Public participation geographic information system as a tool to study the relationship between reassurance and outdoor lighting

Abstract

Thoughtfully designed outdoor lighting plays a significant role in supporting reassurance after dark. This study aims to explore the use of the public participation geographic information system (PPGIS) methodology to examine the relationship between reassurance and outdoor road lighting, focusing on participants’ everyday experiences. By employing PPGIS, which has seldom been used in urban lighting research, we investigate the associations between reported reassurance and actual lighting conditions, including the presence of lighting features and the requirements for various road lighting classes. Additionally, the study explores correlations between reassurance and perceived lighting parameters, such as overall quality of road lighting and brightness. Our findings confirm that the presence of lighting itself can positively contribute to reassurance and that unlit areas are perceived as less safe in comparison with areas with any lighting (i.e. P3 and P4 lighting classes). We also establish a positive correlation between reassurance and perceived lighting parameters, with distinct differences in these parameters observed between safe and unsafe places, where higher ratings are associated with areas reported as safe. Overall, the PPGIS methodology demonstrates considerable potential for further development and application in outdoor lighting studies, providing valuable insights for urban lighting design.

1. Introduction

Visual information significantly shapes human perception, with up to 80% of our learning, cognition and activities influenced by vision. ¹ As night falls, artificial urban lighting transforms our sensory experiences of outdoor spaces. ² In contrast to homogeneous daylight conditions, numerous light sources with diverse characteristics create a distinct and varied urban environment after dark. This diversity turns urban nightscapes into a canvas with unique lighting scenes that people experience in their daily lives.

In urban lighting research, particularly in human perception and experience studies, reassurance or perceived safety is typically investigated by individuals evaluating different lighting conditions, both in daylight and after dark. ³ Existing studies primarily rely on subjective ratings of predefined and controlled lighting installations, often conducted in laboratories^4,5 or on predetermined routes and sites.^6–8 The availability of modern technologies, such as mobile and web-based applications, opens avenues for innovative digital methodologies, offering new ways to capture insights from people’s everyday living experiences. These methods help to validate and complement the existing body of knowledge. Additionally, digital methods provide benefits such as reaching a larger audience, lowering costs and gathering place-based knowledge across broader urban areas. ⁹

This study aims to explore the use of the public participation geographic information system (PPGIS) methodology to examine the relationship between reassurance and artificial outdoor lighting after dark. By focusing on participants’ everyday, place-based experiences, it seeks to demonstrate the applicability and potential of PPGIS techniques in urban lighting research and to complement existing knowledge on how lighting affects reassurance. Despite its potential, the use of PPGIS in urban lighting research remains underexplored, leaving a gap in understanding how real-world, place-based experiences can be studied with the help of this approach.

1.1 Outdoor lighting and reassurance

Artificial light at night delivers many benefits to people, such as greater safety, reduced fear of crime, increased use of outdoor spaces after dark, the creation of environments that are a source of beauty and entertainment and enhanced economic growth. ³ Furthermore, outdoor lighting contributes to sustainable development goals, including those connected with people’s well-being and safety. ¹⁰ A recent study by Trop et al. ¹¹ highlighted responses to public road lighting, such as safety, comfortableness, pleasantness and discomfort, with safety being the most systematically explored and widely discussed topic.

Reassurance refers to the confidence pedestrians gain from road lighting, particularly when walking alone after dark, ¹² and, furthermore, the term is used as a synonym for perceived safety in this study. Various factors may influence reassurance, including elements from both social context and the physical characteristics of urban environments. ¹³ Aspects such as a sense of community, as well as the presence of people and the resulting natural surveillance, contribute to the social context, ¹⁴ while the quality of maintenance ¹⁵ and lighting ¹⁶ represent important physical environmental characteristics. Specifically, during nighttime, road lighting is widely recognised as the primary physical feature associated with reassurance.^12,17,18 Furthermore, utility lighting, which provides functional illumination for roads, parks and squares, is often linked with safety. ¹⁹

