Article
The Human Influence Experiment (Part 2): Guidelines
for Improved Mapping of Local Climate Zones Using
a Supervised Classification
Marie-leen V erdonck 1 , Matthias Demuzere 1 , Benjamin Bechtel 2 , Christoph Beck 3 ,
Oscar Brousse 4 , Arjan Droste 5 , Daniel Fenner 6 , François Leconte 7 and
Frieke V an Coillie 1, *
1 Department of Environment, Faculty of Bioscience Engineering, Ghent Univer sity , 9000 Gent, Belgium;
[email protected] (M.-l.V .); [email protected] (M.D.)
2 Center for Earth System Research and Sustainability , University of Hamburg, 20146 Hamburg, Ge rmany;
[email protected]
3 Institute of Geography , University of Augsburg, 86159 Augsbur g, Germany;
[email protected]
4 Department of Earth and Environmental Sciences, KU Leuven, 3001 Leuven, Belgium;
oscar [email protected]
5 Meteorology and Air Quality Section, W ageningen University , 6708 W ageningen, The Netherlands;
arjan.droste@wur .nl
6 Institute of Ecology , T echnische Universität Berlin, 12165 Berlin, Germany; [email protected]
7 Université de Lorraine, LERMaB, 88000 Epinal, France; [email protected]
* Correspondence: Frieke.V [email protected]
Received: 18 December 2018; Accepted: 19 February 2019; Published: 28 February 2019
       
  

Abstract:
Since 2012, Local Climate Zones (LCZ) have been used for numerous studies r elated to
urban envir onment. In 2015, this use amplified because a method to map urban ar eas in LCZs was
intr oduced by the W orld Urban Database and Access Portal T ools (WUDAPT). However in 2017, the
first HUMan INfluence EXperiment showed that these maps often have poor or low quality . Since the
maps ar e used in dif ferent applications such as urban modelling and land use/land cover change
studies, it is of the utmost importance to impr ove mapping accuracies and a second experiment was
launched. In HUMINEX 2.0, the focus lies on providing guidelines on the use of the mapping pr otocol
based on the r esults of both HUMINEX 1.0 and 2.0. The results showed that: (1) it is important to
follow the mapping pr otocol as strictly as possible, (2) a r easonable amount of time should be spent
on the mapping pr ocedur e, (3) all users should perform a driving test, and (4) training area sets
should be stor ed in the WUDAPT database for other users.
Keywords: WUDAPT ; crowdsour cing; classification; experimental setup
1. Introduction
Mapping of urban ar eas in r elation to their urban climate is gaining inter est in the global resear ch
community [
1
]. Even though most urban climate models r equir e a detailed description of the urban
envir onment, until today , no global urban classification scheme useful for urban climate exists. In 2012,
the Local Climate Zone (LCZ) scheme was intr oduced by Stewart and Oke
[ 2 ]
. This scheme consist of
17 zones, divided into ten built and seven natural land cover types (Figur e 1 ). These zones portray
a unique air temperatur e regime at scr een height, under similar atmospheric conditions [
3
] and
could thus serve as a global urban classification scheme. Befor e 2015, the LCZ scheme was mainly
used as a conceptual framework to evaluate in-situ measurement sites r elated to urban heat island
Urban Sci. 2019 , 3 , 27; doi:10.3390/urbansci3010027 www .mdpi.com/journal/urbansci

