The most common approach to assessing natural hazard risk is investigating
the willingness to pay in the presence or absence of such risk. In this work, we propose a new, machine-learning-based, indirect approach to the problem,
i.e. through residential-choice modelling. Especially in urban environments,
exposure and vulnerability are highly dynamic risk components, both being
shaped by a complex and continuous reorganization and redistribution of assets
within the urban space, including the (re-)location of urban dwellers. By
modelling residential-choice behaviour in the city of Leipzig, Germany, we
seek to examine how exposure and vulnerabilities are shaped by the residential-location-choice process. The proposed approach reveals hot spots and cold
spots of residential choice for distinct socioeconomic groups exhibiting
heterogeneous preferences. We discuss the relationship between observed
patterns and disaster risk through the lens of exposure and vulnerability, as
well as links to urban planning, and explore how the proposed methodology may
contribute to predicting future trends in exposure, vulnerability, and risk
through this analytical focus. Avenues for future research include the
operational strengthening of these linkages for more effective disaster risk
management.
Introduction
In the human-environmental system, disaster risk arises from the interactions
of different system components . The Hyogo Framework
for Action 2005–2015 maintains that disaster risk stems from the interaction
of a hazard with exposed physical, socioeconomic and environmental
vulnerabilities , consequently referring to the potential
fatalities and losses in livelihoods, health, assets, and services. Urban
processes such as not only the expansion into potentially hazardous areas but also
gentrification or densification shape exposure and vulnerabilities of services
and assets within urban areas in a highly dynamic manner and are thus at the
basis of urban disaster risk. Hence, incorporating these urban processes more
specifically into disaster risk assessment promises potential for more
holistic perspectives.
Disaster risk R is conceived as a function of the interacting,
interdependent risk component hazard H, exposure E, and vulnerability
V, expressed as R=H×E×V. Here, hazard refers to
potentially damaging physical events or latent conditions representing future
threats of natural, human-natural (environmental), or human origin
. Exposure denotes the physical aspects of disaster risk
, referring to the socioeconomic and demographic
spatiotemporal fabric, i.e. assets such as population or the built
environment that are potentially affected by a hazardous event
. Vulnerability embraces the
predisposition or propensity to be adversely affected, i.e. those physical,
socioeconomic, and environmental conditions leading to (an increase in)
the susceptibility of elements or fragility of elements exposed to hazards
. Disaster risk is consequently driven by the
specifics of hazardous conditions, i.e. hazard extent, severity, and return
period , as well as by (changes in) exposure and the degree
of vulnerability . In the case of extreme events, disaster
risk is mostly conditioned by exposure . H, E, and V
are dynamic over time and across spatial scales and are thus non-stationary
. This gives rise to considerable uncertainty in the
assessment of future risks
, thus calling for
a more holistic, combined assessment of all relevant risk drivers
.
Whilst climatic drivers, encompassing both natural variability and
anthropogenic climate change, affect the magnitude and (joint) probability of
(compound) hazardous events
, non-climatic
drivers including socioeconomic and demographic development with resulting
land-use changes shape exposure as well as vulnerability
. Particularly high levels of or increases in
exposure and vulnerability are found in the global urban land
. Urban areas as complex, highly dynamic, and
integrated systems are particularly prone to hazards, which pose threats to
physical assets as well as economic, social, and political activities;
disadvantaged populations and the urban poor; critical infrastructures;
livelihoods; and households . This is due to various interlinking economic, social, and spatial processes, e.g. the accumulation of capital, the increasing interconnectedness of places, and increasing individualization as well as urban growth and expansion
. For instance, from a
global perspective, almost 90 % of the anticipated urban growth is
expected in regions with limited economic development and thus comparatively
high vulnerability including e.g. the small- to medium-sized cities of Africa
and Asia .
As recognized for instance by , , or
, these global phenomena are linked down to the local level
through their repercussions on the urban form. Consequently, also from this
local perspective, exposure is governed firstly by urban population growth and
the expansion of urban land. However, exposure is also shaped by multiple
processes such as neighbourhood redevelopments and urban and economic
restructuring, gentrification, infill, densification, or decay as well as
(intra-urban) mobility and (rural–urban) migration, social–spatial
segregation, increasing polarization, and growing inequalities
. In this
context, urban disaster risk is also driven by demographic changes and shifts
(ageing) as well as by the impacts of conditions of the natural and built
environment on human wellbeing and human health .