Studies have explored impact of lighting on reassurance by categorising conditions based on significant distinctions, such as the presence versus absence of lighting or comparisons between ‘inadequate’ and ‘adequate’ lighting, treating lighting as a single entity. ¹² Field research has shown that environments lacking road lighting or having poor lighting are generally perceived as unsafe.^20,21 Loewen et al. ²² found that light, whether natural or artificial, was one of the most cited environmental features associated with safety, and outdoor scenes with lighting (i.e. daylight conditions) were consistently rated as safer than those without (i.e. after dark conditions). The mere presence of lighting can directly or indirectly influence people’s reassurance. ²³ On university campuses, adequate lighting was one of the primary reasons for perceiving spaces as safe, while the lack of road lighting contributed to the opposite view. ²⁴ Poor lighting has been strongly linked to fear of crime ²⁴ and has been studied as a barrier to walking and cycling,^25,26 whereas adequate lighting encourages greater pedestrian activity.^27–29

Research has highlighted the importance of various lighting parameters such as illuminance, uniformity and light spectrum in shaping reassurance. ³ Objective measurements of these characteristics are essential for understanding optimal lighting conditions and identifying the transition from ‘inadequate’ to ‘adequate’ lighting. Studies have consistently shown that higher levels of illuminance are generally associated with increased reassurance.^12,30–32 For instance, an optimum illuminance of 10 lx with a high scotopic/photopic (S/P) ratio and lighting directed towards pedestrians and natural elements can enhance reassurance. ¹² Another study suggested that the optimal illuminance range varies depending on the location, from 8.9 lx to 26 lx. ³³ However, studies also indicate that increasing illuminance beyond certain levels yields diminishing returns, with relatively low levels, such as 5 lx to 10 lx, often sufficient to maintain high reassurance.^6,12,34 Additionally, minimum illuminance was found to better predict reassurance than mean illuminance. ⁶

As increased light intensity beyond a certain threshold may not significantly improve reassurance, recent research emphasises the importance of lighting quality, particularly in terms of spectral composition (e.g. Spectral Power Distribution, Correlated Colour Temperature (CCT), CIE General Colour Rendering Index (CRI) and S/P ratios).^11,35 In suburban nightscapes, reassurance has been closely linked to both colour quality and lighting distribution. ³⁶ Specifically, when the colour of light makes the environment appear pleasant, reassurance increases. Better colour rendering properties also improve visual performance and witness descriptions, particularly in low-contrast environments. ³ Studies further show that light sources with a high S/P ratio can create a greater perception of spatial brightness, which is closely associated with reassurance. ³⁷ Regarding CCT, research shows mixed results: warmer lighting tend to generate higher reassurance by creating inviting and comfortable spaces, while cooler LED lighting may evoke a sense of transience, discouraging lingering.^7,34 On the other hand, lighting features with blue-wave content and higher CCT can improve visibility and brightness, further contributing to reassurance, especially at lower intensity levels.^11,35,38

Furthermore, uniformity and spatial distribution of lighting play a significant role in supporting reassurance. Greater illuminance uniformity reduces dark spots where threats might be perceived to hide, thereby enhancing surveillance and reassurance. ³ Studies have shown that better uniformity allows for lower overall light levels while maintaining the same level of reassurance, particularly in parking lots and residential roads.^6,35,39 Illuminated walls, natural elements and visual boundaries, as well as lighting in people’s immediate surroundings, also positively influence reassurance.^11,12,17,36 Additionally, it was found that people feel less safe in lower lighting and higher entrapment settings, often considering these lighting conditions less acceptable for outdoor applications. ⁴ Although higher uniformity is traditionally linked to increased reassurance, its importance may vary depending on the context and the demographics involved. ³⁴

