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joss.05445/10.21105.joss.05445.crossref.xml diff --git a/joss.05445/10.21105.joss.05445.crossref.xml b/joss.05445/10.21105.joss.05445.crossref.xml new file mode 100644 index 0000000000..dabd9576b3 --- /dev/null +++ b/joss.05445/10.21105.joss.05445.crossref.xml @@ -0,0 +1,431 @@ + + + + 20231115T110333-798cf00fd86c9479c80f776f8c1079612a2e7010 + 20231115110333 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 11 + 2023 + + + 8 + + 91 + + + + lczexplore: an R package to explore Local Climate Zone +classifications + + + + Matthieu + Gousseff + https://orcid.org/0000-0002-7106-2677 + + + Erwan + Bocher + https://orcid.org/0000-0002-4936-7079 + + + Jérémy + Bernard + https://orcid.org/0000-0001-7374-5722 + + + Elisabeth Le Saux + Wiederhold + https://orcid.org/0000-0002-2079-8633 + + + + 11 + 15 + 2023 + + + 5445 + + + 10.21105/joss.05445 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10041206 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5445 + + + + 10.21105/joss.05445 + https://joss.theoj.org/papers/10.21105/joss.05445 + + + https://joss.theoj.org/papers/10.21105/joss.05445.pdf + + + + + + The map comparison kit + Visser + Environmental Modelling & +Software + 3 + 21 + 2006 + Visser, H., & De Nijs, T. (2006). +The map comparison kit. Environmental Modelling & Software, 21(3), +346–358. + + + Comparing global vegetation maps with the +Kappa statistic + Monserud + Ecological Modelling + 4 + 62 + 10.1016/0304-3800(92)90003-W + 1992 + Monserud, R. A., & Leemans, R. +(1992). Comparing global vegetation maps with the Kappa statistic. +Ecological Modelling, 62(4), 275–293. +https://doi.org/10.1016/0304-3800(92)90003-W + + + Implications of employing detailed urban +canopy parameters for mesoscale climate modelling: A comparison between +WUDAPT and GIS databases over Vienna, Austria + Hammerberg + International Journal of +Climatology + 38 + 10.1002/joc.5447 + 2018 + Hammerberg, K., Brousse, O., +Martilli, A., & Mahdavi, A. (2018). 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Bulletin of the American +Meteorological Society, 99(9), 1907–1924. +https://doi.org/10.1175/BAMS-D-16-0236.1 + + + A Coefficient of Agreement for Nominal +Scales + Cohen + Educational and Psychological +Measurement + 1 + 20 + 10.1177/001316446002000104 + 0013-1644 + 1960 + Cohen, J. (1960). A Coefficient of +Agreement for Nominal Scales. Educational and Psychological Measurement, +20(1), 37–46. +https://doi.org/10.1177/001316446002000104 + + + LCZ Generator: A Web Application to Create +Local Climate Zone Maps + Demuzere + Frontiers in Environmental +Science + 9 + 10.3389/fenvs.2021.637455 + 2296-665X + 2021 + Demuzere, M., Kittner, J., & +Bechtel, B. (2021). LCZ Generator: A Web Application to Create Local +Climate Zone Maps. Frontiers in Environmental Science, 9, 637455. +https://doi.org/10.3389/fenvs.2021.637455 + + + A generic algorithm to automatically classify +urban fabric according to the Local Climate Zone system: Implementation +in GeoClimate 0.0.1 and application to French cities + Bernard + 10.5194/egusphere-2023-371 + 2023 + Bernard, J., Bocher, E., Gousseff, +M., Leconte, F., & Le Saux Wiederhold, E. (2023). A generic +algorithm to automatically classify urban fabric according to the Local +Climate Zone system: Implementation in GeoClimate 0.0.1 and application +to French cities [Preprint]. Climate; Earth system modeling. +https://doi.org/10.5194/egusphere-2023-371 + + + Estimation of missing building height in +OpenStreetMap data: A French case study using GeoClimate +0.0.1 + Bernard + 10.5194/gmd-2021-428 + 2022 + Bernard, J., Bocher, E., Le Saux +Wiederhold, E., Leconte, F., & Masson, V. (2022). Estimation of +missing building height in OpenStreetMap data: A French case study using +GeoClimate 0.0.1 [Preprint]. Climate; Earth system modeling. +https://doi.org/10.5194/gmd-2021-428 + + + A geoprocessing framework to compute urban +indicators: The MApUCE tools chain + Bocher + Urban Climate + 24 + 10.1016/j.uclim.2018.01.008 + 2018 + Bocher, E., Petit, G., Bernard, J., +& Palominos, S. (2018). A geoprocessing framework to compute urban +indicators: The MApUCE tools chain. Urban Climate, 24, 153–174. +https://doi.org/10.1016/j.uclim.2018.01.008 + + + Simple features for r: Standardized support +for spatial vector data + Pebesma + The R Journal + 1 + 10 + 10.32614/RJ-2018-009 + 2018 + Pebesma, E. (2018). Simple features +for r: Standardized support for spatial vector data. The R Journal, +10(1), 439–446. +https://doi.org/10.32614/RJ-2018-009 + + + Mapping europe into local climate +zones + Demuzere + PloS one + 4 + 14 + 2019 + Demuzere, M., Bechtel, B., Middel, +A., & Mills, G. (2019). Mapping europe into local climate zones. +PloS One, 14(4), e0214474. + + + A global map of local climate zones to +support earth system modelling and urban scale environmental +science + Demuzere + Earth System Science Data +Discussions + 2022 + 10.5194/ems2022-83 + 2022 + Demuzere, M., Kittner, J., Martilli, +A., Mills, G., Moede, C., Stewart, I. D., Vliet, J. van, & Bechtel, +B. (2022). A global map of local climate zones to support earth system +modelling and urban scale environmental science. Earth System Science +Data Discussions, 2022, 1–57. +https://doi.org/10.5194/ems2022-83 + + + Combining expert and crowd-sourced training +data to map urban form and functions for the continental +US + Demuzere + Scientific data + 1 + 7 + 10.1038/s41597-020-00605-z + 2020 + Demuzere, M., Hankey, S., Mills, G., +Zhang, W., Lu, T., & Bechtel, B. (2020). Combining expert and +crowd-sourced training data to map urban form and functions for the +continental US. Scientific Data, 7(1), 264. +https://doi.org/10.1038/s41597-020-00605-z + + + + + + From c6d90618b3f2ef9e008d974aaa2b994f1460b298 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Wed, 15 Nov 2023 11:03:55 +0000 Subject: [PATCH 03/13] Creating 10.21105.joss.05445.jats --- joss.05445/10.21105.joss.05445.jats | 1319 +++++++++++++++++++++++++++ 1 file changed, 1319 insertions(+) create mode 100644 joss.05445/10.21105.joss.05445.jats diff --git a/joss.05445/10.21105.joss.05445.jats b/joss.05445/10.21105.joss.05445.jats new file mode 100644 index 0000000000..873de18a0e --- /dev/null +++ b/joss.05445/10.21105.joss.05445.jats @@ -0,0 +1,1319 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5445 +10.21105/joss.05445 + +lczexplore: an R package to explore Local Climate Zone +classifications + + + +https://orcid.org/0000-0002-7106-2677 + +Gousseff +Matthieu + + + + +https://orcid.org/0000-0002-4936-7079 + +Bocher +Erwan + + + + +https://orcid.org/0000-0001-7374-5722 + +Bernard +Jérémy + + + + +* + + +https://orcid.org/0000-0002-2079-8633 + +Wiederhold +Elisabeth Le Saux + + +* + + + +CNRS, Lab-STICC, UMR 6285, Vannes, France + + + + +University of Savoie Mont-Blanc, LOCIE, UMR 5271, +France + + + + +Université Bretagne Sud, Lab-STICC, UMR 6285, Vannes, +France + + + + +University of Gothenburg, Department of Earth Sciences, +Sweden + + + + +* E-mail: +* E-mail: + + +1 +4 +2023 + +8 +91 +5445 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +R SOFTWARE +climate +environment +GIS +spatial analysis +cities + + + + + + Summary +