The aforementioned processes bring about the substantial reorganization of urban
structures and functions and the redistribution of activities and assets in
cities . This also effects changes in individual
self-selections, preferences, and attitudes, e.g. regarding the choice of
residential location and household mobility . It has been
estimated that, overall, in North America, Australia, and New Zealand, the
share of households moving annually is about 15 % to 20 % and in Europe is 5 % to 10 % . Household mobility is typically
characterized as a two-step process, i.e. the decision to seek a new
residence and its actual selection . A comprehensive
body of literature on residential choice adopts stated-preference approaches
and discrete-choice modelling to study this decision process and the
corresponding determinants of residential location choice. This includes case
studies, e.g. for Burkina Faso , China ,
Colombia , Germany , Israel
, the Netherlands , Pakistan
, or the UK .
describes the choice of housing location as a rational, complex decision based
on multiple dwelling characteristics such as the number of rooms or types of
appliances, as well as location or neighbourhood attributes such as proximity to
green spaces and the accessibility to places of work, commerce, education,
and transportation. It is consequently recognized that residential location
choice and hence residential mobility and migration are driving
(intra-urban) spatial (re)structuring and thus exposure and vulnerabilities
.
additionally highlights the role of land-use policies and
population densities in the residential-location-choice process and the
urban–rural gradient patterns emerging from this process. A substantial body
of research studies this nexus of household perceptions on environmental
amenities and disamenities – i.e. risks – and their role in residential
location choice
(, , for a comprehensive list of
references). For example, in the case of less developed countries, in-migration
and residential location choice within hazard-prone areas is often the result
of the lack of coordination of urban development; informality of large parts of
the residential sector; lack of institutional capacities; failed risk
governance; lack of financial capacities; housing-market discrimination; and lack
of knowledge, awareness and risk perception of disadvantaged populations
. However, in the case of the more developed
countries, it is also highlighted that risks and potential losses are often
accepted due to locational benefits or outweighed by environmental amenities such as riparian areas, lake shores, or scenic views
.
Most approaches that investigate the nexus between residential housing choice
and hazard risk assume an indirect approach, i.e. the hedonic price model and
associated regression methods . Hereby, physical housing
attributes and locational and neighbourhood characteristics as well as
environmental attributes – such as the level of exposure, risk, or expected
losses – are considered in the derivation of a willingness to pay
. Whilst following some empirical findings
suggest that residents' willingness to pay is indeed lower in hazard-prone
areas; it is also remarked that this evidence base is not at all
clear-cut. Direct approaches, e.g. using household surveys, thus aim to
directly identify the respondents' main motivations and decision factors for a specific location choice and the role that hazard exposure and risk play in
them .
This paper seeks to bring together the study of residential housing choice and
the school of natural hazard risk assessment by an indirect, machine-learning-based approach. Unlike the aforementioned approaches, it is not the focus of
this case study to estimate the willingness to pay in the presence or absence
of natural hazard risk. It is also not aiming to elicit risk awareness e.g.
of households on the move. Instead, it is proposed to explore the means and
insights that residential-location-choice modelling offers for the
identification of spatial hot spots of exposure and/or vulnerabilities and – by
extension – how identifying current shifts in these patterns may hint at
future trends in exposure and vulnerability, which we consider fundamental
information for disaster risk assessment. The study presented in this paper
builds on a case study by that – akin to a stated-preference approach – modelled residential-choice behaviour towards
hypothetical apartment listings in the city of Leipzig, Germany. To do so,
use a random forest model that models
residential choice as the likelihood of a positive or negative decision
outcome including considering the heterogeneity of preferences, i.e. the
variation in housing preferences across individuals and socioeconomic groups
. Random forests allow the use of large sets of mixed data
and have proved to be robust and efficient classification models that often
outperform individual decision trees or regression models
(, ;
see , , and
, , for more comprehensive reviews
of random forests and other machine-learning algorithms). This case study goes
beyond the previous work by making predictions of residential choice for
actual real-estate data in the form of apartments advertised for rent on a
common internet platform called ImmobilienScout24 , and by
spatializing these predictions to elicit spatial patterns of residential
choice and their change over time. In so doing, this case study seeks to
address the following research questions:
Does residential-choice modelling allow us to identify spatial patterns of
exposure, e.g. hot spots of (vulnerable) socioeconomic groups? How are these
spatial patterns of exposure and thus vulnerabilities shaped by the
heterogeneity of preferences as a function of the socioeconomic status of
urban dwellers?
Can residential-choice modelling contribute to the estimation of changes
in exposure and vulnerabilities by detecting trends in the spatial
distribution of vulnerable groups?
In so doing, this case study aims to bring disaster risk assessment forward by
making manifold and complex urban dynamics that shape the spatial distribution
of urban dwellers and that consequently drive urban exposure and
vulnerabilities more accessible in the assessment process.