In conclusion, the relationship between lighting and reassurance is well-established and has been extensively explored across various studies. The presence of lighting is consistently associated with higher reassurance and specific factors such as illuminance, uniformity and spectral composition further shape this perception. While the connection between lighting and reassurance is stable, some mixed results highlight the need for continued exploration of lighting parameters, especially those related to the quality of lighting design. ³⁴ Confirming findings through diverse methodologies is essential to refining our understanding. The use of PPGIS offers a promising approach to investigate the relationship between outdoor lighting and reassurance, enabling the validation of established findings while potentially uncovering new insights. ⁴⁰

1.2 PPGIS as a tool for outdoor lighting research

The PPGIS methodology is a powerful tool for understanding location-specific human values, perceptions, behaviour and preferences, ⁴¹ incorporating insights from people’s everyday lives. Participatory mapping, including the PPGIS methodology, is grounded in the transactional model of person–environment relationships, which suggests that individuals actively engage with their environment, attributing meaning and value to specific places. ⁴¹ This approach is supported by theoretical frameworks such as Zube’s ⁴² transactional model of landscape perception and Gibson’s ⁴³ ecological perception framework. Participatory mapping relies on individuals’ ability to recall their transactional experiences within a place or landscape, reflecting the actualised affordances. ⁴¹

Participatory mapping is typically implemented through web-based mapping surveys, where participants are encouraged to share various urban experiences in a spatial format. The survey can include mapping questions (e.g. using points, lines and polygons) and traditional questionnaires. ⁹ Once collected, the geocoded ‘soft layer’ of experiential information can be correlated with specific aspects of the built environment and other geographical features with the help of spatial data analysis. By being simultaneously person- and place-focused, PPGIS holds excellent potential for accurately accounting for environmental aspects of places,^13,24 such as road lighting and people’s reassurance.

Participatory mapping approaches have been widely applied in environmental and urban research, for instance, in analysing relationships between pandemic outdoor recreation and green infrastructure, ⁴⁴ exploring adults’ leisure-time physical activities and the neighbourhood’s built environment ⁴⁵ and studying safety perceptions in university campuses. ²⁴ Nevertheless, the application of PPGIS in studying the impact of outdoor lighting on location-specific urban experiences remains relatively recent and somewhat limited practice.

In some PPGIS studies, outdoor lighting has been considered merely one of several attributes of the built environment, often discussed primarily in terms of safety.^{13,24,46–48} While participants in these studies did pay significant attention to outdoor lighting, it has predominantly been treated there as a single entity, for example, as either inadequate or adequate lighting. ²⁴ An in-depth exploration of specific lighting perceptions after dark was usually beyond the scope of these research efforts. Notably, to the best of the authors’ knowledge, no PPGIS studies to date have focused specifically on the after-dark environment or integrated GIS lighting layers into their analysis. This reveals a gap in current research and underscores the need to further explore the PPGIS methodology for outdoor lighting studies with broader applicability in the lighting research field.

An exception to this trend was a recent thesis, ⁴⁹ which employed PPGIS to examine lighting quality in urban planning. Participants mapped problematic locations, revealing issues related to insufficient light intensity and a lack of lighting infrastructure. However, since this thesis is not available in English, it highlights the need for further exploration of the use of PPGIS in outdoor lighting studies.

PPGIS as many research methodologies presents both strengths and limitations. One of its key advantages is the ability to link people’s place-based experiences and perceived knowledge with specific locations and spatial data, making it highly applicable across various research fields.^9,50 Unlike more controlled settings, PPGIS gathers data in real-world environments, capturing participants’ experiences as they naturally occur. For example, perceptions of after-dark environments can be directly tied to particular outdoor areas, enabling more detailed studies of human–environment relationships in natural settings.