Urban Sci. 2019 , 3 , 27 2 of 16
r esearch [
2
]. In 2015, a method [
4
,
5
] was pr esented within the W orld Urban Database and Access
Portal T ools (WUDAPT) initiative, to classify major urban ar eas into spatially explicit LCZ maps and
gather information on the internal structur e and texture of cities [
4
,
6
,
7
]. The data gathering process is
or ganized into a hierarchy based on the level of detail. The default WUDAPT level-0 fundamentally
r elies on supervised classification of Landsat satellite scenes into LCZ types based on training areas
(T As) that are cr eated by urban experts who identify parts of the urban landscape that exemplify each
type pr esent in a city [
4
]. Thus, WUDAPT is an example of crowd-sour cing geographic information,
also r eferred to as volunteer ed geographic information [
8
] and citizen science, among other terms
r elated to user-generated content [ 9 ].
Figure 1.
Urban (
1
–
10
) and natural (
A
–
G
) LCZ types and their characteristics (adapted from T able 2 in
Stewart and Oke
[ 2 ]
, text shortened, icons r eworked) B: Buildings; C: cover; M: materials; F: function;
T all: > 10 stories, Mid-rise: 3–9 stories, Low: 1–3 stories.
At the 10th International Confer ence on Urban Climate in 2018, it became clear that LCZ maps
ar e now regar ded as a global refer ence for urban land cover descriptions [
10
]. Since LCZ maps ar e
intended for and alr eady used in a range of differ ent applications, such as climate models at various
scales [
11
–
20
], land use change investigations [
6
], or the characterization of several hundreds of
cr owd-sourced citizen-weather stations in terms of their local-scale surr oundings [
21
], ther e is a clear
need for highly accurate classification r esults [
22
]. However , it is unclear what determines the final
quality of a LCZ map derived with the WUDAPT level-0 methodology . Hence in 2017, the Human
Influence Experiment (HUMINEX) was intr oduced to investigate the variability of the quality of LCZ
maps, produced by dif ferent individuals using the WUDAPT methodology [
23
]. It aimed at identifying
how lar ge discrepancies between individual LCZ maps can be for a given city or r egion. In HUMINEX
1.0, about 120 students from six universities classified a total of twelve cities. The experiment pr ovided
several inter esting and relevant insights, namely that some specific LCZ classes can be easily identified,
or that the iterative scheme set up in the WUDAPT methodology is justified [
23
]. However , the
r esults of HUMINEX 1.0 also clearly highlighted that LCZ maps are often of overall poor to moderate
quality . Best quality of LCZ maps for differ ent cities was observed when multiple training sets fr om
dif ferent participants wer e combined, thus indicating a certain “wisdom of the crowd” [
23
]. Beside
these findings in HUMINEX 1.0, some deficiencies with the original setup caused pr oblems for meta
data analysis and a number of questions remained unanswer ed. HUMINEX was thus continued in a
second phase, called HUMINEX 2.0.

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While during HUMINEX 1.0, the participating institutions carried out their own intr oduction to
the topic for their individual courses, HUMINEX 2.0 included a standar dized intr oduction to the topic
acr oss participating institutions, along with improved course materials distributed to the participants
( http://www .wudapt.or g/huminex- 2- 0/ Moreover , in HUMINEX 1.0 most participants lived in the
city that they classified and hence, multiple cities were classified. HUMINEX 2.0 focused only on
one city: Berlin, Germany . This city and surrounding ar ea was selected for this case study due to
the variety of LCZ classes pr esent, ranging from natural landscapes to densely built-up urban ar eas.
Additionally , many participants in HUMINEX 1.0 had never carried out a supervised classification
befor e and were unfamiliar with the LCZ scheme, possibly contributing to the poor or moderate
quality of LCZ maps [
23
]. T o investigate if such a deficiency could be overcome, a ‘driving test’ for
LCZ classification with aerial imagery was developed and intr oduced to half of the participants in
HUMINEX 2.0. Finally , V an Coillie et al. [
24
] found that for remote sensing image interpr etation,
operator performance is mainly determined by demographic, non-cognitive and cognitive personality
factors, and less by external and technical factors. Hence, for HUMINEX 2.0 we aimed at identifying
similar r esults and to see if individuals’ psychological structures might influence the assumption and
the classification of the landscape.
The pr esent study aims at presenting r esults of the second phase of HUMINEX, which mainly
aimed at over coming the limitations of HUMINEX 1.0. Moreover , based on the results of both phases,
this study aims at pr oviding guidelines for operators of the WUDAPT level-0 methodology to obtain
LCZ maps of high quality . Specifically , we focus on the following resear ch questions:
1.
Can the quality of LCZ training areas be assessed fr om operator self-assessment or from the
training ar eas themselves?
2. Does pr evious knowledge on LCZ given by the driving test help to correctly classify LCZs?
3. How much does the personality of the operator influence the classification quality?
2. Materials and Methods
2.1. Layout of the Experiment
The LCZ workflow [
2
,
4
,
23
] was pr ovided online and consisted of a set of training materials that
wer e used in guided student exercises. First, the students (in the remainder of the paper also r eferred
to as participants) wer e intr oduced to the LCZ scheme and the WUDAPT framework [
4
]. Subsequently ,
they wer e provided with the softwar e and the workflow of the exercise.
Each participant defined a T A set for Berlin accor ding to the protocol developed by WUDAPT ,
i.e., “ to be of a size of appr oximately 1 km
2
; to be as homogeneous as possible; to be compact in shape; and to
have sufficient space along the borders with neighbouring LCZ ar eas” [
4
]. Next to that, in the first round, the
T A sets of each LCZ class had to include at least five to ten T A polygons in or der to cover the internal
variation within the dif fer ent zones (e.g., for an urban LCZ class the internal variation due to differ ent
r oof colours/materials). The experiment was set up as a joint effort of several universities who of fer ed
the online exer cises to their students as part of a geographic information course. All participants wer e
pr ovided with the same training materials (Saga GIS software, website, and papers), which included
the LCZ mapping workflow as described in [
23
], and wer e asked to perform a LCZ classification with
at least thr ee iterations.
Next to the T A sets and LCZ maps, elaborate metadata was collected from each participant in
the second phase of the experiment using an online questionnair e. T able 1 pr ovides an overview of
the collected metadata, ranging fr om basic information (e.g., age and gender) to questions relating to
human behaviour and personality . Besides, LCZ- and city-specific knowledge was enquired as well as
details on T A collection and LCZ classification.
The five principal factors of human personality ar e often referr ed to as the Big Five: agr eeableness
(two questions), conscientiousness (12 questions), emotional stability (neur oticism) (12 questions),