Climate change is a growing concern for city planners as Urban Heat + Islands have an impact on mortality + (Clarke, + 1972), health in general + (Lowe, + 2016) and consumption of energy for building cooling + (Malys + et al., 2012) among other effects. A first step towards large + scale study of urban climate is to define classes based on logical + division of the landscape, such as Local Climate Zones (LCZ) defined + by Stewart & Oke + (2012).

+

A spatial comparison is performed by producing agreement maps + between classifications, agreement statistics and a confusion matrix + to help qualify and quantify the misclassifications.

+

The lczexplore package aims at comparing different LCZ + classifications, but can be used to compare any pair of + classifications on geographical units.

+

This software is available as a free and open source R package.

+
+ + Statement of need + + Comparing maps +

As stated in + (Visser + & De Nijs, 2006) comparing maps is an important issue in + environmental research. The four main reasons to compare categorical + variables on geographical units are:

+ + +

to assess the differences between maps generated by models + under different scenarios and assumptions,

+
+ +

to detect temporal changes,

+
+ +

to calibrate or validate models,

+
+ +

to perform uncertainty and sensitivity analysis.

+
+
+
+ + Comparing specifically LCZ maps +

Climate change is a growing concern for city planners with a + special focus on Urban Heat Island phenomenons. The terms + rural and urban lack of a clear + definition and different classifications of urban and rural + landscapes were proposed. Stewart & Oke + (2012) + defined an approach that complies with criteria of geographical + classification defined in Grigg + (1965). + Their Local Climate Zones (LCZ) are based on a logical segmentation + of land-use and urban canopy parameters and define 10 urban types + and 7 land cover types.

+

LCZ have gained popularity in the past decade as they sum up + relevant information and can help, for instance, apprehend the + intensity of the Urban Heat Island + (Kotharkar + & Bagade, 2018).

+

Several methods aim to classify a territory into LCZ, but only + few workflows allow an automatic classification for any given area. + Quan & Bansal + (2021) + distinguishes two main streams of production of these LCZ:

+ + +

the raster stream processes remotely sensed information, and + applies machine learning algorithms trained using local experts’ + knowledge. In this way, the WUDAPT community + (Ching + et al., 2018) produced thousands of city-based LCZ maps, + accessible via the LCZ Generator + (Demuzere + et al., 2021), but also large-scale maps for Europe, the + continental United States and the whole world + (Demuzere + et al., 2019, + 2020, + 2022).

+
+ +

the vector stream uses Geographic Information System (GIS) + layers that represent the main topographic features, defines + spatial units, computes urban canopy parameters and uses them to + classify spatial units into LCZ. For instance, the GeoClimate + geospatial toolbox produces LCZ classifications from + OpenStreetMap or french BDTopo data + (Bocher + et al., 2021).

+
+
+

The existence of several methods to produce LCZ classifications, + or the use of a method with different input data, raises the need + for a tool to quickly get:

+ + +

Statistics measuring the general agreement between two + classifications

+
+ +

A spatial representation to allow a fast visual assessment of + the zones where the classifications agree or disagree.

+
+ +

A confusion matrix, to explore how the levels of one + classification break up in levels of the other classification, + shown in a graphical way to help visualize the main differences + between the classifications.

+
+
+
+
+ + State of the field and feature comparison + + State of the field on spatial classification + comparison +

The question of going beyond the simple visual observation of + differences between maps is not a new one. A first approach would + be, on raster maps, to simply compute the agreement between two maps + as the proportion of pixels for which the two maps have the same + value of the variable of interest. This approach is often sufficient + to help specialists compare pairs of maps, but it has two main + drawbacks:

+ + +

two totally random maps won’t have a value of agreement of + zero, as some pixel values may agree by chance,

+
+ +

only raster maps where pixels match perfectly (same size, not + translated) can be treated, or some pre-treatment are needed + (like nearest neighbours interpolation for instance).

+
+
+

To prevent the first drawback, Monserud & Leemans + (1992) + proposed the use of Cohen’s kappa coefficient of agreement for + nominal scales + (Cohen, + 1960). Cohen’s Kappa allow to assess how two raters agree or + differ in the task of rating individual. In our case, each pixel is + seen as an individual, each map is seen as a rater and the value of + the raster at each pixel is seen as the rate.