Furthermore, PPGIS offers a cost-effective method for collecting individual-based data, ⁹ allowing for the inclusion of diverse voices, including those typically excluded due to time constraints, distance or physical disabilities. ⁵¹ By enabling anonymous participation, PPGIS mitigates barriers to engagement and encourages broader community involvement. ⁵¹ However, the inclusiveness and quality of PPGIS data are directly linked with sampling design, recruitment methods and participation rates.^41,52 Recruitment methods for online PPGIS surveys include random sampling from national/household registers, ⁵³ purposive sampling, ⁵⁴ internet survey panels ⁵⁵ and volunteer or crowdsourced participation via social and traditional media. ⁵⁶ To further enhance data quality, approaches such as school-based recruitment for younger participants ⁵⁷ or providing mapping assistance to respondents ⁵⁸ can also be used. ⁵² This is especially relevant when targeting older adults, who may require additional support or adjustments in survey design. ⁵⁹

The quality of PPGIS data also depends on several other factors, including accuracy and precision, which are recognised concerns. ⁵² Research has shown that the general public can achieve reasonable spatial accuracy, even when mapping physical landscape features, ⁴¹ and PPGIS data are often ‘good enough’ for analytical purposes.^60,61 Additionally, there has been evidence of spatial correspondence between locations identified through online PPGIS surveys and those recorded via Global Positioning System (GPS) tracking.^62,63 Overall, the accuracy of PPGIS data can depend on what is being mapped, how it is being mapped and who is doing the mapping. ⁹ In lighting studies, the environments mapped are typically broad areas or places rather than precise pinpoints, reducing potential inaccuracies in place selection. Previous research has shown that spatial accuracy and the quality of PPGIS data are related to participant familiarity with the study region, ⁶⁰ and digital mapping methods tend to offer greater precision than traditional paper maps. ⁴¹

Since PPGIS surveys are typically self-administered, ⁵² they often rely on participants’ ability to evaluate places based on memory rather than in-the-field observations. Research has suggested that mapping place-related activities and experiences is cognitively less challenging than mapping abstract concepts like values. ⁶⁴ Although the methodology is relatively new for lighting studies, prior explanatory analysis of PPGIS data has revealed significant differences between places with higher reported perceived overall quality of road lighting (POQ) and brightness, compared to those with lower POQ and brightness, supporting the use of PPGIS for capturing these particular lighting perceptions. ⁴⁰ Cognitive neuroscience further supports this, showing that specific brain regions, such as the parahippocampal cortex, retrosplenial cortex and hippocampus, enable humans to reliably recall familiar environments over the time.^65–68

Finally, a major limitation of PPGIS data, as highlighted by Hasanzadeh, ⁹ is the absence of a temporal component, such as the time associated with responses. While GPS data includes timestamps, PPGIS relies on self-reported information, which can be laborious and less reliable for analysis. ⁶⁹ This limitation makes it challenging to focus specifically on after-dark urban environments when analysing existing datasets. Therefore, when using PPGIS to study lighting conditions, it is crucial to incorporate specific questions about lighting and include time-of-day clarifications in the survey design.

1.3 Objective and scope

The primary objective of this study is to employ the PPGIS methodology to examine the relationship between reassurance and actual outdoor lighting conditions. Specifically, the study investigates how the presence of lighting and the required lighting conditions for various road lighting classes influence reassurance in urban environments. Additionally, it examines potential correlations between reassurance and perceived lighting parameters such as the overall quality of road lighting and brightness. By focusing on participants’ everyday experiences after dark, this study aims to demonstrate the applicability of PPGIS in urban lighting research and contribute to a deeper understanding of the impact of lighting on reassurance.

The scope of this research is centred on examining lighting parameters in relation to reassurance, without considering other socio-economic factors (e.g. education level) or socio-demographic factors (e.g. age, gender) or broader physical environmental aspects (e.g. layout, maintenance, greenery). Dataset was collected through volunteer recruiting of participants via traditional and social media channels. The study limits its analysis to the collected PPGIS dataset and integrates two GIS lighting layers: a detailed utility lighting map and road lighting classes, including unlit roads (URs). The study does not aim to provide general conclusions but rather to demonstrate the applicability of the PPGIS methodology, supporting its potential for further application and deeper exploration in urban lighting research.