Urban Sci. 2019 , 3 , 27 4 of 16
extraversion (12 questions) and openness (two questions). The participants were asked to indicate how
much they r elate to the personality questions using the Likert scale [ 24 ].
•
Agr eeableness is the willingness to help other people, act in accordance to other people’s inter ests
and the degr ee of co-operative, warm and agreeable traits in an individual.
•
Conscientiousness can be described as the pr eference to follow rules and schedules, keep
engagements, work har d and organize.
•
Participants, which are emotional stable, ar e characterized by being relaxed and independent,
calm, self-confident and self-r estrained.
•
Extraversion defines the need for human contact, empathy , assertiveness and the wish to
inspir e people.
•
Openness measur es the degr ee to which a participant needs intellectual stimulation, change
and variety .
In addition, in HUMINEX 2.0, some self-assessment questions wer e asked, including their
assessment of the final LCZ map, their knowledge of the city being mapped, and their image
classification experience. A so called “driving test”—performed by 50% of the participants—was
intr oduced. This is a fr eely available only tool, which can be consulted at http://77.69.20.19/dev/
driver/training.php , that provides a dynamic interface to an operator to get familiar with the LCZ
scheme befor e digitizing. After the exer cise, a self-reflection questionnair e is presented to each
participant to evaluate the dedication of the participants in the experiment. Dedication can be divided
into motivation and comparative anxiety . Motivation is defined as: internal and external factors that
stimulate desir e and energy in people to be continually inter ested and committed to a job, role or
subject, or to make an effort to attain a goal. Comparative anxiety on the other r efers to the confidence
the participant has in his/her own abilities and performance, and how much concern he/she puts in
the performance of others.
T able 1.
Metadata collected from the part icipants. The allowed answers ar e provided in brackets
(After Bechtel et al. [ 23 ]).
Category Metadata Collected
General ID; City name
participant
Number of participants per training area set; highest degree (B.Sc./M.Sc./Ph.D.);
total years of study (Number of years); University course; Experience with Image
Classification (Self-Estimation ); Age; Gender; City of origin
LCZ knowledge
Introduction in seminar/course (Y es/No); WUDAPT website visit (Y es/No); study of
Stewart and Oke 2012 paper (Y es/No); study of LCZ fact sheets (Y es/No); completion
of LCZ Driving test (Y es/No); Numbers of cities classified before (Number of cities);
LCZ knowledge self-estimation (0–100%)
City knowledge
How long have you lived in the city of interest (Number of years); how long have you
lived in similar (climate, morphology) cities (Number of years); Familiarity with city of
interest self-estimation (0–100%)
Classification
T ime invested for training ar ea collection (Number of hours); Number of iterations
(Number of iterations); Used online manuals? (Y es/No); Which LCZ did you find
difficult to distinguish? (LCZ type)
Overall Self-Rating (0–100%) of final classification [map] quality
Personality All 40 personality related questions can be found in T able 2
Dedication All 20 dedication related questions can be found in T able 2