+

The comprehensive Map Comparison kit, which was released in 2001 + by the Netherlands Environmental Assessment Agency + (Visser + & De Nijs, 2006), is an example of a tool that provides + multiple methods to compare raster maps. It includes a fuzzy + algorithm which allows to tackle small shifts of one map from + another. It only works on raster maps, only on Windows OS and + doesn’t allow automation for several pairs of maps.

+

To tackle the second drawback, one can rasterize vector files, + for instance by applying a regular grid to a vector layer and assign + to each pixel the value of the vector geometry most present in the + pixel (in terms of area of the intersection). This can produce + severe effect of smoothing one may want to prevent.

+

Other approaches also rely on comparing the distribution of some + variables of interest. For instance + (Hammerberg + et al., 2018), rather than comparing the maps, compare some + model output variables using the data of the map.

+
+ + State of the field on Local Climate Zone maps + comparison. +

As Local Climate Zones emerged as a new standard for + characterizing urban landscapes + (Demuzere + et al., 2021), specific tools are needed to allow easy + automation of LCZ maps. As a matter of fact, “ground truth” is hard + to define in matters of LCZs, and one may want to compare LCZs + produced by experts and by different algorithms.

+

The very definition of LCZs by Stewart & Oke + (2012) + relies on ranges of values for several indicators, and sometimes + several LCZ types could be assigned to the corresponding geometries. + Therefore, one may associate a confidence value to the LCZ type and + check if filtering pixels (for raster data) or geometries (for + vector data) accordingly to a confidence threshold has an impact on + the way maps agree or disagree.

+

A comparison of a raster stream result and a GIS vector stream + approach was proposed by Muhammad et al. + (2022). + This comparison relies on rasterising vector data. It uses several + tools: QGIS, python scripts and SAGA GIS. As far as we know, the + scripts and the automation of the method are not publicly + available.

+

The need for automation of map comparison (both raster and + vector), and specific features (like standard colors or legends for + LCZs, or sensitivity analysis according to confidence) justified the + development of lczexplore.

+
+ + Features + + General workflow and processing steps +

The lczexplore package is a set of + R functions to import, visualize, group and + compare LCZ classifications, even when they don’t use the same + spatial units to classify the area. These LCZ classifications can + come from vector layers or raster layers (the latter will be + vectorized by the dedicated import function)

+ +

Workflow and main functions of the + package

+ +
+

Figure 1 describes the general workflow of the package.

+

Main functions are presented in plain lines, the dashed boxes + and arrows represent optional steps. These functions are presented + in detail in the next section, and they allow the following steps + of exploration:

+ + +

The LCZ classifications (or any other qualitative + variables) are imported from a file (GeoJSON or Shapefile + format)

+
+ +

Each LCZ classification can then be visualized

+
+ +

Some LCZ levels may be grouped in broader categories

+
+ +

A pair of LCZ classifications (or qualitative variable + maps) can then be compared:

+ + +

a map of agreement/disagreement is produced,

+
+ +

the general agreement and a pseudo-kappa indicator of + agreement are computed,

+
+ +

the summed surface of each LCZ type is computed for + each classification,

+
+ +

a confusion matrix shows how the levels of one LCZ + classification break up into the levels of the other

+
+
+
+ +

Influence of the level of confidence on the agreement + between classifications is performed (sensitivity + analysis)

+
+
+

All the steps of the analysis can be easily automated in R, for + instance on several cities at a time.

+
+
+ + Description of the main functions +

The main functions are presented in this section. More details + about their arguments and output can be found in the minimal example + section.

+ + From import to comparison +

The importLCZvect function allows to + import a LCZ classification from a GeoJSON or a Shapefile. + Geometries and LCZ types are needed, but one can also optionally + load unique identifiers and a confidence level for the LCZ type of + each geometry.

+

The importLCZraster function allows the + import from a raster map, and the user has to feed it a GeoTIFF + and a bounding box of the area of interest.

+

The import functions output objects of class + simple feature, as defined by the openGIS + consortium and as handled by the sf + package, an R reference library for Geographical Information + System Vector Data + (Pebesma, + 2018).

+

The compareLCZ function creates + intersected geometries on which classifications either totally + agree or totally disagree, as seen in the figure 2, and this + prevents artificial rounding effects that may arise with a + rasterisation approach.

