# Managing padel-court siting near housing: guideline adherence shortfalls and populations at risk – national evidence from France

Jean-Charles Dufour, Christophe Bonnet

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Jean-Charles Dufour, Christophe Bonnet. Managing padel-court siting near housing: guideline adherence shortfalls and populations at risk – national evidence from France. City and Environment Interactions, 2026, 29, pp.100300. 10.1016/j.cacint.2026.100300. inserm-05483224

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Research Articles

# Managing padel-court siting near housing: guideline adherence shortfalls and populations at risk – national evidence from France

Jean-Charles Dufour<sup>a, *, D</sup>, Christophe Bonnet<sup>b</sup>

ARTICLE INFO

Keywords:

Environmental noise

Setback distances

Line-of-sight

Dasymetric allocation

Siting policy

Spatial equity

Urban planning

City governance

A B S T R A C T

**Background:** The rapid rise of padel in Europe, and of pickleball in North America, has created noise-related land-use conflicts in residential areas. Guidance recommends minimum setback distances, yet there has been no nationwide assessment of how many sites fall short or which populations may be exposed. We develop a GIS screening approach that combines proximity (edge-to-edge distance) and visibility (line-of-sight, LoS) to flag potentially high-exposure configurations and quantify affected residents, framed by urban-morphology and environmental-justice debates.

**Methods:** We compiled a database of 878 active padel sites in metropolitan France from open sources, geocoded each site, and classified them by distance from the nearest dwelling and presence of effective built screens. For sites falling short of recommended setbacks (Class 1), we identified residential buildings within 100 m, determined LoS, and allocated population and households to buildings by dasymetric floor-area weighting of INSEE 200 m grid data. The screen deliberately trades acoustic modelling for scalability and transparency.

**Results:** Nearly one third of sites (271/878, 30.9%) were Class 1, with higher concentrations in lower-density communes. Across these sites we identified an estimated 17,100 residents living within 100 m of a padel court. Buildings with direct LoS were closer on average than screened buildings and accounted for most residents within 50 m.

**Conclusions:** Combining proximity and visibility in a GIS screen pinpoints where racket-sport noise exposure is most plausible. The results support context-modulated setbacks, distance-triggered acoustic studies, and post-implementation follow-up, and offer planners a reproducible, low-cost tool that complements detailed acoustic modelling.

Introduction

Emergence of padel and pickleball as urban land-use conflicts

The rapid growth of certain racket sports is creating new land-use conflicts and environmental management challenges in residential areas worldwide. In Europe, the swift proliferation of padel facilities has led to widespread complaints about disturbing sound levels, intrusive lighting and visual intrusiveness around courts [1]. A parallel phenomenon is occurring in North America with the meteoric rise of pickleball, a sport that generates similar psychoacoustic issues and intense community opposition [2,3]. In both cases, the pace of facility development has outstripped the implementation of clear planning controls

and regulatory safeguards, raising questions about how cities manage emerging sports facilities in close proximity to housing.

Padel is a four-player sport played with solid, stringless rackets, using a ball similar to a tennis ball, on a 20 × 10 m court enclosed by glass walls and metal mesh fencing [4]. The sport has sustained growth in many countries [5]. In France, the number of participants rose from 110,000 in 2020 to 500,000 in 2025, and the French Tennis Federation (FFT), which has held the ministerial delegation for the sport since 2014, plans to nearly triple the number of courts by 2029 to meet persistent demand [6]. However, the siting of padel courts, especially in residential, urban and peri-urban settings, raises coexistence issues with nearby residents [1]. The sport's acoustic signature, comprising dry, impulsive sounds from ball-racket and ball-glass impacts, metallic clangs on the

<footnote><sup>a</sup> Aix Marseille Univ, APHM, Inserm, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, ISSPAM, Hop Timone, BioSTIC, Biostatistique et Technologies de l'Information et de la Communication, Marseille, France</footnote>

<footnote><sup>b</sup> Independent Researcher, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: jean-charles.dufour@univ-amu.fr (J.-C. Dufour).</footnote>
wire-mesh fence, and players' vocalizations, generates substantial annoyance for nearby residents and has become a leading cause of legal disputes in several countries [7,8]. Acoustic studies report sound power levels around 89–91 dB(A), with peaks exceeding 100 dB(A), and comparisons with tennis indicate higher perceived annoyance for padel [9,10,11]. International experience, such as the large-scale expansion of padel centres in Sweden, illustrates how market-led growth can outpace planning controls and raise broader environmental concerns [12].

## Environmental noise and health implications

These controversies sit against a broader backdrop of evidence on the health effects of environmental noise. Chronic exposure to community noise is associated with cardiovascular, metabolic and mental health impacts, and psychoacoustic studies show that repetitive, impulsive noises are particularly difficult to tolerate and linked to stress, sleep disturbance, anxiety and mood disorders [13,14,15,16,17,18,19,20,21,22,23,3]. Promonged exposure to repetitive, impulsive noises specific to padel has been documented in cases of anxiety-depressive disorders that resulted in criminal convictions in Spain [24]. In France, the Hauts-de-France Regional Health Agency (Agence Régionale de Santé des Hauts-de-France, ARS Hauts-de-France) has added padel activity to the list of developments requiring particular vigilance, alongside shooting ranges, carwash facilities, and venues broadcasting amplified sounds, because of the substantial noise nuisance they can generate [25,26]. This convergence of widespread padel deployment, sensitive acoustic characteristics and growing health concerns makes padel an instructive case for examining how planning and environmental governance respond to novel sound sources.