2. Method

2.1 PPGIS survey design

A PPGIS survey was developed by using the Maptionnaire platform. The platform is designed for participatory mapping surveys, allowing participants to engage effectively without external assistance. To comply with ethical standards, the survey was designed in accordance with the General Data Protection Regulation (GDPR) legislation, avoiding the collection of personal sensitive data, and all data were stored securely. The web-based questionnaire consisted of traditional and mapping components (Figure 1). Participants provided basic personal information such as age and gender before being asked to map outdoor place(s) in the study area where they felt safe and unsafe after dark. They had to map at least one safe and one unsafe place without any maximum limit for the number of mapped places. These mapping tasks were deliberately focused on after-dark experiences, encouraging participants to reflect on their reassurance in different locations, without initially emphasising outdoor lighting as the primary research topic. After each mapping task, participants were asked to answer descriptive and clarifying questions through a pop-up questionnaire for each selected point. The pop-up questions were dedicated to the following two themes:

1. Reassurance and related reasons: These included the aggregated five-point Likert scale question on reassurance (‘How safe do you feel when you are visiting this place: very unsafe (1), somewhat unsafe (2), neutral (3), somewhat safe (4), very safe (5)’) and clarifying open-ended questions (‘Please type here if you have any other reasons’).

2. Perceived lighting conditions: These included the five-point Likert scale questions on POQ (‘How would you rate the overall quality of road lighting of the place: very poor (1), poor (2), adequate (3), good (4), very good (5)’) and brightness (‘How bright was the space: very dark (1), dark (2), neutral (3), bright (4), very bright (5)’) in the mapped places, as well as additional open-ended questions (‘Please type here any additional comments you have about the lighting of the place’). An option to answer ‘I do not remember’ was available for the brightness question. Additionally, a lighting visualisation accompanied this question to help participants better understand the concept of brightness (Figure 1(b)).

It is essential to clarify that participants were not required to make their evaluations on-site, which means that most observers may have relied on their memory when providing their assessments. To ensure familiarity with the area, the survey targeted residents of the neighbourhood or individuals who actively used the space, such as for work or study. Furthermore, given Finland’s northern latitude and the limited daylight hours during late November when the survey was conducted, it is reasonable to assume that participants were familiar with the area under after-dark conditions, as they would spend most of their time outdoors under artificial lighting.

Figure 1

Computer screenshots of the designed PPGIS survey: (a) a mapping task with the interactive map and (b) a lighting section of the following pop-up survey

2.2 Procedure

Participants used the Maptionnaire platform independently to share their urban experiences after dark, specifically identifying places within the campus area that made them feel safe or unsafe. The welcome page informed participants that their input would be anonymously utilised in research, and clear instructions were provided on how to map their selected locations (Figure 1(a)). Participants were able to zoom in and out to view different map scales, search for the address and show their current location while conducting the mapping. To support accurate point selection, a base-map included basic features of the physical environment such as campus landmarks, buildings and road networks.

The responses were collected from 24 November to 2 December 2022, at the Aalto University campus in Espoo, Finland. The area includes various types of buildings, facilities and green spaces and is predominantly used by Aalto University students and staff, as well as private and public companies’ staff. After dark, the area is predominantly lit by road lighting, and the use of architectural and commercial lighting is limited.

Participants were voluntarily recruited through social networks such as Telegram’s public chats related to the University and printed advertisements. The study included 111 participants (60% males, 37% females and 3% preferred not to say a gender). Participants’ ages ranged from 19 years to 47 years (M = 27, SD = 5). Nine participants chose not to report their age, and therefore, were not included in the age-related statistics. The participants shared their experiences about after-dark urban environment through the specially designed PPGIS survey, which was available in Finnish and English. The survey content in Finnish can be found in Appendix 1. A total of 282 places were reported, specifically 156 safe and 126 unsafe places. On average, participants mapped one safe location (M = 1.1, SD = 0.8, max = 4) and one unsafe location (M = 1.2, SD = 0.4, max = 3).

2.3 Road lighting layer digitalisation

Two approaches were applied to gather data about the actual lighting conditions in the studied area. Initially, the most recent lighting drawings and related materials were acquired from the City of Espoo’s representative and the Aalto University Campus & Real Estate company, responsible for managing and leasing Aalto University facilities. This data focused on functional lighting elements, specifically lighting poles. The resulting utility lighting map of road lighting features is illustrated in Figure 2(a).