+ +

Intersecting geometries to have full agreement or + disagreement +

+ +
+

Next, the function computes:

+ + +

the area of each LCZ type for each classification,

+
+ +

the percentage of area on which the two classifications + agree, and a map of this agreement,

+
+ +

a pseudo kappa statistics which evaluates agreement beyond + how two random maps may agree,

+
+ +

a confusion matrix of how the types of one LCZ + classification break up in the types of the other, and a + graphic of this, with an indicator of the summed area for each + LCZ type (useful if the two classifications disagree on a type + almost absent of the area).

+
+
+

The output of these functions are shown in the minimal example + section.

+

With the standard representation, + comparing LCZ maps is made easy by a default setting of legends + and colors. The alter representation allows + the user to deal with regrouped LCZ categories or any type of + qualitative variables. Levels can either be specified by the user + or deduced from the data, colors can either be defined by the user + or chosen from a random palette.

+
+ + Class levels grouping +

It is sometimes useful to group some LCZ types, for instance to + create a broader category for all the urban LCZ types and another + for landscape LCZ types, or to group the levels with similar + estimated impact on an urban heat island intensity.

+

The groupLCZ function allows the user to + specify the LCZ types one wants to group together, the names of + the new resulting categories and their corresponding colors. One + can then feed compareLCZ function these new + groups, setting repr="alter", and + specify desired levels and colors.

+ +

Grouping of LCZ types into larger categories +

+ +
+
+ + Sensitivity analysis +

Some algorithms add a uniqueness value to the LCZ type it + assigns to a spatial unit. It can measure if another LCZ level + could have been assigned to this unit. Thus, it can be seen as a + confidence value of the LCZ type. The + lczexplore package allows a sensitivity + analysis according to this level of confidence, in order to answer + the question: does keeping only geometries with a higher + confidence value make the degree of agreement between two + classifications higher?

+

This sensitivity analysis is performed considering all LCZ + types and within each individual LCZ type.

+ +

Sensitivity analysis according to confidence for all + LCZ levels +

+ +
+

The agreement between classifications for the geometries with a + confidence level higher than the threshold, and their numbers, are + plotted in blue. The agreement and the numbers of geometries under + the threshold are plotted in magenta. On this example, one can see + that ditching geometries that have a confidence level lower than + 0.5 increases the agreement to more than 90%. The curve then tends + to flatten, and the number of kept geometries decreases a lot + (from 602 to 122). One also needs to notice that on this example, + most geometries didn’t have a confidence value (7476 with a + general agreement of 59.21%)

+
+ + Import and explore categorical variable (other than LCZ + classifications) +

This package focuses on LCZ maps comparison, but more often + than not, people working on LCZ maps also describe their area of + interest with other categorical indicators. The workflow of + comparison of LCZ maps can be used for any pair of maps of + categorical variables, under certain limitations:

+ + +

there must not be more than 36 levels for the categorical + variable to explore,

+
+ +

the associated geometries must be (multi) polygons or + easily converted to them, (typically, the package would not be + suitable to compare road characterization),

+
+ +

the geometries must be topographically valid (this is also + true for LCZ).

+
+
+

The importQualVar function allows the + import of such variables on (multi-) polygons maps. It outputs an + sf object that can be fed to the other main functions of the + package (showLCZ, + compareLCZ, + groupLCZ…).

+
+
+
+ + Coding implementation +

lczexplore is an R package, all its specific + functions are coded in R language. It relies on state-of-the art + packages:

+ + +

geographical computation requires the + sf package for vector data and + the terra package for raster + data,

+
+ +

data management mainly requires the following packages: + dplyr, tidyr, forcats, rlang + and methods packages,

+
+ +

graphical production uses + ggplot2, grDevices, cowplot + and RColorBrewer,

+
+ +

tests need the tinytest + package.

+
+
+

Every step corresponds to an R function (see the workflow on figure + 1 for the name of the main functions).

+

Every function has an associated file for unitary testing.