Urban Sci. 2019 , 3 , 27 5 of 16
T able 2. Dedication and Personality related questions in the HUMINEX questionair e.
Dedication T rait Question
Motivation Doing well in this classification exercise is important to me;
I wanted to do well in this exercise;
I tried my best in this exercise;
I tried to do the very best I could in this exercise;
While taking this test, I concentrated and tried to do well;
I want to be among the top scorers in this exer cise;
I pushed myself to work hard on this exer cise;
I was extremely motivated to do well in this exer cise;
I just did not care how I did in this exer cise;
I did not put much effort in this exer cise;
Comparative anxiety
I probably did not do as well as most of the other people who participated in this exercise;
I am not good at exercises;
During the exercise, I often thought about how poor I was doing;
I usually get very anxious about doing exercises;
I usually perform well on exercises;
I expect to be among the people who score r eally well in this exercise;
My scores usually do not r eflect my true abilities;
I very much dislike doing exercises of this type;
During the exercise, I found myself thinking of the consequence of failing;
During the exercise, I got so nervous I couldn’t do as well as I should have.
Personality T rait Question
Extraversion Make friends easily;
Feel comfortable around people;
Start conversations;
Know how to captivate people;
Don’t mind being the center of attention;
Don’t talk a lot;
Keep in the background;
Have little to say;
Don’t like to draw attention to myself;
Am quit around strangers;
I see myself as extroverted, enthusiastic;
I see myself as reserved, quiet;
Neuroticism I’m relaxed most of the time;
Seldom feel blue;
Get stressed out easily;
W orry about things;
Am easily disturbed;
Get upset easily;
Change my mood a lot;
Have frequent mood swings;
Get irritated easily;
Often feel blue;
I see myself as anxious, easily upset;
I see myself as emotionally stable, calm;
Conscientiousness Am always prepar ed;
Pay attention to details;
Get chores done right away;
Follow a schedule;
Like order;
Am exacting/demanding in my work;
Leave my belongings around;
Make a mess of things;
Often forget to put things back in their pr oper place;
Shirk my duties;
I see myself as dependable, self-disciplined;
I see myself as disorganized, car eless;
Agreeableness I see myself a critical, quarrelsome;
I see myself as sympathetic, warm;
Openness I see myself as open to new experience, complex;
I see myself as conventional, uncreative.

Urban Sci. 2019 , 3 , 27 6 of 16
2.2. Participants and Study Sites
In total 141 students from six universities and one independent contributor participated in
HUMINEX 2.0, but only 81 managed to pr ovide images in the correct format. Only 59 performed three
or mor e iterations and filled out the questionnaire (T able 3 ).
T able 3. Participants and cities in the HUMan INfluence EXperiment.
Name of Institute Number of Students # T A Sets Used in Evaluation
University of Augsburg 25 16
NO institute 1 1
Yncréa HEI 19 6
University of Leuven (2017/2018) 35/28 9/11
T echnical university of Berlin 15 5
Ghent University 6 3
W ageningen University 13 8
Analysis was thus performed on this selection of participants. From the r emaining 59 participants,
six did not provide their years of study , how they rate their own competence and their gender .
The other 53
, saw themselves as competent, advanced beginner or novice participants (r espectively ,
2, 19 and 31). Most of the participants ar e thus inexperienced in image classifications. Only 5 of the
participants lived in Berlin, and 43 of the participants felt less than 26% familiar with the city . 88% of
the participants had never done a LCZ classification before and 32 of the participants felt they had less
than 25% knowledge of the LCZ scheme at time of the classification. 17 felt familiar for 25–50%, nine
for 50–75% and only one felt 80% knowledgeable on the LCZ scheme. For all the above, 100% equals
perfect familiarity/knowledge, 0% equals no familiarity/knowledge.
2.3. Analysis and Accuracy Assessment
For all r esearch questions the accuracy of the r esulting LCZ maps was assessed using a sample
of r eference ar eas previously identified by a LCZ expert familiar with the methodology and the city
under study [
21
,
23
]. For each map, the following two standard accuracy measur es were derived
(see also V erdonck et al. [
22
], Bechtel et al. [
23
]: overall accuracy (OA = percentage of corr ectly
classified pixels); and the F1-scor e, which repr esents the arithmetic mean of the class-wise F1 values,
which ar e calculated as the weighted harmonic mean of the user ’s (UA) and pr oducer ’s accuracy (P A).
The class-wise F1-scor e (Equation ( 1 )) for class i is calculated as [
25
] and r esults in a value between
0 and 1:
F 1 i = 2 U A i · P A i
U A i + P A i
(1)
A statistical
t
-test was performed to evaluate whether a significant differ ence could be found
between the dif ferent iterations, the dif ferent gr oups of each personality trait, the participants who did
or did not do the driving test and the dif ferent gr oups regar ding time investment. The significance
thr eshold was set at 0.05.
3. Results
3.1. Self-Assessment
This experiment investigated the ability of the participants to corr ectly assess the quality of their
maps based on visual interpr etation. Figure 2 a shows that on average, participants have the tendency
to under estimate the accuracy of their LCZ map in the first two iterations and, to overestimate their
final mapping r esult.