Figure 2

Lighting map of the studied area (a) and road lighting classes in the studied area (b). The figure can be seen in colour in the electronic version of the paper

Subsequently, lighting road classes were established for existing roads according to the City of Espoo lighting guidelines and supporting materials. ⁷⁰ The map depicting these lighting classes and the corresponding lighting parameters are presented in Figure 2(b), Tables 1 and 2, respectively. The required parameters encompass design values for lighting levels (i.e. luminance and illuminance) and uniformity. All roads and areas were classified into five lighting classes: M3b + P3, M4 + P4 and M5 + P4, which denote scenarios where a pedestrian path is combined with a roadway, while P3 and P4 lighting classes were assigned to standalone pedestrian paths. All pathways and areas designed for pedestrians’ use, such as pedestrian and cyclist paths, roads, squares and park areas within the studied area, are recommended to be illuminated with lighting sources having a CCT of 3000 K and a CRI (R_a) of at least 80 with caution to avoid CCTs higher than 4300 K.

Table 1

The road lighting classes (M) used in the study along with their performance requirements, as outlined by the City of Espoo guidelines ⁷⁰

Lighting class	Dry and wet roadway luminance
	Dry			Wet
	L m (cd m−2, min)	U o (min)	U l (min)	U ow (min)
M2	1.50	0.40	0.60	0.15
M3b	1.00	0.40	0.60	0.15
M4	0.75	0.40	0.40	0.15
M5	0.50	0.35	0.40	0.15

The abbreviations used are as follows: L_m refers to average road surface luminance (of a carriageway of a road), U_o to uniformity, U_l to longitudinal uniformity, U_ow to uniformity for wet surfaces.

Table 2

The road lighting classes (P) used in the study along with their performance requirements, as outlined by the City of Espoo guidelines ⁷⁰

Lighting class	Horizontal illuminance
	E hm * (lx, min)	E hmin (lx, min)
P3	7.50	1.50
P4	5.00	1.00

The abbreviations used are as follows: E_hm refers to average illuminance (on a road area), E_hmin refers to minimum illuminance (on a road area).

*

To ensure sufficient uniformity, the E_hm of the object shall not exceed 1.5 × the required E_hm (e.g. 7.5 lx for lighting class P4).

To identify UR, the ‘lit’ feature in QGIS software was applied to filter the GIS layer of roads based on the presence or absence of artificial lighting. The accuracy of the GIS dataset was further verified through a field investigation, during which the study area was walked using a GPS-tracker mobile app.

For simplification of the analysis, road lighting classes were divided into three groups, namely GP3 (i.e. M3b + P3 and P3), GP4 (i.e. M4 + P4, M5 + P4, and P4) and UR.

All collected data were converted into the same format, geographically mapped and processed using the QGIS software. Overall, the study area encompasses illuminated zones with varying lighting needs and completely unlit pathways.

2.4 Data analysis

QGIS software is a free and open-source GIS software that allows users to visualise, analyse and interpret spatial data. Through it and specifically through the Quick OSM plugin, the contextual information about the area, such as roads (queries: highway=*), green zones (queries: leisure=park, nature_reserve, common, natural=wood, grassland, scrub, wetland, landuse=forest, allotments), buildings (queries: building=*), playgrounds (queries: leisure=playground) and water layers (queries: natural=bay, water), was obtained. Road data were downloaded from Digiroad, the Finnish national road database with the most crucial data on road attributes, such as functional and administrative categories.

The GIS analyses of the data, such as calculating the shortest distances between points, counting points in polygons and buffering were implemented by applying the same name vector analysis tools, which are integrated into the QGIS software version 3.22, QGIS Development Team.