Materials and methods
Predictors for residential choice include spatial as well as non-spatial
housing attributes, namely inclusive rent, location,
number of rooms, total size, furnishing features, and house type – i.e. the structure type of the
apartment building – and neighbourhood amenities such as the presence of
major roads, urban green areas, or local suppliers. Additionally, various
household attributes including income, employment status, qualification, and
age are used for this prediction. The spatialization of the random forest
model by necessitates that the real-estate data provided
by are re-coded, e.g. regarding categorial predictor variables,
and geolocated. Hence, the methodology applied in this case study embraces the
following steps (Fig. ): (i) extraction of non-spatial
housing attributes, i.e. the characteristics of each actual apartment, from
the scientific-use file provided by (;
see Table ); (ii) determination of spatially homogeneous units
for the geolocation of prediction targets; (iii) determination of spatial
housing attributes based on ancillary data (Table ); (iv)
formulation of a set of socioeconomic profiles to account for heterogeneity of
preferences (Table ); and (v) application of the pre-trained
random forest model to predict the likelihoods of positive residential-choice
outcomes. To evaluate changes in residential choice over time, this case study
considers three reference years: 2008/09, 2013/14, and 2018/19. In the
following, each methodological step is described in more detail.
Data
(pre-)processing for the prediction of residential-choice
behaviour. (a) Non-spatial housing attributes are elicited directly
from the apartment advertisements (Boelmann et al., 2019;
see Table 1). (b) Identification of spatially homogeneous units and
estimation of neighbourhood amenities per spatially homogeneous unit based on
the spatial overlap of buffer and service areas of major roads, urban green
areas, pharmacies, and local suppliers. Geolocation of advertised apartments
within these spatially homogeneous units; (c) determination of
spatial housing attributes as a function of the properties of the
corresponding spatially homogeneous unit; (d) based on a set of
formulated socioeconomic profiles, household attributes are
created. (e) Permutation of predictor factors and subsequent
application and evaluation of the random forest model.
First, the non-spatial housing attributes house type, number
of rooms, furnishing features, inclusive rent (rent
including heating costs), condition, and total size
(Table ) were determined from the apartment advertisements
listed in the scientific-use file (Fig. a). As shown in
Table , all housing attributes except furnishing
features have a one-to-one cardinality; i.e. each advertised apartment has
exactly one inclusive rent, a specific number of rooms, etc. A given apartment may however have multiple furnishing features, such as a fitted kitchen, courtyard or garden, and so forth. This constitutes a one-to-many relationship.
Second, prediction targets, i.e. the individual advertised apartments, need
to be geolocated. The geolocation of each apartment typically corresponds to
its address. However, in the provided scientific-use file, due to privacy
protection, the actual address is anonymized and coded to a 1 km2
grid cell location in the European standard ETRS89-LAEA. Such a coarse spatial
resolution obviously has limits, particularly in complex urban
environments. To overcome this limitation, we suggest increasing the spatial
resolution through a mapping of apartment locations to so-called spatially
homogeneous units (SHUs). SHUs were identified on the basis of a grid with a
spatial resolution of 250m×250m instead of
1000m×1000m; i.e. each grid cell of the original
1 km2 grid was divided into 16 sub-cells. An SHU is characterized by the following properties: (i) residential land use, (ii) a predominant (unique) house type, and (iii) the presence or absence of each
individual spatial housing attribute. Areas of residential land use were
determined from official topographic land-use data ATKIS . The
predominant house type for each grid cell was subsequently derived by
intersecting the 250m×250m grid with a dataset by
that describes the urban structure of the city of Leipzig
by a combination of land-use and (residential) house types, e.g. “single and
semi-detached houses” or “prefabricated housing estates”. House types were
consequently assigned to each grid cell of the 250m×250m grid through the intersection. Then, the grid cells with common
types of housing within each original 1 km2 grid cell were merged,
and in so doing, the SHUs were identified (Fig. ). As shown in
Fig. , it needs to be noted that as the final delineation of each
SHU depends on the predominant house type, the size of the resulting SHU must
not correspond to a single 250m×250m grid cell but
may comprise more than one sub-cell.
Delineation of spatially homogeneous units (SHUs) based on house types. (a) Study area overlaid with the 1 km2 INSPIRE grid used in the scientific-use file (Boelmann et al., 2019) for geolocating advertised apartments (bold lines) and the 250m×250m grid used as basis for the delineation of SHU (thin lines); (b) SHU obtained by dissolving the intersection of the 250m×250m grid and the urban structure and land-use dataset by Haase and Nuissl (2007). As the detail shows, the final size of each SHU may vary considerably, depending on the homogeneity or heterogeneity of urban structure and corresponding predominant house types within each ETRS89-LAEA 1 km2 grid cell. GDR refers to the German Democratic Republic.