+
+ + A minimal example + + Install and load +

The lczexplore package can be downloaded from + https://github.com/orbisgis/lczexplore and + installed with the command + install_github:

+ library(devtools) +devtools::install_github("orbisgis/lczexplore", build_vignettes = TRUE) +library(lczexplore) +

In order to discover the package, vignettes can be browsed via + browseVignettes("lczexplore") or by + calling them directly:

+ vignette("lczexplore_en") +vignette("lczexplore_raster_vector") +vignette("lczexplore_alter") +
+ + Import the data +

This example uses two LCZ classifications of French Redon city, + produced using a single LCZ classification method (the GeoClimate + workflow), with two different input data: OpenStreetMap and the 2.2 + version of the French BDTopo.

+ +# Set the path to the example data folder created once you install the package + +dirPath<-paste0(system.file("extdata",package="lczexplore"),"/") +dirPathOSM<-paste0(dirPath,"osm/2022/Redon") +dirPathBDT<-paste0(dirPath,"bdtopo_2_2/Redon") + +# Import into an sf object the data produced with GeoClimate and +# the OpenStreetMap data (city of Redon) +redonOSM<-importLCZvect( + dirPath=dirPathOSM, + file="rsu_lcz.geojson", column = "LCZ_PRIMARY", geomID="ID_RSU", + confid="LCZ_UNIQUENESS_VALUE") + +# Import into an sf object the data produced with GeoClimate and +# the french BDTopo V2.2 data (city of Redon) +redonBDT<-importLCZvect( + dirPath=dirPathBDT, + file="rsu_lcz.geojson", column = "LCZ_PRIMARY",geomID="ID_RSU", + confid="LCZ_UNIQUENESS_VALUE") +
+ + Visualize the data +

To visualize a LCZ classification, use the + showLCZ function. Setting + repr to + "standard" directly deals with + standard values of LCZ levels and colors.

+ +# Plot the LCZ levels on the Redon Area using the default color set +# from the sf object produced by importLCZvect function +map1<-showLCZ( + sf=redonOSM, column="LCZ_PRIMARY", repr="standard", + title="LCZ Classification on the French City of Redon") + +

The result is a map of the Local Climate Zones on the area:

+ +

Local Climate Zones for Redon city based on the + GeoClimate workflow applied to OSM data +

+ +
+
+ + Compare the two LCZ classifications +

To compare the two loaded LCZ classifications, use the + compareLCZ function.

+ + +

the sf1 and + column1 arguments allow to specify the sf + dataset of the first map, and the column in which the LCZ levels + are stored (same for sf2 and + column2),

+
+ +

the wf1 and wf2 + arguments take strings to specify the workflows used to produce + the input data.

+
+ +

The location argument allows to name + the area of interest covered by input datasets. The 3 previous + arguments will be parsed in the legends of the graphics (except + if title is specified) and the name of + potential csv output files,

+
+ +

when exWrite=TRUE, the data computed + to create the confusion matrix will be written in a csv file in + the working directory,

+
+ +

if saveG=TRUE, the four plots created + by the function will be written in a png file in the working + directory.

+
+
+ # Compare how the BDTopo and the OpenStreetMap Data produce different classifications. +# The outputs are stored in a list. + +comparison<-compareLCZ(sf1=redonBDT,column1="LCZ_PRIMARY", wf1="BDTopo v2.2", + sf2=redonOSM, column2="LCZ_PRIMARY", wf2="OpenStreetMap", + ref=1, repr="standard", exwrite=F, location="Redon") + +

All graphics are concatenated for a quick glance.

+ +

Local Climate Zones comparison for the French city of + Redon based on the GeoClimate workflow applied to OpenStreetMap + and BDTopo data +

+ +
+

The first and second maps show the spatial distribution of the + first and second LCZ classifications levels, respectively. The third + map shows where the two classifications agree or disagree. The last + graphic is a confusion matrix: how the LCZ types of the first + classification break-up in those of the second, in percentage of the + surface.

+

CompareLCZ outputs a list called + matConfOut which contains:

+ + +

$data, intersected geometries, their + identifiers and associated confidence value (if fed to the + function), their LCZ type and their area,

+
+ +

$areas, the summed area for each LCZ + for both classifications,

+
+ +

$matConfLarge, the confusion + matrix,

+
+ +

$percAgg, the general agreement of the + two classification on the whole area (as a percentage of the + global surface of the area),

+
+ +

$pseudoK, the value of a heuristic + pseudo Cohen’s Kappa coefficient of agreement.