## Conceptual framework: urban morphology, noise externalities and environmental justice

From an urban-planning perspective, padel-court siting can be understood as a problem of spatial externalities: sounds generated within a facility propagate beyond its boundaries, potentially imposing environmental burdens on nearby residents. Urban morphology (the configuration of plots, blocks, street networks and building typologies) conditions how these externalities are transmitted in space. In particular, separation distance and mutual visibility between source and receptor are key morphological attributes that shape how sound decays with distance and whether it is shielded or channelled by buildings. Recent work in urban morphology and sustainable urban form has developed conceptual models and spatially hierarchical assessment frameworks that explicitly link built-form attributes to sustainability outcomes and planning decisions [27,28,29]. In parallel, urban-equity research underscores the need for systematic, process-driven interventions to rectify inequities in access to services and to distribute environmental burdens fairly. Nikoofam and Hoşkara identify eight key interventions organised into three design strategies that translate equity principles into tangible planning actions, an approach that resonates with concerns about disproportionate exposure to padel noise across settlement types [30]. Methodologically, multi-criteria decision frameworks using fuzzy analytic hierarchy and network processes have been applied to siting problems such as multi-storey parking in Shiraz's central business district, weighing criteria like proximity to activity hubs, accessibility, land prices and population density [31]. Such studies highlight the trade-off between model complexity and the need for transparent, auditable tools that planners can readily use. Finally, the rapid emergence of low-cost Internet-of-Things monitoring systems for energy efficiency demonstrates how simple, scalable technologies can support decarbonisation in the built environment [28,29]. These strands collectively argue for indicators that are both conceptually grounded and operationally feasible. Our choice of a distance + line-of-sight (LoS) screen echoes this logic: it is simple enough to be operationalised nationwide, yet explicitly grounded in morphological and

environmental-justice concepts. In practice, minimum setback distances and effective built screens are simple spatial planning tools for managing noise externalities: they limit how far padel noise travels beyond the site and how much of it is imposed on neighbouring dwellings. Moreover, the same spatial configuration (short setbacks, direct LoS and lack of effective building screens) also governs light spill from floodlights and the visual intrusion of padel structures. As such, the distance + LoS screen can be read as a first-order indicator of where broader amenity conflicts (noise, lighting and visual intrusion) are most plausible in residential settings. Environmental-justice perspectives emphasise who is exposed, who takes part in siting decisions, and whose complaints are recognised when conflicts arise. Combined with the diversity of regulatory approaches (from non-binding guidance to more unified national rules), these perspectives suggest that padel-related noise conflicts depend not only on decibel levels, but also on the interaction between built form, local institutions and power relations among affected residents.

## Regulatory context, knowledge gaps and study aims

Several countries have begun to respond to padel-related noise through distance-based guidance. In the Netherlands, a collaborative process involving the national tennis and padel federation, municipal associations, court builders and noise experts produced the “Handreiking Padel en Geluid” (Padel and Noise Guide) which recommends a minimum distance of 100 m between padel courts and dwellings, with larger distances in quiet residential areas and for multi-court facilities [32]. This document is among the most explicit and quantified worldwide and serves as a reference for local authorities and Dutch judiciary. In Wallonia (Belgium), a public acoustic study similarly concludes that distances “beyond 100 m” are necessary to limit nuisance around outdoor padel courts [11]. In France, the FFT’s technical study [33,34] proposes distance bands (e.g. low risk beyond 100 m, high risk between 50–75 m, and strong discouragement below 50 m without substantial acoustic treatment), and regional health agencies recommend prior acoustic assessment and avoiding siting within 50 m of dwellings [26,7]. Operating permits for padel courts are granted at municipal level within the framework of local urban plans, and municipalities are not legally obliged to follow FFT or health-agency guidance when authorising new facilities. Noise reduction is nevertheless a stated objective of the National Health-Environment Plan 4 [35], and initiatives such as the “Quiet” label promoted by the Conseil National du Bruit and CidB explicitly encourage better sound environments around leisure infrastructures [36]. Recent parliamentary questions in the National Assembly show that padel-related noise has already entered the national political agenda [37], but setback practices and potential exposures are still not monitored systematically. This fragmented, largely non-binding landscape further motivates simple, auditable tools that can inform planning decisions and complement existing guidance.

Despite these emerging regulatory responses, significant knowledge gaps remain. Existing research on padel is dominated by analyses of performance, biomechanics and play patterns, with environmental nuisance and built-environment proximity rarely treated as primary objects of study [38]. While GIS-based noise and population mapping frameworks exist for other sources, such as road traffic, there is, to our knowledge, no peer-reviewed national study that systematically inventories padel sites near dwellings, classifies their potential exposure configurations, and estimates the populations potentially affected. Nor has the padel/pickleball phenomenon been widely examined through the lenses of urban morphology, environmental justice and regulatory governance as a new category of noise-driven land-use conflict.

From a population-health perspective, potential exposure to community noise is a relevant determinant of well-being even when health outcomes are not measured. Our objective is therefore not to model acoustic propagation or to quantify health impacts, but to identify where exposure is most plausible and who is likely affected, as a basis for
primary prevention and planning. We interpret patterns across settle-
ment density to highlight distributional aspects of potential exposure.
Using proximity as a policy-relevant proxy for potential exposure is
consistent with existing regulations and recommendations mentioned
above and with planning studies that rely on distance-based indicators
when source-specific measurements are unavailable or impractical at
scale [39,40]. We extend this logic by pairing simple distance bands
with a line-of-sight screen to approximate the presence of intervening
built barriers, making the results directly translatable into siting
decisions.

This article addresses these gaps by developing and applying a sim-
ple, auditable “distance + line-of-sight” GIS screening framework for
padel-court installations. Specifically, we build a national geodatabase
of active padel facilities by aggregating open sources, classify each site
into exposure classes based on distance to the nearest dwellings and the
presence of effective building screens, and estimate the number of res-
idents and households potentially exposed around sites that fall short of
recommended setbacks. In practical terms, the recent FFT distance
recommendations provide our reference “compass”: we quantify to what
extent existing siting practices respect these indicative setbacks and how
many residents live in configurations considered high-risk by the na-
tional federation.

Our contributions are threefold. Empirically, we provide the first
national-scale spatial characterisation of guideline-adherence shortfalls
for padel courts and a quantification of the populations living in
potentially high-exposure configurations. Methodologically, we
demonstrate how a reproducible distance + visibility screen can
generate policy-relevant evidence at national scale in settings where
systematic noise monitoring and detailed acoustic modelling are absent
or impractical. Conceptually, we situate padel-court siting within
broader debates on urban morphology, environmental justice and the
regulatory governance of environmental externalities, showing how the
interaction between built form and institutional arrangements struc-
tures who bears the burdens of rapidly expanding racket sports such as
padel and pickleball.