In a next step, each SHU was assigned spatial housing attributes, i.e. the
presence or absence of major roads as well as of the neighbourhood amenities of green
urban areas, pharmacies, and local suppliers. It follows that similarly to
furnishing features, neighbourhood amenities constitute an attribute with a
one-to-many cardinality, where the presence of a given amenity was affirmed if
at least 67 % of an SHU was within one of the following (see Fig. b and
Table ):
A 150 m buffer area to major roads. This distance threshold is
in line with the literature that suggests that air pollutant concentrations are
highest within this distance to major roads and is further
supported by studies stating increased health risks – e.g. regarding
obstetrical complications , decreased lung function in
adults , or neurological disease incidence
– within up to 200 m of major roads.
The service area of urban green areas. This is defined by a walking distance of
250 m, a threshold in line with recommendations that urban green areas
should be accessible within no more than a 300 m linear (buffer)
distance or an approximately 5 min walk /
The service areas of local suppliers or pharmacies. This is defined by a walking
distance of 500 m or an approximately 10 min walk
.
The advertised apartments were then geolocated to a given SHU within their
coded 1 km2 grid cell by the matching of house types.
Third, as a function of this geolocation, spatial housing attributes for each
apartment listing were determined by the properties of the corresponding
SHU. Moreover, the categorized location as well as multiculturality was
determined (see Fig. c and Table ).
Fourth, to account for heterogeneity of preferences and in this way for
different degrees of vulnerability (Table ), predictions are
carried out for a set of socioeconomic groups that are characterized by
employment status, qualification, net income, and age
(Fig. d). In so doing, the shaping of exposure and
vulnerabilities – and subsequently disaster risk – through residential
choice can be illuminated as a function of these household
characteristics. The attributes for each socioeconomic group were chosen from
the factor distributions, i.e. mode, of the sampled dataset used by
for random forest training. The hazard-specific degree of
vulnerability, as exemplarily postulated in Table , is a
compound based on the age and income characteristics of each socioeconomic
group. Regarding flood hazards, the estimated degree of vulnerability follows
empirical findings by , whereas for heat stress,
vulnerability is based on . In both cases, older persons
feature generally higher degrees of vulnerability. Likewise, more deprived or
disadvantaged groups feature higher vulnerabilities compared to less
disadvantaged ones.
Types, variables, description, cardinality, and source of data.
VariableDescriptionCommentCardinality*SourceSizeClassified sizeThe total size (area in m2) of the apartment.1:1Non-spatial housing predictorsRoomsClassified number ofroomsThe total number of rooms of each apartment.1:1RentClassified inclusive rentThe inclusive rent is the exclusive rent including heating costs in EUR.1:1Furnishing featuresAvailability of a courtyard or garden, fittedkitchen, or insulationFurnishing characteristics of the apartment.1:nConditionClassified condition of the apartmentIndicates if apartment is fully renovated (first occupancy (after reconstruction), like new, reconstructed, modernized, completely renovated), partly renovated (well-kept), or not renovated (needs renovation, by arrangement, dilapidated)1:1House typeBuilding structure typeRe-classified to factors Wilhelminian, detached, GDR, and post-reunification as required by the random forest model.1:1Categorized locationClassified city districtAssignment of class as a direct function of the geolocation1:1Spatial housing predictorsNeighbour-hoodamenitiesMajor road (roads equivalent to types motorway, primary road, secondary road, tertiary road, trunk (including corresponding links))150 m buffer of major road1:nOpenStreetMap, Urban green area (recreational and sport areas, shrubs, forests)250 m walking distance service areaLocal suppliers500 m walking distance service areaSelf-compiled databasePharmaciesMulticulturalityMulticultural image as a function of categorized locationOwn classification
* A one-to-one cardinality is indicated by 1:1; a one-to-many cardinality is indicated by 1:n.
Characterization of the apartments offered for rent regarding predictors (a) categorized inclusive rent (rent including heating costs), (b) house type, (c) categorized total size, and (d) categorized number of rooms. For house type, GDR is equal to prefabricated housing estates, post-90 to buildings constructed post-reunification, and W to Wilhelminian-style buildings. For each house type, the condition is indicated in brackets: FR = fully renovated; PR = partly renovated; NR = not renovated. Note that condition is not differentiated for post-reunification buildings due to the random forest training data.