+
+
+

The value pseudoK corresponds to a Kappa, + where the cells of the cross tabulation would be weighted by the + percentage of the concerned surface.

+

One can see in a quick glance, that the BDTopo workflow leads to + more and smaller Spatial Reference Units (1553 RSU) than the + OpenStreetMap workflow (521 RSU). The third map shows that while the + classifications tend to agree on wide vegetation areas, there seem + to be more some discrepancies in the urban areas. The last graphics + helps to refine this analysis: one can notice that both workflows + quite agree for “compact mid” and “low plants” LCZ levels (89% and + 88 % of the concerned areas). On the contrary, only 48% of the areas + set to “dense trees” by BDTopo workflow are also set to “dense + trees” by OpenStreetMap workflow, while 37% of these areas are set + as “low plants.”

+

In the same way, 73% of the areas set to “compact low” by BDTopo + workflow are set to “compact mid” by OpenStreetMap workflow. This is + coherent with the fact that building heights are often missing on + OpenStreetMap and that the algorithm that GeoClimate uses to predict + them is less precise than the available information of BDTopo. One + may read more in Bernard et al. + (2023) + about differences between these workflows.

+
+ + Study the impact of confidence on agreement +

The confidSensib function makes a + sensitivity analysis of the agreement according to the threshold of + confidence above which one keeps the geometries of the compared + maps. To perform this analysis, the confidence value and the + identifier of each geometry must be fed to the comparison function. + The $data slot is then passed to + confidSensib. The + saveG allows to set the path to a folder + where resulting graphics will be saved.

+

The values of confidence for which the agreement will be computed + are determined by the range of confidence values present in the data + and by the number of points, set by the + nPoints argument.

+ # Path to the folder where compareLCZ stored output data file +# mainPath<-system.file("extdata", package = "lczexplore") +# (not executed to avoid useless files in the package) + + +# Specification of geomID and confid columns for both dataset, +# as confidSensib needs them in the next step + +comparison<-compareLCZ( + sf1=redonBDT,column1="LCZ_PRIMARY", wf1="BDTopo v2.2", + geomID1="ID_RSU", confid1="LCZ_UNIQUENESS_VALUE", + sf2=redonOSM, column2="LCZ_PRIMARY", wf2="OpenStreetMap", + geomID2="ID_RSU", confid2="LCZ_UNIQUENESS_VALUE",ref=1, + repr="standard", exwrite=F, location="Redon") + +testSourceFact<-comparison$data + +# Sensitivityanalysis on this compareLCZ output + +sensitAnalysis<-confidSensib(inputDf=comparison$data, filePath="", + nPoints=5, wf1="bdtopo_2_2", wf2="osm", + geomID1="ID_RSU", column1="LCZ_PRIMARY", confid1="LCZ_UNIQUENESS_VALUE", + geomID2="ID_RSU.1",column2="LCZ_PRIMARY.1", confid2="LCZ_UNIQUENESS_VALUE.1", + plot=TRUE, saveG="") # one would use saveG = mainPath to write the results in + # the mainPath folder + +

Figure 7 shows the resulting graphics for the agreement per LCZ + type (types not present in the dataset are excluded).

+ +

Sensitivity analysis according to confidence by LCZ + levels

+ +
+

For each LCZ type, the x-axis shows the minimum confidence + threshold. The y-axis shows the agreement levels. Each cyan point + shows the average agreement between classifications for spatial + units where the minimum confidence value is greater than the x-axis + value.

+

As one wants to be sure the dropped units didn’t have a greater + agreement, they are showed as magenta triangles. For this particular + example, it seems that for the “Open Low” LCZ type, the greater the + confidence, the smaller the agreement!

+

This unexpected result is explained by the fact that in our case, + the confidence qualifies the method, not the data. In this example, + in the OSM dataset which is compared, the building height are mostly + estimated using a statistical method + (Bernard + et al., 2022) as stated above.