Materials and methods

Identification and categorization of sites

We compiled a database of active padel sites using the public "Padel Speak" directory as our primary source [41]. We first excluded entries explicitly labelled as "planned" and retained all facilities marked as open or active. Each remaining record corresponds to a club or sports facility hosting one or more padel courts. We then cross-referenced this list with the national open government registry of sports facilities and filtered entries labelled "padel" to check coverage and refine site attributes [42]. When a government record clearly matched an existing Padel Speak facility (similar name and address in the same municipality), we used it to complete or correct address information and other attributes. The government registry contained approximately 500 padel-labelled facilities, all of which could be matched to Padel Speak sites; no additional active sites were identified beyond those already present in Padel Speak. All retained facilities had sufficiently precise address information to be geocoded via Google Maps, and no sites were excluded because of geocoding failure. After excluding planned facilities and harmonising sources, the final database comprised 878 active padel sites across metropolitan France. In what follows, a "site" refers to a distinct facility (club or sports complex) hosting one or more courts.

Criteria for potential noise exposure

For each padel site, the potential noise exposure of nearby dwellings was classified into three categories using the criteria described below. This classification relied on GIS tools (Google Earth Pro, Google Maps, OpenStreetMap and Géoportail) for distance measurements and topographic review and was complemented by open imagery (Google Street View; photographs on club websites and social media; press photos).

Each padel site was classified into one of the following three mutu-
ally exclusive categories according to these criteria:

**Class 1: Potentially high exposure**

This class groups sites with a high-risk configuration for noise nuisance. A site was assigned here if it met at least one of the two criteria below:

A) Open or semi-covered courts. The distance from the edge of the nearest court to the closest dwelling façade is $\leq 100$ m, with no major acoustic screen (continuous, massive building; noise barrier; or significant topographic relief) on the direct LoS between the source (court) and the receptor (dwelling).

B) Covered but non-insulated courts. The distance from the structure to the closest dwelling façade is $\leq 50$ m, and the structure is lightweight with no evident acoustic insulation (e.g., single-sheet metal panels, bubble-type enclosure, non-masonry shed).

Class 2: Potentially low or no exposure

This category includes padel sites where the risk of noise nuisance is considered negligible. A site was assigned to this class if it met at least one of the following criteria:

A) Sufficient setback. The distance from the court (or its enclosing structure) to the closest dwelling façade is > 100 m.

B) Structural acoustic insulation. The court is fully enclosed within a masonry building (e.g., concrete, cinder blocks) or shows clear acoustic treatment (e.g., acoustic sandwich panels), even when the distance is < 100 m.

C) Presence of another, closer dominant noise source than the padel site (e.g., highway, railway).

Class 3: Indeterminate exposure

This category is reserved for cases where the available data did not
allow a reliable assignment to Class 1 or 2. The reasons for Class 3 were
documented and included, in particular:

A) Impossible measurement. Satellite imagery was obscured or outdated, or photographic evidence was unavailable, preventing confirmation of the presence or absence of dwellings within 100 m.

B) Ambiguous acoustic screening. Partial obstacles (e.g., low-rise buildings, discontinuous walls) have made it impossible to determine their sound-attenuation capacity with confidence.

C) Uncharacterizable structure. Street View/photos were missing or unusable, preventing determination of the materials and construction of a covered structure.

**Operational and theoretical rationale.** Class 1 is intended to capture configurations that typically fall short of commonly cited siting guidance: courts located within about 100 m of dwellings when open or semi-covered and not shielded by effective built screens, or within about 50 m when housed in lightweight covered structures with limited intrinsic sound insulation. Conversely, Class 2 corresponds to configurations that broadly align with indicative setback distances or that benefit from substantial acoustic shielding by buildings or more insulating enclosures. The underlying thresholds derive from basic acoustic-exposure principles. For an outdoor source approximated as a point source in free field, sound pressure level decreases by roughly 6 dB with each doubling of distance, so increasing separation substantially reduces average façade levels. Continuous, massive obstacles such as building façades or solid noise barriers can further attenuate direct transmission of impulsive noise by several decibels, whereas lightweight elements (wire-mesh fences, vegetation) provide little shielding. Setbacks on the order of 100 m for open or semi-covered courts and 50 m for covered structures are consistent with guidance issued by French and Dutch organisations and with acoustic studies showing that, beyond such
distances, padel noise approaches background levels except for isolated peaks [32,11,33]. The addition of a line-of-sight criterion translates morphological considerations (specifically the presence or absence of intervening buildings) into an indicator of potential acoustic shielding. By combining distance bands and LoS, the classification system operationalises acoustic physics in a simple, reproducible way that aligns with the ecological logic of other distance-based environmental screens used in environmental epidemiology and planning when detailed source-specific modelling is infeasible at national scale [39,40].

## Noise-exposure assessment workflow

Two examiners classified the padel sites in three phases:

1. Phase 1. Independent, mutually blinded classification.

2. Two examiners independently classified all sites using the pre-specified criteria, blinded to each other's labels and notes and to project-wide prevalence. Inter-rater agreement (Cohen's κ) was computed on Phase-1 labels only.

3. Phase 2. Cross-review and re-assessment.

4. Sites with discrepant classifications were flagged. The examiners exchanged worksheets to see each other's initial label, notes, and evidence. Based on this cross-review and additional independent checks, each examiner either revised or confirmed their own classification, still without consultation.

5. Phase 3. Consensus meeting.

6. The remaining disagreements were isolated and resolved by discussion to assign a consensus class.

Inter-examiner agreement was assessed with Cohen's kappa (κ) computed from the classification contingency matrix, with 95% confidence intervals (CIs) obtained by bootstrap. To limit observer bias, every site assigned to Class 1 was verified via an automated geometric test in QGIS: the presence of at least one residential building at $\le 100$ m in direct LoS, operationalized as the minimum edge-to-edge distance segment not intersecting any intervening building polygon (line-of-sight/acoustic screen).

## Spatial distribution of sites

We examined the regional and municipal distributions of Class 1 padel sites using a standardized over-representation ratio (SIR), defined as Observed/Expected, where $Expected = (total number of sites in the region) \times (national proportion of Class 1)$. 95% CIs for SIR were obtained by exact Poisson inversion (Garwood method). Overall heterogeneity was tested with a Pearson $\chi^2$ test.