Fifth, applying the pre-trained random forest model
(Fig. e) that is implemented in the R package
randomForestSRC, the predicted probability p
for a positive residential choice is then a function of factor combinations:
p=f(house type, rooms, size, rent,
features, location, amenities, employment,
qualification, income, age). For this random
forest model, the rate of success, i.e. the share of all correct predictions
including both negative and positive outcomes, is shown to be approximately
78 %; however, precision – i.e. the share of correct positive
choices – is lower at approximately 26 %. This is however comparable to other prediction models, such as binary logistic
regression . It is also important to note that the random forest model allows for only a single factor value per predictor variable. To overcome this limitation, for each apartment, the factor values of all predictors with a one-to-many cardinality – i.e. furnishing features m and neighbourhood amenities a – were permuted to obtain all a⋅m factor combinations. E.g. a given apartment features both a garden and a fitted kitchen, so m=2. If this apartment is then located near both an urban green area and local suppliers, also a=2, and predictions thus need to be carried out for all four possible combinations of factors, with the values of all remaining predictors being held constant. The predicted likelihoods of residential choice for all factor combinations were subsequently averaged per apartment and then aggregated at the level of SHUs for further analysis, including hot-spot and cold-spot analysis using local G* statistics as implemented in the R package spdep.
Trends in the number of apartments offered for rent and demanded inclusive rent (EUR) averaged per SHU, per time step (a); identified SHU as prediction targets and modelled spatial housing attributes (b).
.
Results
Figure summarizes the non-spatial housing attributes of the
advertised apartments. A total of N=25579 apartment listings were considered
in this analysis; for the period 2008/09, n2008=5468; for 2013/14,
n2013=10803; and for 2018/19, n2018=9308 (Fig. ). In
this context, it is important to note that this does not necessarily
correspond to the number of apartments available for rent. Instead, a single
apartment could be advertised multiple times, e.g. in the case of short rental
periods. The listings were geolocated to 132 different SHUs, out of a total of
455 SHUs identified across the whole city of Leipzig.
As shown in Fig. , listings include mostly apartments with a
size of between 40 and 80 m2 and with two to four rooms. The highest
share is of the Wilhelminian house type – i.e. multi-storey tenement blocks –
followed by buildings constructed in the GDR, i.e. prefabricated housing
estates, and residential parks constructed post-reunification in the 1990s. In
2008 and 2013, a considerable number of apartments in GDR-type housing were
offered in a rather bad condition, i.e. not renovated or requiring
renovation. This number declined substantially in the following period until 2018. The majority of
Wilhelminian housing is offered in good condition (fully renovated), although
a considerable amount is also categorized as only partially renovated. This is
due to the rental object being categorized as only well-kept. Spatial housing
attributes in the form of the derived SHU properties, including the
categorized location and multiculturality as well as
proximity to or the presence of the neighbourhood amenities of major roads, urban
green areas, local suppliers, and pharmacies, are visualized in
Fig. .
Set of household predictors in the form of socioeconomic profiles to represent societal groups differentially vulnerable and/or at risk.
Only 7 % of all SHUs feature a multicultural image. Most SHUs are
attributed to being dominated by single or semi-detached housing
(41.3 %), followed by multi-storey tenement blocks and Wilhelminian house
types (33.4 %), prefabricated GDR housing estates (20.4 %),
and post-reunification residential parks (4.9 %). This contrasts with
the house types offered, which were majorly Wilhelminian style, whilst there
are only few offers of single or semi-detached housing; this could also
explain the high number of SHUs in which no advertisements were geolocated. The
median relative SHU area covered by the buffer area to major roads is equal to
60.8 % (mean 56.3 %); about 42.4 % of all SHUs are
considered to be within 150 m of major roads. The median coverage
of SHUs by the service areas of urban green spaces is equal to 75.3 %
(mean 67.1 %); more than half of the derived SHUs (about
60.9 %) are located within 250 m walking distance of urban
green areas. The median areas covered by the service areas of local suppliers and
pharmacies are 3.6 % (mean 23.8 %) and 4.2 % (mean
27.3 %), respectively, so that only 14.9 % of all SHUs are
located within 500 m walking distance of local suppliers; for
pharmacies, this share is equal to 18 %. Looking at
Fig. , it becomes apparent that the coverage of SHUs by local
suppliers and pharmacies is concentrated in the city centre.