Urban Sci. 2019 , 3 , 27 7 of 16
Figur e 2 b portrays the average differ ence between self-estimation and OA for each iteration and
the number of participants who r espectively over- or under estimated their mapping results. Similar
to Figur e 2 a, the results show that for the first two iterations, most participants under estimated the
r esults, whereas for the last iteration 56% of the participants over estimated the final mapping result.
Figure 2.
(
a
) Boxplots of self-estimated (SE) and actual (OA) overall accuracy for each iteration. Median
OA: red stripe; average OA: white dot; boxplot ends: first and thir d quantile; whiskers:
+ / −
the
1.5 fold interquartile range on OA values and outliers: grey dots. (
b
) Dif ference between self-estimated
and actual overall accuracy . Numbers beneath the bars indicate the number of participants who
respectively under - or overestimated the mapping accuracy .
3.2. Information from the T raining Areas
Built zones have on average smaller T A surface areas compar ed to natural zones (Figure 3 ), since
the latter ar e characterized by a higher degree of homogeneity .
Figure 3. Number of T A sets according to surface ar ea (km 2 ).
What we can learn fr om the information on surface areas of the T As is related to the occurr ence
of r epr esentative zones for a class. When zones are underr epresented in a city , it is possible to find
some small training ar eas, but it is likely that the classifier does not pick up on the zone, due to the
limited amount of information about this zone. The final LCZ map will contain these zones but the
accuracy will be low . Since LCZ maps provide information on the local climate it is important that
a local climate can be established in the zones and they should thus be of a certain size (
>
1 km
2
).
When zones ar e smaller and embedded in other zones, it is often better to remove the zone fr om the
T A set. The size of the average area of the T A for each zone can be an indication for underrepr esented
or non-existing LCZs in a city .
In addition to the surface ar ea of T As, the number of T As selected for each zone can be an
indicator for zones which ar e hard to classify . In T able 4 , mean, min and max number of T As for
each zone ar e listed. The number of times a zone was not selected (NS) by a participant is also listed.