Sites were grouped according to INSEE's communal density grid [43], after merging the two rarest categories into a single "Rural (dispersed + very dispersed)" class. Given the ordinal nature of the INSEE categories, we applied a Cochran-Armitage trend test (scores 1→6), weighted by stratum size. 95% CIs for category-specific SIRs were computed using the exact Garwood method. Class 1 proportions by category are reported descriptively.

## Exploration of residents' exposure

The analysis of potential noise exposure for dwellings was carried out in QGIS (v. 3.34 LTS) and focused on Class 1 padel sites. The 2D ground footprint of the reference court (visually selected as the court closest to surrounding dwellings) was modelled by a circular buffer with a 5 m radius centered on the court's midpoint.

Geometric rationale for the circular proxy. Although a rectangular footprint would represent the court more precisely, automated extraction of court geometry and orientation was not feasible at national scale. We therefore approximated the court by a 5 m circle inscribed within the 20 × 10 m rectangle. Mathematically, this circle always lies inside the true court footprint, regardless of orientation, which makes distance measurements azimuth-invariant and ensures that the building-

to-footprint distance is never underestimated. Geometrically, the overestimation of distance is bounded by 5 m along the longitudinal axis (and 0 m along the transverse axis), so any misclassification can only affect buildings lying very close to the 50 m and 100 m thresholds. We judged this conservative bias acceptable for a national screening tool. This choice is consistent with our sensitivity analyses, in which excluding buildings within 45–55 m of the court proxy did not materially alter the visibility–distance patterns or the estimated exposed populations (see "Sensitivity analyses" section and Table S3).

All vector data were projected to RGF93 / Lambert-93 (EPSG:2154) to ensure metric accuracy. Data on buildings within 100 m were extracted from IGN's BD TOPO® (June 2025 edition), which provides, among other attributes, 3D building footprints and a classification of primary use (Institut national de l'information géographique et forestière [44]).

To improve the reliability of building-use classification, we conducted a quality-control check to address limitations of BD TOPO. Buildings labeled Unspecified ("Indifférencié", BD TOPO), potentially dwellings not identified as such, were manually verified using iconographic sources (IGN orthophotography, Google Earth Pro). When confirmed, these buildings were reclassified as Residential ("Résidentiel"). Buildings labeled Residential were retained for the exposure analysis. Non-residential buildings were nevertheless retained in the dataset to evaluate screening effects in the line-of-sight visibility analysis.

## Visibility analysis

For each padel site, we performed a 2D proximity analysis in QGIS. First, buildings within 100 m were selected based on the shortest Euclidean edge-to-edge distance between the reference-court footprint and each building footprint (represented as a straight-line segment). We then conducted a line-of-sight analysis to classify buildings: a building was labeled "direct" if the segment was uninterrupted; it was labeled "masked" if any other building, regardless of use or height, intersected that segment (i.e., acted as an intervening screen).

Because harmonised building-height information was not consistently available in public datasets, the visibility analysis was performed in two dimensions using building footprints. For our purposes, the presence or absence of a continuous built footprint along the straight line between court and dwelling is the primary determinant of whether a building can function as a potential acoustic screen; height differences mainly modulate the degree of attenuation rather than the basic distinction between "screened" and "unscreened" configurations. This simplification is conservative: some buildings classified as screens may in practice be too low to provide substantial shielding, whereas a full three-dimensional model incorporating height and material data would refine the line-of-sight classification. Conversely, tall buildings that exceed court height will still be captured as screens in 2D. Similar 2D proxies are widely used in large-scale environmental-justice and exposure-screening studies when high-resolution 3D inputs are unavailable or impractical [39,45]. Future work could integrate LiDAR-derived building heights or 3D city models to reduce misclassification in borderline cases, but we consider the 2D approach adequate for a first-order national screening tool.

## Floor-area-weighted sociodemographic allocation and uncertainty intervals

To characterize exposed populations, for each residential building *b* within INSEE tile *c* (FILOSOFI 2019, carroyage 200 m [46]), we compute a floor area as $S_b = A_b \times \max(1, E_b)$ (building footprint $\times$ total number of floors, including the ground floor; default floor count = 1 if missing). The tile's total floor area is $S_c = \sum_{k \in B_c} S_k$, and the allocation weight for building *b* is $P_b = S_b/S_c$. INSEE tile totals (individuals, households) are then redistributed in proportion to this weight so that the sum over buildings within a tile exactly matches the observed tile totals.

To quantify allocation uncertainty for a subset *Z* (e.g., direct 0–50 m),
we compute, for each tile, the share $p_c = \sum_{b \in Z \cap B_c} p_b$, bounded in $[0, 1]$. Assuming that individuals (or households) within the tile are randomly assigned according to this share, the exposed count follows $Y_c \sim \text{Binomial}(X_c, p_c)$ (with $X_c = \text{INSEE total for the tile}$). The overall estimate is $\hat{\mu} = \sum_c X_c p_c$ and the variance $\hat{\text{Var}}(\hat{\mu}) = \sum_c X_c p_c (1-p_c)$; the 95% uncertainty interval (UI) is $\hat{\mu} \pm 1.96 \sqrt{\text{Var}(\hat{\mu})}$. These UIs reflect intra-tile allocation uncertainty, not sampling error.

Alternative dasymetric weighting schemes could have been adopted as proxies for dwelling density. For example, allocating INSEE tile totals solely on the basis of building footprint area or incorporating additional attributes such as building typology (e.g. distinguishing multi-storey apartment blocks from single-family houses) or construction period. We chose floor-area weighting because it approximates dwelling capacity, especially in multi-storey buildings, but this implicitly assumes that population-to-floor-area ratios are roughly constant within each tile. In practice, household size and occupancy may vary systematically with building type, so ecological-fallacy and misclassification risks remain [39]. Our uncertainty intervals capture only imputation variability within tiles, not errors in the census data or systematic differences in occupancy by building type.

Distances were summarized at the building level (unweighted) using the mean and the sample standard deviation. The distance bands were 0–50 m and 50–100 m.

Residential buildings located in tiles without available INSEE aggregates (statistical confidentiality) could not be allocated and were excluded from the sociodemographic analysis.

This procedure yields an enriched dataset in which each exposed building is characterized by its distance to the reference court, its line-of-sight status (*direct* or *masked*), and floor-area-weighted estimates of the resident population and households.