+
+ + Group some levels and perform the same analysis +

The groupLCZ function aggregates chosen + levels into broader categories. For each category, a vector is + needed, which name is the name of the broader category to create and + which values are the levels to be grouped in this new category.

+ redonOSMgrouped<-groupLCZ(redonOSM,column="LCZ_PRIMARY", + urban=c("1","2","3","4","5","6","7","8","9"), + industry="10", + vegetation=c("101","102","103","104"), + impervious="105", + pervious="106", + water="107", + colors=c("red","black","green","grey","burlywood","blue")) + +redonBDTgrouped<-groupLCZ(redonBDT,column="LCZ_PRIMARY", + urban=c("1","2","3","4","5","6","7","8","9"), + industry="10", + vegetation=c("101","102","103","104"), + impervious="105", + pervious="106", + water="107", + colors=c("red","black","green","grey","burlywood","blue")) + +

By default, the column where the new categories are stored is + named "grouped", but it can be set + with the argument outCol.

+

It is the possible to perform the same analysis as on standard + LCZ classifications, as shown in the following example:

+ map2<-showLCZ(redonOSMgrouped, column="grouped",repr="alter", + title="Regrouped Categories for the city of Redon", + LCZlevels = c("urban","industry","vegetation","impervious","pervious","water"), + colors=c("red","black","green","grey","burlywood","blue")) +compareLCZ(sf1=redonOSMgrouped, column1="grouped", wf1="OpenStreetMap data", + sf2=redonBDTgrouped, column2="grouped", wf2="BDTopo data",ref=1, + repr="alter",exwrite=F,location="Redon",saveG="", + LCZlevels = c("urban","industry","vegetation","impervious","pervious", + "water"), + colors=c("red","black","green","grey","burlywood","blue")) + +

Map of grouped levels on the city of Redon +

+ +
+

You can also map any qualitative variable using this grouped + representation, as long as you specify the expected levels the same + way.

+
+ + An example of qualitative variables +

The function importQualVar allows you to + import maps of qualitative variables on (multi)polygons. For + instance, classifications of urban tissue called Urban Typology by + Random Forest + (Bocher + et al., 2018) are available as examples in this package.

+

After the import, the usual functions can be used, as shown in + the following code:

+ utrfRedonBDT<-importQualVar(dirPath=paste0( + system.file("extdata", package = "lczexplore"), "/bdtopo_2_2/Redon"), + file="rsu_utrf_area.geojson", column="TYPO_MAJ") + +map3<-showLCZ(sf=utrfRedonBDT, column="TYPO_MAJ",repr="alter", + title = " UTRF classification of the French city of Redon") + +

An example of some qualitative variable: Urban + Typology by Random Forest (UTRF)

+ +
+

Figure 9 represents the UTRF classification of the French city of + Redon, as computed by the GeoClimate workflow on BDTopo v2.2 data. + The colors were not specified and therefore were randomly picked + from a palette.

+ utrfRedonOSM<- + importQualVar( + dirPath=paste0( + system.file("extdata", package = "lczexplore"), + "/osm/2022/Redon"), + file="rsu_utrf_area.geojson", column="TYPO_MAJ", + geomID="ID_RSU", confid="UNIQUENESS_VALUE") + +utrfComparison<- + compareLCZ(sf1=utrfRedonBDT, column1="TYPO_MAJ",wf1=" UTRF BDTopo", + sf2=utrfRedonOSM, column2="TYPO_MAJ", wf2 = " UTRF OpenStreetMap", + location = " Redon",exwrite=FALSE,repr="alter") + # Plot the confusion matrix of thes two classifications + +

Example of comparison on a qualitative variable + (UTRF)

+ +
+
+
+ + Licensing, availability and contribution +

lczexplore is licensed under the GNU Lesser General Public License + (v3.0), with all source code stored at GitHub. In the spirit of honest + and open science, requests and suggestions for fixes, feature updates, + as well as general questions and concerns are encouraged via direct + interaction with contributors and developers by filing an issue while + respecting Contribution Guidelines.

+
+ + Research projects involving lczexplore +

The lczexplore package was developed thanks to the project PÆNDORA2 + (Pour la gestion du confort estival: Donnees, Outils et + Recherche-Action) (2022 -2025), funded by ADEME.

+
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