Urban Sci. 2019 , 3 , 27 8 of 16
Fr om this table and Figure 3 , it becomes clear that when the number of T As for a specific zone is low ,
the r epr esentativeness of this T A might be low , inducing lower accuracies. As a user this can be of
importance. In fact, inexperienced participants often spent a lot of time searching for r epresentative
T As for all the LCZs even when some of the zones are not even lar ge enough or occur too sparsely in
the city to become a LCZ.
T able 4.
T A characteristics for area, number , shape and node count of T As, NS = not selected by
a participant.
LCZ 1 LCZ 2 LCZ 3 LCZ 4 LCZ 5 LCZ 6 LCZ 7 LCZ 8 LCZ 9 LCZ 10
Area (km 2 )
mean 0.1 1.0 0.3 0.3 0.5 0.5 0.2 0.4 0.3 0.3
max 0.8 7.9 2.7 1.2 2.1 3.5 1.0 1.5 6.7 1.3
Number
mean 3.6 9.7 5.7 6.7 10.7 14.7 6.12 9.99 7.3 6.2
min 1 2 1 2 3 5 1 4 1 1
max 11 26 16 19 50 115 23 28 17 19
NS 15 1 16 4 0 0 35 1 7 9
Shape (mean) 1.52 1.51 1.49 1.33 1.46 1.47 1.63 1.45 1.59 1.80
V ertices (mean) 6.02 7.77 8.58 7.88 7.54 7.76 7.26 8.33 7.41 7.99
LCZ A LCZ B LCZ C LCZ D LCZ E LCZ F LCZ G
Area (km 2 )
mean 5.9 0.8 0.5 2.7 0.3 0.3 1.1
max 28.4 8.5 5.7 12.8 2.5 1.7 3.5
Number
mean 13.1 9.6 6.3 12.4 7.5 8.5 13.9
min 7 1 1 8 1 1 6
max 40 24 15 38 26 21 40
NS 0 0 1 0 0 0 0
Shape (mean) 1.99 1.62 1.66 1.83 2.10 2.81 2.22
V ertices (mean) 10.70 8.44 8.63 8.98 7.80 9.34 12.10
In addition, the WUDAPT method suggests to digitize compact and simple T A sets. This would
translate into T A sets which are characterized by a shape ratio close to one and a low number of vertices.
The shape ratio is calculated based on the ratio between surface ar ea and perimeter (Equation ( 2 )),
considering a cir cle (shape = 1) is the most compact shape:
s h a p e = p e r i m e t er 2
4 ∗ p i ∗ ar e a (2)
Fr om T able 4 , it is shown that on average the built zones have more compact T As compared to the
natural zones.
3.3. Driving T est
HUMINEX 2.0 also focused on the influence of the driving test: do participants perform better
after classifying a number of test images? Fr om the r esulting participants, 31 performed the driving test.
All participants wer e fr ee to choose the amount of test images to classify . The range of the classification
images was quite lar ge: 20–147, with a median/mean of 50/58 images classified. The r esults on
Figur e 4 indicate that participants who did the driving test perform better than those who did not,
it also shows that impr ovement with iterations is smaller if the driving test is carried out, but people
who carried out the driving test always have higher quality . Overall, it shows that for all participants
overall accuracy incr eased over the iterations regar dless of the test. Most importantly the
t
-test showed
a significant dif f er ence in the OA after the first iteration for participant who carried out the driving test.
In Figur e 5 scatter plots are shown for the number of test images classified compar ed to the
OA. Fr om these figures it is clear that the number of test images does not influence the OA and the
participants should thus be fr ee to choose the amount of test images.

Urban Sci. 2019 , 3 , 27 9 of 16
Figure 4.
Boxplots of overall accuracies for each iteration depending on driving test. Median OA:
red stripe; average OA: white dot; boxplot ends: first and thir d quantile; whiskers:
+ / −
the 1.5-fold
interquartile range on OA values and outliers: grey dots.
Figure 5. Number of test images versus overall accuracy .

Urban Sci. 2019 , 3 , 27 10 of 16
3.4. Dedication
Based on all the answers in T able 2 , a weighted score is given for each dedication trait, three
equally lar ge gr oups for the two traits are defined based on the scor es of all participants. Since some
of the scor es wer e pr esent in two differ ent gr oups, the groups wer e reclassified and thr esholds are
set [
24
]. Group 1 portrays the participants who shows a low agr eement with a certain trait, group 3
shows high agr eement with the respective dedication trait (T able 5 ).
Fr om Figure 6 a it seems like motivation did not have an important influence in the mapping
pr ocess. Participants who had low motivation perform best after three iterations. Participants who did
not suf fer fr om comparative anxiety performed better compar ed to participant who felt pressur e to
perform well for all thr ee iterations (Figure 6 b). This suggests that participants which suffer fr om high
levels of comparative anxiety achieve the lowest map accuracies. It should be noted that no significant
dif ferences wer e found between groups and iterations. However , due to the small r esulting sample of
participants (59) it is not clear whether dedication had no influence or whether more data is necessary
to find statistically significant r esults.
Figure 6.
Boxplots of overall accuracies depending on iteration and gr oup (
a
) motivation and
(
b
) comparative anxiety . The x-axis ticks ar e formatted as
X _ Y
with
X
the iteration number and
Y
group number . Median OA: red stripe; average OA: white dot; boxplot ends: first and third quantile;
whiskers: + / − the 1.5-fold interquartile range on OA values and outliers: grey dots.