**Sensitivity analyses.** Boundary effects were stress-tested (±5 m around 50/100 m) to ensure thresholds do not spuriously drive the results. We conducted two pre-specified checks: (A) exclusion of buildings within 45–55 m of the court proxy to stress-test the 50 m cut-off, and (B) exclusion of borderline line-of-sight (“grazing”) cases identified by intersecting a +1 m buffer of third-party buildings without intersecting their true polygon after line trimming. All analyses reused the ≤ 100 m inclusion, dasymetric floor-area allocation, and binomial intra-tile 95% uncertainty intervals.

### Rationale for policy-oriented metrics

We selected simple, auditable indicators (edge-to-edge distance bands (0–50 m; 50–100 m) and a binary 2D LoS) to enhance policy translatability. Distance bands align with common siting cautions used by local authorities and practitioners, whereas LoS approximates the presence of intervening built screens. Although these indicators are not substitutes for acoustic modelling, they are reproducible in GIS and can be used as preliminary screening criteria to prioritize projects for further assessment. Our choice of proximity and 2D line-of-sight follows established screening practice. In environmental justice research, distance-to-source metrics are routinely used as reproducible indicators of potential exposure where monitoring is sparse or heterogeneous. Similarly, separation distances and setbacks are widely discussed for other noise-generating land uses (e.g., onshore wind [40]), precisely to provide tractable, policy-translatable criteria short of site-specific modelling. We adopt the same screening logic here and complement it with a basic visibility test to reflect the intervening built screens [39].

## Results

At national scale, we document (i) adherence shortfalls (271/878), (ii) a distance-visibility coupling of proximity, and (iii) the order of magnitude of potentially affected residents/households.

### Identification and categorization of sites

As of 1 September 2025, our database contained 878 padel sites, each comprising one or more courts. The variables recorded were: club name, address, municipality, department, and region.

Sites were distributed across 747 of metropolitan France's 34,805 communes, 89 of its 96 departments, and all 13 regions.

The three-phase assessment by the two examiners was conducted over 15 days, from 2 to 16 September 2025. The GIS layers and open imagery consulted correspond to the versions available during this period.

Final distribution (Phase 3, consensus):

* Class 1 (potentially high exposure): 271 sites (30.87%)

* Class 2 (potentially low or no exposure): 567 sites (64.58%)

* Class 3 (indeterminate exposure): 40 sites (4.56%)

A brief exploration of the 40 Class 3 sites showed that they were spread across nine regions and all six INSEE communal density categories, with no single region or category accounting for more than one-third of Class 3 sites; their distribution was broadly similar to that of all padel sites (Tables S2, supplemental material). Given the small numbers and the underlying data ambiguities that define Class 3, we did not pursue more detailed stratified analyses.

The inter-examiner agreement at Phase 1 was 91.23% ($κ = 0.83$; 95% CI [0.79–0.87] obtained by 4,000-replicate bootstrap), and all remaining discrepancies were resolved in the subsequent phases. For all Class 1 sites, QGIS measurements confirmed the presence of at least one residential building within 100 m with direct LoS (i.e., no intervening-built screens; see end of “Spatial distribution of sites” section below).

### Spatial distribution of sites

Table 1 shows that the regional distribution of Class 1 padel sites is not homogeneous in France ($χ^2 = 24.85$; $p = 0.0156$). Provence-Alpes-Côte d'Azur accounts for 14.24% of all sites nationally but 22.88% of Class 1 sites; the within-region proportion classified as Class 1 is 49.6% ($SIR = 1.61$; 95% CI [1.35–1.87]). Conversely, Grand Est ($SIR = 0.58$; 95% CI [0.30–0.89]) and Centre-Val de Loire ($SIR = 0.22$; 95% CI [0.00–0.67]) are under-represented.

An SIR greater than 1 indicates that a region hosts a higher number of potentially high-exposure sites than expected given its share of all sites nationally. In policy terms, regions (or categories) with SIR > 1 warrant heightened scrutiny when authorising new padel developments and may benefit from targeted guidance or mitigation measures to prevent further concentration of at-risk configurations.

The spatial distribution of sites across France is shown in Fig. 1. Site locations were aggregated to the centroid of their respective communes. Point displacement symbology was used to visualize multiple sites in the same commune.

For INSEE's 6-level communal density grid [43], the distribution of Class 1 padel sites shows overall heterogeneity ($χ^2 = 11.20$; $p = 0.0476$). A significant trend also emerges along the density gradient: the proportion of Class 1 sites increases as density decreases (Cochran-Armitage trend test: $Z = 2.66$; $p = 0.0078$). The “Small towns” category is significantly over-represented ($SIR = 1.39$; 95% CI [1.06–1.74]).

As with the regional results, SIR values above 1 in Table 2 signify over-representation of Class 1 sites relative to the national average within a density category. Categories with SIR > 1, including “Small towns” and “Rural (dispersed + very dispersed)”, should be prioritised for scrutiny when planning future facilities. Conversely, categories with SIR < 1 are under-represented and may have greater leeway for development.