The shortest distances between each safe and unsafe mapped place and the nearest lighting point were calculated to investigate the impact of the actual presence of lighting on reassurance. Additionally, the number of lighting features within a 30-m radius around each mapped location was counted. This 30-m buffer represents a reasonable distance within which pedestrians might assess their surroundings after dark, encompassing a 15-m range recommended for examining lighting’s effects on interpersonal judgements in natural nighttime settings ⁷¹ and extending to cover the action space zone (2 m to 30 m) proposed by Cutting and Vishton. ⁷² This buffer zone may allow for a sufficient area around each safe and unsafe place to capture relevant lighting conditions and their impact on reassurance.

To study the influence of lighting conditions specified by the existing lighting classes (see Figure 2(b); Tables 1 and 2) on reassurance, each defined lighting class (i.e. M3b + P3, P3, M4 + P4, M5 + P4, P4 and UR) was assigned to GIS geometrical features such as lines (for roads) and polygons (for squares and playgrounds). A 15-m buffer zone was built around each feature to approximate the immediate area influenced by road lighting. This approach enabled the spatial classification of mapped safe and unsafe places based on their proximity to lighting classes, from the highest M3b + P3 to UR. Safe and unsafe places were first assigned to one of the six lighting classes, which were later divided into three broader groups: GP3, GP4 and UR.

The collected PPGIS data were cleared and filtered so that only completely submitted responses (i.e. at least two mapped and described places) and responses from participants who mapped and described at least one unique place were used for further analysis. All 282 mapped places were analysed, when investigating the relationships between PPGIS survey variables, namely reassurance, POQ and brightness ratings. In GIS analysis, the number of mapped places decreased to 266 places, including 151 safe and 115 unsafe because some of the participants selected places out of the studied area. The ordinal variables for reassurance, POQ and brightness within the collected PPGIS data were converted into a numerical format by assigning values on a scale of 1 to 5, as described above (i.e. Section 2.1).

The statistical analyses of the results were conducted with the help of the IBM-SPSS Statistics software package version 29, IBM (International Business Machines Corporations). The selection of statistical tests was guided by the results of the Shapiro–Wilk normality test and Levene’s test for homogeneity of variances. For ordinal data with more than two groups, Spearman correlation and the Kruskal–Wallis H-test were applied. For comparisons between two groups, the Mann–Whitney U-test was used for both ordinal and continuous data types. The results are reported as median (Mdn) and interquartile range (IQR).

3. Results

All the places within the research area that were mapped as safe or unsafe are presented in Figure 3. Each point on the map contains the background information (i.e. age and gender) of the respondent and responses from an observer on questions related to reassurance and the perceived lighting environment in that place.

Figure 3

Collected PPGIS data: perceptions of safe (a), unsafe (b) and both safe (green points) and unsafe (red points) (c) places mapped within the research area. The figure can be seen in colour in the electronic version of the paper

3.1 Reassurance and actual lighting conditions

The shortest distance from each mapped safe and unsafe place to the nearest lighting point was calculated to assess how the actual presence of lighting influences reassurance. The median distance for unsafe places (N = 115, Mdn = 16.76 m, IQR = 19.62 m) was longer than for safe places (N = 151, Mdn = 11.22 m, IQR = 9.52 m). A Mann–Whitney U-test revealed that the difference between these distances was highly significant (U = 6039, p < 0.001, two-tailed, r = 0.28).

Similarly, the number of lighting features was counted within a 30-m radius circular buffer zone around each safe and unsafe mapped place, as shown in Figure 4. The median number of lighting features within the buffers of safe places (N = 151, Mdn = 4.00, IQR = 4.00) was higher than those within the buffers of unsafe places (N = 115, Mdn = 3.00, IQR = 3.00). A Mann–Whitney U-test showed that the difference between the numbers of lighting features within the buffers of safe and unsafe places was highly significant (U = 5863, p < 0.001, two-tailed, r = 0.29).

