-or-fishnets-7f0b19273aab

[hunter2021a]

Hunter, G., Chan, J., & Rempel, M. (2021). Assessing the impact of infrastructure on arctic operations (Scientific Report DRDC-RDDC-2021-R024). Defence Research and Development Canada. https://cradpdf.drdc-rddc.gc.ca/PDFS/unc356/p812844_A1b.pdf

[shaito2021a]

Shaito, M., & Elmasri, R. (2021). Map visualization using spatial and spatio-temporal data: Application to COVID-19 data. The 14th Pervasive Technologies Related to Assistive Environments Conference, 284–291. https://doi.org/10.1145/3453892.3461336

Copyright and License

Copyright (c) His Majesty the King in Right of Canada, as represented by the Minister of National Defence, 2022.