Using aerial imagery and club websites, we counted 534 outdoor courts, 37 covered but non-enclosed courts, and 21 courts housed within lightweight enclosed structures across the 271 Class 1 sites. Projecting a
<table><caption>**Table 1**
Distribution of padel sites by region in metropolitan France.</caption><thead><tr><th>Region</th><th>Padel sites (n)</th><th>National share of sites (%)</th><th>Class 1 sites (n)</th><th>Within-region Class 1 (%)</th><th>National share of Class 1 sites (%)</th><th>Class 1 SIR (over-representation ratio) (95% CI)</th><th>Regional pop. density (inh./km²)</th></tr></thead><tbody><tr><td>Provence-Alpes-Côte d'Azur</td><td>125</td><td>14.24%</td><td>62</td><td>49.60%</td><td>22.88%</td><td>1.61 [1.35-1.87]</td><td>166.9</td></tr><tr><td>Auvergne-Rhône-Alpes</td><td>128</td><td>14.58%</td><td>43</td><td>33.59%</td><td>15.87%</td><td>1.09 [0.84-1.34]</td><td>118.5</td></tr><tr><td>Occitanie</td><td>153</td><td>17.43%</td><td>50</td><td>32.68%</td><td>18.45%</td><td>1.06 [0.85-1.27]</td><td>85.3</td></tr><tr><td>Normandie</td><td>29</td><td>3.30%</td><td>9</td><td>31.03%</td><td>3.32%</td><td>1.01 [0.46-1.55]</td><td>111.7</td></tr><tr><td>Pays de la Loire</td><td>39</td><td>4.44%</td><td>12</td><td>30.77%</td><td>4.43%</td><td>1.00 [0.56-1.48]</td><td>122.7</td></tr><tr><td>Nouvelle-Aquitaine</td><td>123</td><td>14.01%</td><td>34</td><td>27.64%</td><td>12.55%</td><td>0.90 [0.65-1.13]</td><td>73.7</td></tr><tr><td>Île-de-France</td><td>81</td><td>9.23%</td><td>22</td><td>27.16%</td><td>8.12%</td><td>0.88 [0.58-1.19]</td><td>1036.5</td></tr><tr><td>Bourgogne-Franche-Comté</td><td>20</td><td>2.28%</td><td>5</td><td>25.00%</td><td>1.85%</td><td>0.81 [0.18-1.45]</td><td>58.5</td></tr><tr><td>Bretagne</td><td>37</td><td>4.21%</td><td>9</td><td>24.32%</td><td>3.32%</td><td>0.79 [0.36-1.26]</td><td>127.8</td></tr><tr><td>Hauts-de-France</td><td>55</td><td>6.26%</td><td>11</td><td>20.00%</td><td>4.06%</td><td>0.65 [0.33-1.00]</td><td>187.8</td></tr><tr><td>Grand Est</td><td>67</td><td>7.63%</td><td>12</td><td>17.91%</td><td>4.43%</td><td>0.58 [0.30-0.89]</td><td>96.5</td></tr><tr><td>Corse</td><td>6</td><td>0.68%</td><td>1</td><td>16.67%</td><td>0.37%</td><td>0.54 [0.00-1.65]</td><td>41.5</td></tr><tr><td>Centre-Val de Loire</td><td>15</td><td>1.71%</td><td>1</td><td>6.67%</td><td>0.37%</td><td>0.22 [0.00-0.67]</td><td>65.9</td></tr><tr><td>All regions (Metropol. France)</td><td>878</td><td>100%</td><td>271</td><td>30.87%</td><td>100%</td><td></td><td></td></tr></tbody></table>

Population density (inhabitants/km²) : INSEE source, « Estimations de population au 1er janvier 2025 » [51]. Values computed/reported using the current regional boundaries. SIR = Observed / Expected; 95% CIs by exact Garwood method.

5 m circular buffer around the midpoint of the court closest to dwellings, and then computing edge-to-edge distances to buildings labeled Residential in BD TOPO, revealed inconsistencies for 11 sites: the minimum distance to a residential building exceeded 100 m, which did not match the situation observed by the examiners. These discrepancies stem from incomplete labelling in BD TOPO. To correct this bias, 32 buildings initially classified as Unspecified (“Indifférencié”, BD TOPO) were reclassified as Residential (“Résidentiel”) after manual verification using GIS sources (Google Earth Pro satellite imagery and IGN aerial orthophotography). These reclassified buildings were then included in the final building set.

## Exploration of residents' exposure

The dataset comprises 7,471 buildings within 100 m of 271 padel sites. Fig. 2 shows an example of a 2D QGIS display of buildings within 100 m of a padel court (circular buffer). Each building is described by its distance to the site (straight-line segment), its visibility status (solid = direct; dashed = masked), and the sociodemographic indicators of its INSEE FILOSOFI 2019 tile (not shown in Fig. 2). Of these buildings, 3,097 are residential (red, orange, sand, yellow) and 4,374 non-residential (gray), the latter being retained solely to characterize visibility/masking. Residential buildings are split into 1,379 buildings with direct LoS and 1,718 masked buildings.

However, the sociodemographic analyses (Fig. 3) are based on a final dataset of 3,037 residential buildings with complete information and 608 INSEE tiles; tiles subject to statistical confidentiality were excluded from allocation, resulting in the exclusion of 60 residential buildings (≈1.9% of the initial 3,097). No cases of invalid tile floor area (≤ 0) were observed.

Among the 3,037 residential buildings not subject to statistical confidentiality, 44.4% have direct LoS and 55.6% are masked. Overall, the floor-area allocation yields an estimated 17,116 people (95% UI [16,952-17,280]) and 7,821 households (95% UI [7,710-7,932]) within 100 m of the installation's reference court, with a mean distance of 71.9 m (±21.0).

Direct buildings account for 8,717 people (95% UI [8,588-8,846]) and 3,945 households (95% UI [3,859-4,032]), at a shorter mean distance (65.8 m, ±22.5) than masked buildings (76.8 m, ±18.4). The share of buildings located at 0-50 m is roughly doubled under direct visibility (11.1%) vs masked (5.6%), and the majority of people at 0-50 m are in direct buildings (3,278/4,370 ≈ 75%).

## Sensitivity analyses (see Tables S1, supplemental material)
Option B (excluding “grazing” LoS) reduced totals modestly (buildings 2,834; people 16,480; households 7,527), preserving the visibility-distance pattern. Option A (excluding 45-55 m) yielded 2,783 buildings, 15,264 people, and 6,962 households. The combined Option A+B gave 2,595 buildings, 14,678 people, and 6,692 households (95% UIs in Tables S1, supplemental materials).

## Discussion

### Main findings and interpretation

The close siting of some padel sites near dwellings, combined with an acoustic signature of impulsive, repetitive sounds, exposes nearby residents to noise nuisances that may adversely affect their quality of life. From an urban-health perspective, a transparent, reproducible siting screen helps prevent noise-related conflicts before they translate into degraded well-being or costly retrofits. Our national analysis reveals that distance-visibility coupling structures potential exposure: direct line-of-sight configurations are more often associated with very short distances and concentrate the most sensitive situations. In a context where recommendations exist but are unevenly known or implemented, these findings argue for clearer siting conditions. Comparable international trajectories, where corporate-led development and eventification have quickly multiplied facilities, underscore the value of simple, auditable siting screens that local authorities can apply consistently before detailed assessment. The Swedish case, for example, highlights how market speed and organisational models shape environmental sustainability and the need for clear planning criteria [12]. Our approach aligns with proximity-based methods long used to flag potential environmental nuisances and inequities, where distance functions as a conservative screen pending detailed assessment. Evidence from other planning domains shows that setback frameworks can guide siting and mitigation decisions even when direct health effects are not evaluated [39,40].