The demanded inclusive rent, averaged across the whole city, was equal to EUR 477
in 2008, was equal to EUR 524 in 2013, and increased further to EUR 642 in 2018. As
shown in Fig. , inclusive rents increase particularly in
the central parts of the city and to a lesser extent in the eastern parts of
Leipzig. However, it is here where a comparatively high number of
apartments are also offered for rent. On the outskirts, particularly in the western
parts of the city, inclusive rents remain lower but so does the number of
apartments listed for rent.
The predicted likelihoods for positive residential choices, averaged at the
level of SHUs per socioeconomic group as described in Table ,
were subsequently summarized into hot spots and cold spots using local G*
statistics . Figure 5 shows the associated z scores for the
three considered time steps. Here, high z scores (z>1.65) indicate
likely hot spots, i.e. a clustering of comparatively high likelihoods of
positive residential choices for a specific socioeconomic group at a given
location. Hot spots are therefore considered to feature a comparatively high
chance that a socioeconomic group moves into (or resides at) the location in
question. Conversely, low z scores (z<-1.65) indicate likely cold spots,
i.e. a grouping of comparatively low likelihoods of positive residential
choices. Consequently, cold spots are considered to feature lower chances of a
given socioeconomic group moving in.
In 2008, the spatial distribution of hot spots and cold spots between the
different socioeconomic groups appears to be rather similar. In all cases, the
western outskirts of the city comprising the district of Grünau, a
prefabricated housing estate district with a rather negative image
, is mostly avoided by all groups. Similarly, locations
on the northern outskirts feature relatively low z scores across all
socioeconomic profiles. However, in the period until 2018, these patterns change
considerably, thereby becoming less similar overall, with many of the changes
being explained by “extreme” locations, such as the very city centre or the
outskirts. The centre loses attractiveness, as indicated by decreasing
z scores. This is particularly true for middle-aged skilled workers; precarious, unemployed persons; and pensioners, i.e. for the socioeconomic groups considered most vulnerable due to comparatively lower incomes; part-time, precarious, or lack of employment; and/or age. A contrary trend of increasing z scores suggests an increasing attractiveness of the corresponding locations. For these vulnerable socioeconomic groups, such a trend can be identified for previous cold spots such as Grünau in the west or locations in the north of the city.
In contrast to the more vulnerable socioeconomic groups, the spatial patterns
of z scores indicating hot spots and cold spots of full-time employed
academics and young adults in education appear to shift less over time. For
these groups, the loss of attractiveness of the city centre is much less
pronounced. It can be noted instead that certain hot spots, e.g. in the
eastern parts of the city, seem to reinforce themselves. For these groups, it
also appears to be the case that certain locations, e.g. Grünau in the
western part of the city, remain rather unattractive, as indicated by
continuously low z scores over time (Fig. ).
Map of local G* z scores indicating likely hot spots and cold spots of the predicted likelihoods of positive residential choices per socioeconomic group for the time steps 2008, 2013, and 2018. Arrows indicate exemplary locations of persisting cold spots (a), reinforcing hot spots (b), hot spots turning into cold spots (c), and cold spots turning into hot spots for unemployed (d) or elderly persons (e). The map furthermore shows the area potentially affected by a 1-in-300-year flood event (HQ300).
Discussion
This case study demonstrates that residential-choice behaviour can inform
disaster risk assessment through several means. First, it has been shown that
the proposed methodology allows for identifying hot spots and cold spots of
residential choice for distinct socioeconomic groups, i.e. groups of
population with heterogeneous preferences. The hot spots of
residential choice are especially considered to highlight where a progressive
concentration of the respective group of the population is likely. Consequently,
the spatial pattern of hot spots is seen to directly reveal the shaping of
exposure and vulnerabilities towards specific hazards through residential-choice processes. The impact on disaster risk becomes specifically obvious
when the elicited hot-spot or cold-spot pattern is overlaid with hazard-prone
areas to account for the hazard dimension of disaster risk. By so doing, areas
of importance for disaster risk assessment can immediately be revealed. For
example, Fig. includes the area potentially affected by a
1-in-300-year flood event, denoted as HQ300. By comparing this area with the
pattern of hot spots, it appears that especially academics and young adults in
education may be particularly exposed to flooding, a trend possibly explained
by previous studies indicating that environmental amenities outweigh possible
risks . Contrary to that, exposure and thus
vulnerabilities to heat stress may be more dominated by the spatial patterns
of the hot spots of the elderly and deprived socioeconomic groups
.