Urban Sci. 2019 , 3 , 27 11 of 16
T able 5. Differ ent group sizes and thr esholds for each personality and dedication trait
Extraversion Neuroticism Conscientiousness Motivation Comparative Anxiety
Group Size Threshold Group Size Threshold Group Size Threshold Group Size Threshold Group Size Threshold
Group 1 22 < 2.7 20 < 3.1 21 < 3.3 19 < 3 18 < 2.3
Group 2 17 2.7–3.1 20 3.1–3.6 21 3.3–3.8 19 3–3.4 19 2.3–2.6
Group 3 20 > 3.1 19 > 3.6 17 > 3.8 18 > 3.4 18 > 2.6
3.5. Personality
The participants wer e also questioned (T able 2 ) about their personality . Again all participants
wer e divided into three gr oups according to their answers (T able 5 ). The results r elated to personality
ar e, similarly to the results on dedication, not significantly dif ferent, again pr obably due to the small
sample size.
In Figur e 7 , r esults are shown for the personality analysis. The trends clearly show that with
each iteration overall accuracy r ose. For neuroticism it is shown that participants showing medium
characteristics on this personality trait had maps with the highest overall accuracies. Both for
conscientiousness and extraversion the tr ends show that participants scoring low for these traits
had the highest overall accuracies.
Figure 7. Cont.

Urban Sci. 2019 , 3 , 27 12 of 16
Figure 7.
Boxplots of overall accuracies depending on iteration and group for extraversion, neur oticism
and conscientiousness. The x-axis ticks are forma tted as
X _ Y
with
X
the iteration number and
Y
group number . Median OA: red stripe; average OA: white dot; boxplot ends: first and third quantile;
whiskers: + / − the 1.5-fold interquartile range on OA values and outliers: grey dots.
3.6. Difficulties According to the Participants
After each iteration participants were asked to indicate which classes wer e difficult to r ecognize
on the Google Earth images. In Figure 8 , F1 scor es are shown in boxplots for all pr esent LCZs in Berlin
for the last iteration.
Figure 8.
Boxplots for the F1 scor e for each LCZ after the last iteration, blue values = number of times a
participant indicated this zone as dif ficult to classify over all iterations. Median OA: red stripe; average
OA: white dot; boxplot ends: first and third quantile; whiskers:
+ / −
the 1.5-fold inter quartile range
on OA values and outliers: grey dots. The colour of the boxplots are the LCZ colours when the zones
are mapped.
Below the boxplots in blue, the number of times participants indicated that a zone was dif ficult to
r ecognize over all iterations is shown. Especially for the natural zones, the results show a clear r elation
between the dif ficulty in r ecognition and the classification accuracy , e.g., zones A, D and G always
r esulted in high accuracies. For the built zones this link is not as pronounced. The best classified
zones ar e LCZ 2, 6 and 8. It is however clear that zones which ar e not present in lar ge enough areas,
wer e recognized as dif ficult e.g., LCZ 1, 3 and 7. LCZ 9 was one of the most dif ficult zones to classify .

Urban Sci. 2019 , 3 , 27 13 of 16
In addition, T able 4 shows that zones that had low F1 scor es according to Figur e 8 (LCZs 1, 3, 7 and 10)
ar e characterized by small T As on average.
3.7. T ime Investment
In a final step, all participants wer e asked to report the time they spent on each iteration.
The r esults (summed time investment for all iterations) are evaluated and pr esented in Figure 9 .
Figur e 9 shows that a medium time investment is the most beneficial for the accuracy results. For our
study medium time investment is defined between 240 (4 h) and 330 (5 h and 10 min) minutes in total.
Figure 9.
Overall accuracy based on overall time investment. Median OA: red stripe; average OA:
white dot; boxplot ends: first and third quantile; whiskers:
+ / −
the 1.5-fold inter quartile range on OA
values and outliers: grey dots.
4. Discussion
The second experiment showed that it is not straightforwar d to deduce impr oved guidelines from
the metadata and the training areas. As was shown in the results, no significant r elation could be
found between personality and dedication traits. This could mean that any individual, independently
of their backgr ound could pr operly map Local Climate Zones after some training (e.g., thr ough
multiple iterations or by using a ‘driving test’ pr evious to the mapping). However , the small size
of the HUMINEX 2.0 sample does not allow for drawing such conclusions. Experiments with mor e
participants should be performed to get convincing results. Still, r esults indicate that pressuring
participants decr eases their ability to produce accurate maps. Encouragingly , the driving test results
support that all WUDAPT contributors shall take the test in or der to improve their capacity to locate and
r ecognize repr esentative T As, and hence their mapping accuracies. Self-assessment of the intermediate
and final mapping r esults indicated that participants became better in assessing the quality of their
maps after multiple iterations, even though the overall accuracy for all maps remained rather low .
The most inter esting results fr om the second experiment are r elated to the input of the participants on
dif ficult classes and the information embedded within the training areas. If a LCZ is not pr esent or
not suf ficiently large, participants often indicated this corr ectly . A similar conclusion can be made for
the surface ar ea and the number of T As, indicating that when LCZs are not pr esent in sufficient lar ge
surface ar eas, it is generally harder for participants to find T As in large numbers or with an adequate
surface ar ea. This will become clear after the first iteration.
For futur e resear ch it is of importance to include the limitations of this experiment. First, as
discussed in the methods, less than 50% of the participants delivered useful data for HUMINEX 2.0.
In this r espect, it is of utmost importance to improve communication in this type of experiments,
otherwise significant r esult ar e difficult to come by . Second, due to the fact that less then 10% of