To our knowledge, while GIS-population approaches exist for other noise sources (e.g., road traffic) [47], they have never been applied to padel. To date, no peer-reviewed national study has characterized resident exposure around padel sites via a spatial approach coupled with sociodemographic allocation. Available bibliometric overviews place padel research largely in the fields of play analysis, performance, and biomechanics, with environmental nuisance and built-environment
<figure><img src="image_2.png" /><figcaption>Fig. 1. Geographic distribution of padel sites across metropolitan France's regions.</figcaption></figure>

<table><caption>**Table 2**
Distribution of padel sites by INSEE's communal population density grid.</caption><thead><tr><th>INSEE communal density category</th><th>Sites (n)</th><th>National Share of sites (%)</th><th>Class 1 sites (n)</th><th>Within-category Class 1 (%)</th><th>National share of Class 1 sites (%)</th><th>Class 1 SIR (over-representation ratio) (95% CI)</th></tr></thead><tbody><tr><td>Large urban centers</td><td>276</td><td>31.44%</td><td>72</td><td>26.09%</td><td>26.57%</td><td>0.85 [0.70–0.99]</td></tr><tr><td>Intermediate urban centers</td><td>181</td><td>20.62%</td><td>59</td><td>32.60%</td><td>21.77%</td><td>1.06 [0.86–1.25]</td></tr><tr><td>Urban belts</td><td>153</td><td>17.43%</td><td>43</td><td>28.10%</td><td>15.87%</td><td>0.91 [0.70–1.13]</td></tr><tr><td>Rural towns</td><td>139</td><td>15.83%</td><td>43</td><td>30.94%</td><td>15.87%</td><td>1.00 [0.77–1.24]</td></tr><tr><td>Small towns</td><td>79</td><td>9.00%</td><td>34</td><td>43.04%</td><td>12.55%</td><td>1.39 [1.06–1.74]</td></tr><tr><td>Rural (dispersed + very dispersed)</td><td>50</td><td>5.69%</td><td>20</td><td>40.00%</td><td>7.38%</td><td>1.30 [0.88–1.73]</td></tr><tr><td>All INSEE categories (Metropol. France)</td><td>878</td><td>100%</td><td>271</td><td>30.87%</td><td>100%</td><td></td></tr></tbody></table>

The INSEE categories “Rural dispersed” and “Rural very dispersed” were merged.

proximity not treated as primary objects of study [38]. Our work therefore fills a gap by documenting, at the national scale, potential exposure configurations and the order of magnitude of the populations concerned.

At the scale of metropolitan France, our work shows that approximately one third of padel sites do not meet recommended setback distances, suggesting noise-nuisance exposure for thousands of nearby residents. Moreover, the over-representation of the most problematic
<figure><img src="image_3.png" /><figcaption>Fig. 2. Example of a 2D QGIS representation of buildings within 100 m of a padel court.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3. Hierarchical summary of residential buildings within 100 m of Class 1 padel sites, by visibility (direct vs masked) and distance band (0–50 m; 50–100 m).</figcaption></figure>

sites in certain density categories (notably “Small towns”) and the shorter distances observed under direct line-of-sight converge on the idea that the distance-visibility coupling is a major determinant of potential exposure. The concentration of at-risk configurations in less-dense settings suggests possible distributional concerns, as smaller municipalities may have fewer resources for technical assessment and enforcement. These findings arise in a regulatory and guidance landscape (France, the Netherlands, Belgium) that is heterogeneous and, in practice, does not ensure effective control of siting near housing. This pattern also raises environmental-justice concerns, as smaller municipalities with fewer technical and financial resources may struggle to enforce guidance and protect residents from disproportionate noise burdens.

Our observations are consistent with existing recommendations and

technical guides that advocate minimum distances (often ≥100 m, modulated by the number of courts and the urban context) precisely to limit the impact of the activity's impulsive, repetitive sounds. The fact that some sites lie below these benchmarks helps explain the frequently reported mismatch between the sport's rapid growth and the rise in neighborhood complaints. In this context, our spatial approach provides a national quantification of a previously undocumented phenomenon and pinpoints the highest-risk configurations, which is useful for prioritizing actions (prevention, mediation, urban planning and noise nuisance mitigation) [48,35].

### Comparative regulatory perspective

Contrasting regulatory frameworks helps clarify the role that a GIS-
based screening tool can play in a non-mandatory setting. In the Netherlands, the *Handreiking Padel en Geluid*, developed collaboratively by the national tennis and padel federation, municipal associations, court builders and noise experts, provides unified national distance ranges of roughly 100–160 m between padel courts and dwellings, depending on context and number of courts [32]. In Wallonia (Belgium), a public acoustic study similarly recommends siting outdoor padel courts at distances “beyond 100 m” from nearby housing [11]. Our analysis is confined to France, where current practice combines indicative setback ranges proposed by the FFT and regional health agencies with highly decentralised, autonomous municipal permitting [26,33]. Within this fragmented, largely non-binding framework, we find that nearly one third of sites fall within distance bands that French and Dutch guidance describe as high-risk configurations. Placed alongside the more unified Dutch and Wallonia guidance, these findings suggest that a simple national GIS-based distance + visibility screen can help identify potentially problematic siting decisions, guide case-by-case acoustic assessments, and inform future moves towards more coherent national standards. More broadly, this juxtaposition illustrates how differences in governance arrangements shape the spatial distribution of environmental externalities, a core concern of urban-morphology and environmental-justice scholarship.