Second, it has been shown that the proposed methodology allows for detecting
changing patterns of residential-choice behaviour over time, e.g. cold spots
becoming more attractive, as well as hot spots “cooling”, i.e. losing
attractiveness. Particularly the former are considered to be of relevance in
disaster risk assessment, as such “warming” cold spots could be highlighting
spatial shifts in exposure and vulnerabilities, thereby possibly forming
future hot spots of disaster risk. It is consequently such areas that could
become a priority for intervention, and by bringing such potential hot spots to
the attention of decision-makers, timely and proactive instead of rather
reactive adaptation measures might be taken. In the case of heat stress, for
instance, greening measures could be implemented for heat adaptation in
evolving hot spots with low green-space accessibility and thus lack of cooling
potential . Similarly, in the case of
flooding, the implementation of both structural and non-structural
(behavioural) flood protection measures may be facilitated. Such mitigation
and adaptive action address vulnerabilities and exposures ,
thereby promising large potential for a reduction in damage and disaster
risk .
Moreover, spatially co-located hot spots of residential choice for different
disadvantaged socioeconomic groups may be highlighting strong competition
between these demand groups and may furthermore be indicative of conflicts in
urban planning, e.g. due to diverging interests and needs of the said demand groups for the
development of residential areas vs. the implementation
of greening as a risk adaptation measure or for the improvement of environmental
justice. It is consequently through such “feedbacks” that links between (the
prediction of) residential-choice behaviour, disaster risk assessment, and
urban planning become apparent, and the role of urban planning in managing
disaster risks, climate change adaptation, and human health and wellbeing is
emphasized clearly. In this context, the proposed method could point to
relevant process chains between urban drivers, housing-market dynamics, and
disaster risk management, thereby inviting research and action to address
developmental shortcomings or planning weaknesses.
Third, by providing disaster risk assessment with a spatially explicit model
of residential choice, the spatial outcomes of a multitude of urban processes
influencing residential-choice behaviour become incorporated into the disaster
risk assessment process. Thereby, additional bodies of knowledge are tapped
into, and bridges are built between different scientific disciplines. In so doing,
novel insights may be obtained allowing for a more holistic and integrative
perspective on disaster risk, and a better understanding of the importance of
urban processes in the driving and shaping of exposure, vulnerabilities, and risks
may be achieved . In the context of the presented case
study, these processes include (eco-)gentrification, segregation,
polarization, and ageing, each influencing the formation of both hot spots and
cold spots. In the case of comparatively privileged socioeconomic groups such as
academics, hot spots may indicate an increasing (self-reinforcing)
concentration of potentially exposed (material, economic) assets at risk. For
socioeconomically disadvantaged or more vulnerable groups of people such as
the unemployed or the elderly, hot spots may however put emphasis on locations
of increasing socioeconomic vulnerabilities. In contrast, cold spots reveal
evasive behaviour of specific socioeconomic groups, e.g. due to increasing
rents. This becomes apparent in the wider city centre, which appears to be
increasingly avoided over time by pensioners and the unemployed, who in turn
shift, at least partially, towards the prefabricated GDR real-estate complexes
such as Grünau (Fig. ). These findings are in line
with previous case studies for Leipzig, e.g. on the centrally located
Lene-Voigt-Park, where greening led to inner-city urban renewal resulting in
an influx of higher-income families, rising rents, and a subsequent exodus of
less privileged groups , or on the risk of the
accumulation of a socially weak and ageing population in the large prefabricated
GDR housing estates . Hereby, the importance of selected
predictors in the shaping of patterns of vulnerability and exposure is
emphasized once more; for example, rent was identified to be amongst the three
most important predictors of residential-choice behaviour by
. Furthermore, it becomes clear that the presented approach
is a means for detecting and communicating social phenomena associated with
complex urban processes.
Whilst we believe that disaster risk assessment is brought forward by the
proposed approach through informing the dimensions of exposure and
vulnerability by incorporating heterogeneous preferences of distinct
sociodemographic and socioeconomic groups, several shortcomings of the
presented approach need to be identified. These include the overall data
availability and completeness of data, e.g. regarding neighbourhood amenities
such as local suppliers or pharmacies. In this context, due to re-using a
pre-trained machine-learning algorithm, the choice of predictors and
corresponding categorial values was also limited. Shortcomings further include the
spatial resolution of the SHUs for the geolocation of apartment listings, which
is obviously dependent on the way data were provided in the scientific-use
file but which is clearly too coarse to depict spatial manifestations of
“hyper-local” urban processes such as redevelopments, retrofitting, or urban
infill in high detail, i.e. at site level. The SHUs' coarse
spatial resolution thus compounds the quality of predictions of residential
choice through the limited spatial representation of housing attributes, which
had to be approximated at the level of SHUs. For example, in the case of house
type, a dominant house type had to be elicited, thereby possibly neglecting
other house types within a given grid cell.