Urban Sci. 2019 , 3 , 27 14 of 16
the participants lived in Berlin or wer e familiar with the city , it was not possible to investigate the
influence of local knowledge on the classification results. Even though, currently r esearch is done
investigating the potential of continental-scale LCZ maps [
26
–
28
], the question on local knowledge
r emains important. In this respect, it might be better to include mor e cities. But instead of focusing on
western/ Eur opean cities, as was done in HUMINEX 1.0, it might be important to include cities but
also participants fr om other geographical backgrounds.
5. Conclusions
The second phase of the HUMan INfluence EXperiment (HUMINEX 2.0) focused on a single
city (Berlin, Germany) and pr ovided a standar dized introduction to the topic as part of the student
courses within participating institutions. The experiment’s main aim was to provide users with better
guidelines to pr oduce more accurate LCZ maps.
Participants who did the driving test achieved better r esults and were able to assess the importance
of the dif fer ent zones in their study area. Moreover , r esults indicated that str essful conditions ar e
likely to r educe the mapping accuracies. It hence means that education of the participants and proper
working conditions ar e indispensable for achieving good results. Unfortunately , the relatively small
sample size (as a r esult of the small amount of valid submissions) did not allow us to draw r obust
conclusions on the influence of personality factors or the r ole of local knowledge on the quality of
the LCZ maps. Y et despite these deficiencies (present in both phases of HUMINEX), the following
guidelines for impr oved mapping using the WUDAPT methodology can be derived:
• Follow the rules of the WUDAPT pr otocol concerning the size and form of training areas;
• Spend at least 4 hours (for a city similar to Berlin) on the classification without being str essed ;
• Carry out the driving test befor e doing the actual classification;
• The mor e iterations (at least three) the better the accuracy;
•
Submit your LCZ map and training ar eas to the WUDAPT portal [
29
], even if your city is alr eady
pr esent: combining training ar eas typically r esults in an overall better classification.
Author Contributions:
B.B., M.-l.V ., M.D. and F .V .C. came up with the idea to run HUMINEX 2.0. O.B., D.F .,
M.-l.V ., B.B., C.B., A.D., and F .L. ran the HUMINEX 2.0 experiment with their students. M.-l.V . mostly analysed
the data, supported by M.D. and F .V .C. M.-l.V . wr ote the original draft and all of the authors contributed to the
reviewing and editing of the paper .
Funding:
This resear ch was funded by the Belgian Federal Science Policy Office, as part of the UrbanEARS pr oject
(SR/00/307); by the Remote sensing for Epidemiology in African CiT ies (REACT : http://react.ulb.be/ ) pr oject,
funded by the STEREO-III program of the Belgian Science Policy (BELSPO, SR/00/337); by the NWO project
number 864.14.007; and by the German Research Foundation (Grant No. SCHE 750/15-1).
Acknowledgments: The authors would like to thank the involved teaching assistants as well as all participants.
Conflicts of Interest:
The authors declare no conflict of inter est. The funders had no role in the design of the
study; in the collection, analyses, or interpr etation of data; in the writing of the manuscript, and in the decision to
publish the results.
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