The overall heterogeneity and the significant trend along with the density gradient (from dense urban to rural) suggest that urban-integration constraints (built fabric, available land, and building screens) and the degree of land-use leeway modulate the likelihood of “too-close” sitings. In dense urban environments, most courts are indoors, and housed in solid, acoustically insulated buildings, which is why we did not classify them as Class 1. In lower-density settings, building screens are less common and outdoor courts are more frequent, exposing dwellings at short distances; this helps explain SIR > 1 in certain categories. As expected, direct LoS is accompanied by shorter distances and a majority of people located very close to the site (0–50 m). Viewed through the psychoacoustics of impulsive sounds, this distance-visibility coupling strengthens the plausibility of greater disturbance, independent of the sheer number of dwellings affected.

## Strengths and limitations

**Strengths.** Our database, designed to be exhaustive as of 1 September 2025, lists 878 active padel sites whereas the FFT reported 834 active padel sites in metropolitan France over the same period [49]. The study benefits from national coverage, a documented, reproducible GIS workflow, and a cautious two-examiner classification, including an indeterminate class when information was ambiguous or missing, plus formal spatial verification via QGIS distance and line-of-sight calculations, which limits the risk of false positives. The reported 95% uncertainty intervals (UIs) for counts (people, households) stem from intra-tile statistical allocation; they are imputation margins, not sampling errors. Sensitivity analyses indicate that our conclusions are robust to borderline visibility classification (Option B), and that the 50 m split does not spuriously drive the results, although excluding the 45–55 m band (Option A) unsurprisingly lowers counts near the threshold. Operational implications therefore hinge primarily on the distance-visibility configuration rather than edge-case geometry.

**Limitations.** We did not conduct acoustic measurements or propagation modelling, and we did not evaluate health outcomes. Our indicators (distance bands and 2D LoS) are therefore screening proxies intended to inform siting and prioritization for further assessment. Our database coverage, although broad, may still be incomplete, leading to under-ascertainment of sites. The approximation of the proxy court footprint (5 m inscribed circle) may overestimate building distances and thus undercount buildings in the 0–50 m and 50–100 m bands; any bias is limited to cases lying within 5 m of the band thresholds. The visibility analysis relies on a 2D model (shortest Euclidean distance between entities) without 3D modelling (altimetry, heights, materials), which may

over- or underestimate actual screening. For multi-court sites, the use of a single proxy court may understate cumulative exposure. Sociodemographic data (INSEE FILOSOFI 2019) are projected onto the 2025 building stock; local changes (construction/demolition, population movements) may introduce temporal mismatch. We used dasymetric allocation proportional to floor area; alternative schemes (footprint-only, different occupancy assumptions, inclusion of structural attributes) could have been considered and may have locally altered the redistributed totals. The reported 95% UIs reflect intra-tile allocation uncertainty. The application of statistical confidentiality led to the exclusion of some buildings, causing localized underestimation despite prorated allocation to other tiles. Finally, we did not model contextual factors (traffic, ambient noise, usage rules), and our exposure indicators (distance-visibility) remain indirect, warranting caution for any health inference. Generalisability beyond France may vary with governance and market models, for example, Sweden's largely corporate padel centres [12], but the distance/visibility screen is designed to be transferable across contexts with appropriate local thresholds.

Finally, several research avenues emerge: coupling 3D geometry and acoustic-propagation modelling with multi-period in-situ measurements; linking these exposures to indicators of annoyance and health (housing turnover/sales, complaints, sleep disturbance, mental health) using existing sources or ad-hoc data collected in experimental settings [50]; and evaluating the effectiveness and cost-benefit of mitigation measures to inform operational decisions.

## Implications for urban policy and planning

Our findings support clearer siting conditions: (i) minimum setbacks modulated by built context and number of courts; (ii) distance-triggered, LoS-informed acoustic studies for projects located <100 m from dwellings; and (iii) post-implementation follow-up (monitoring, mediation, engineered barriers or enclosure). These steps prioritize direct-LoS, very short distance configurations for immediate action and scale to national planning workflows. While our indicators are defined with noise in mind, the same distance and visibility configurations are also those most likely to generate intrusive lighting and visual impacts from padel structures, so the screening tool can help planners anticipate a wider set of amenity conflicts.

## Conclusion

Our results document a gap between recommendations and certain padel site implementations near housing. Our national maps of proximity and LoS highlight where current siting practices are most likely to generate noise-related conflicts, providing a practical basis for distance-triggered acoustic assessment, context-modulated setbacks, and targeted mitigation. In a context of rapid growth, this represents both a public-health concern and a spatial-planning challenge. Beyond these pragmatic implications, the study contributes conceptually by demonstrating how morphological attributes (edge-to-edge distance and line-of-sight) and governance structures combine to produce environmental externalities. Situating the empirical screen within urban-morphology, sustainability and environmental-justice theories highlights the broader relevance of our findings and underscores the need for unified regulatory guidance. Three priorities emerge:

1. an enforceable national framework (distance/typology thresholds, modulated by number of courts and the urban context);

2. a pre-implementation acoustic study using a reproducible protocol for every new project, incorporating in situ measurements and usage scenarios;

3. post-implementation follow-up (complaints, measurements, mediation) to adjust problematic configurations through appropriate engineering solutions (effective barriers, enclosure/acoustic treatment of structures), and, if necessary, relocation of sites.
Taken together, distance and visibility provide an operational screen for siting and mitigation decisions, supporting land-use policies that balance sport growth with nearby residents' well-being. While developed at national scale for France, the indicators and workflow are generic and transferable to other countries with locally adapted thresholds.

## Ethics

No human participants or animals were involved. We used publicly available spatial data; ethics approval was not needed.

## Funding

No specific funding was received.

## Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used ChatGPT 5 Pro (OpenAI) and Gemini Pro (Google) to translate some sentences. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

## CRediT authorship contribution statement

**Jean-Charles Dufour:** Writing – review & editing, Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Christophe Bonnet:** Writing – review & editing, Validation, Investigation, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This research was conducted jointly as part of the corresponding author's academic activities and the national citizen collective NAMP, a non-profit organization of individuals affected by nuisances associated with padel or pickleball activities. All the authors are members of these entities. They declare that they have no other competing interests.

## Acknowledgements

The authors thank the members of the NAMP collective for their support and Professor Jean Gaudart for helpful discussions and methodological advice.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cacint.2026.100300.

## Data availability

Aggregated datasets supporting this study are available from Zenodo (concept DOI: 10.5281/zenodo.17290320). Versioned releases will include any subsequent code and documentation updates.

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