In contrast to other case studies, transferability is limited due to the
reliance on case-study-specific data and due to the specific local patterns and trends at play. However, the overall analytical lens of detecting
patterns of residential choice based on tacit knowledge, i.e. unconscious
knowledge tied to personal experiences embedded into a
broader setting of urban development, is a unique approach which will be of
increasing relevance for cities facing similar trends of built-up and climate
changes . In this regard, revealing spatially explicit
trends and shifts in heterogeneous groups of population and thereby enabling
more precise ex ante analysis, the proposed methodology could be particularly
useful for urban-planning authorities of cities in less developed countries,
where census data are less reliably available, thus calling for alternative
data sources .
It furthermore must be noted that the presented case study does not consider
preferences or spatial attributes evolving over time, a limitation deriving
from a lack of training data before 2018. Consequently, the residential-choice
predictions for the time steps 2008 and 2013 assume invariant (homogeneous)
preferences, as well as a constant importance of predictors. This shortcoming
may however be alleviated by adapting the proposed methodology to enable
continuous and incremental training – e.g. with online random forests
or Mondrian forests , each
allowing for so-called online training – as part of long-term panel
studies. Other machine-learning algorithms that are capable of handling mixed
data such as neural networks may also be investigated . Such
longer-term studies could facilitate disaster risk assessment by further
strengthening the linkages between urban planning and disaster risk
management.
Conclusions
This paper proposes a methodology for the spatially explicit prediction of
residential-choice behaviour in the form of hot spots and cold spots for
distinct socioeconomic groups, a process seen to (co-)govern spatial patterns
of exposure and vulnerabilities and subsequently disaster risk. Through the
lens of predicting residential choice, the proposed methodology enables
disaster risk assessment and management to improve (ex ante) analysis of the
highly dynamic spatial shifts and resulting distribution of the urban population
and to tap into additional bodies of knowledge, e.g. through making
heterogeneous preferences of different socioeconomic groups accessible. In so
doing, the assessment of exposure, vulnerabilities, and disaster risk is
brought forward. An interesting avenue for future research includes the
revision of predictors alongside the perpetuation of the methodology to allow
for online training. Thereby, additional components of vulnerability, exposure,
and disaster risk such as coping, preparedness, or adaptation could be
incorporated more specifically. In so doing, linkages between the disaster
risk community and environmental justice, e.g. in the form of green-space
accessibility, would be explored further and operationalized in more detail.
Data availability
The scientific-use file with apartments listed for rent can be obtained by delivery as indicated in the referenced DOI 10.7807/immo:red:wm:suf:v1.
Author contributions
SS, DH, AH, MW, and TW were responsible for conceptualization of the case study. Development and implementation of the methodology, formal analysis, and visualization were by SS. Data acquisition was by MW and SS. The original draft was written by SS with contributions from all co-authors. Funding acquisition was by DH.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Groundbreaking technologies, big data, and innovation for disaster risk modelling and reduction”. It is not associated with a conference.
Acknowledgements
Dagmar Haase and Manuel Wolff were supported
as part of the project ENABLE, funded through the 2015–2016 BiodivERsA COFUND call for research proposals, with the national funders the Swedish Research Council for Environment, Agricultural Sciences, and Spatial Planning; Swedish Environmental Protection Agency; German Aerospace Center; National Science Centre (Poland); Research Council of Norway; and Spanish Ministry of Economy, Industry and Competitiveness. In addition, Dagmar Haase and Sebastian Scheuer benefited from the GreenCityLabHue Project (FKZ 01LE1910A) and Dagmar Haase, Sebastian Scheuer, and Manuel Wolff from the CLEARING HOUSE (Collaborative Learning in Research, Information-sharing and Governance on How Urban tree-based solutions support Sino-European urban futures) Horizon 2020 project (no. 821242). Sebastian Scheuer was additionally supported by the 2018 Summer Academy on World Risk and Adaptation Futures: Urbanization, hosted by the Institute for Environment and Human Security (UNU-EHS) of the United Nations University and the Munich Re Foundation (MRF). Thilo Wellmann receives a scholarship from the Deutsche Bundesstiftung Umwelt (DBU; German Federal Environmental Foundation). We acknowledge support by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universität zu Berlin.
Financial support
This research has been supported by the Bundesministerium für Bildung und Forschung (grant no. 01LE1910A) and Horizon 2020 (grant no. 821242).
Review statement
This paper was edited by Mario Lloyd Virgilio Martina and reviewed by Philippe Ker Rault and Georgia Papacharalampous.
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