Figure 4

Thirty-meter radius buffer zones around each safe and unsafe mapped place. The red and green dots on the area map show unsafe and safe places with their buffers, respectively; yellow dots depict road lighting features. The figure can be seen in colour in the electronic version of the paper

To analyse the relationship between lighting conditions and reassurance, each lighting class was represented by GIS features and extended into 15-m buffer zones (Figure 5). Mapped safe and unsafe places were divided into three groups, namely GP3, GP4 and UR, as described above (i.e. Section 2.4). Descriptive statistics, including distribution of mapped safe and unsafe places across the groups, as well as calculated Mdn and IQR values for reassurance, POQ and brightness, are presented in Table 3.

Figure 5

Fifteen-meter route buffer zones built around each UR (blue colour). The red dots show unsafe places. The figure can be seen in colour in the electronic version of the paper

Table 3

Distribution of mapped safe and unsafe places across different lighting conditions, specifically GP3, GP4 and UR

Groups	Lighting classes	N (%) of safe places	N (%) of unsafe places	Total N of places	Reassurance		POQ		Brightness
					Mdn	IQR	Mdn	IQR	Mdn	IQR
GP3	M3b + P3	21 (13.91%)	7 (6.09%)	99	5.00	3.00	4.00	1.00	4.00	2.00
	P3	45 (29.80%)	26 (22.61%)
GP4	M4 + P4	20 (13.25%)	14 (12.17%)	93	4.00	3.00	3.00	2.00	2.50	2.00
	M5 + P4	9 (5.96%)	7 (6.09%)
	P4	23 (15.23%)	20 (17.39%)
UR	–	7 (4.64%)	25 (21.74%)	32	3.00	1.00	1.00	1.00	1.00	1.00
Unclassified	–	26 (17.22%)	16 (13.91%)	42	–	–	–	–	–	–

The table also presents Mdn and IQR values calculated for reassurance, POQ and brightness across the groups

Our results showed that areas situated within buffers of roads with any lighting (i.e. attributed to GP3 and GP4 groups) demonstrated higher reassurance ratings compared to UR (Figure 6). GP3 exhibited the highest proportion of mapped safe places of around 44% and the highest reassurance ratings (Mdn = 5.00, IQR = 3.00). GP4 showed comparable proportions of safe (~34%) and unsafe (~35%) places, with moderate reassurance ratings (Mdn = 4.00, IQR = 3.00). UR had the lowest percentage of safe places of around 5% with the lowest reassurance ratings (Mdn = 3.00, IQR = 1.00). However, the distribution of unsafe places did not exhibit a clear trend across the groups.

Figure 6

Median values of reassurance (blue squares), perceived overall quality of road lighting (POQ, orange diamonds) and brightness (grey triangles) across three groups GP3, GP4 and UR. Error bars represent IQRs. Ratings are based on a five-point Likert scale: reassurance – very unsafe (1) to very safe (5); POQ – very poor (1) to very good (5); and brightness – very dark (1) to very bright (5). The figure can be seen in colour in the electronic version of the paper

To further investigate the relationship between lighting conditions and reassurance, Kruskal–Wallis tests were conducted. The results indicated significant differences in the mean ranks of reassurance across the groups (H(2, n = 223) = 15.03, p < 0.001). Subsequently, Dunn’s pairwise tests were performed for three pairs, using the Bonferroni correction for multiple comparisons and a significance level of 0.05. The results revealed significant differences in reassurance ratings between GP3 and UR (p < 0.001) and GP4 and UR (p = 0.006). However, no significant difference in reassurance ratings was observed between GP3 and GP4.

Additionally, the same statistical tests were applied to examine whether reported perceived lighting parameters, specifically POQ and brightness, varied across the groups. The Kruskal–Wallis tests indicated significant differences in both POQ (H(2, n = 223) = 45.91, p < 0.001) and brightness (H(2, n = 217) = 49.63, p < 0.001). The results of Dunn’s pairwise tests revealed significant differences in POQ and brightness between all pairs, specifically GP3 and GP4 (POQ: p = 0.002; brightness: p = 0.015), GP3 and UR (POQ: p < 0.001; brightness: p < 0.001) and GP4 and UR (POQ: p < 0.001; brightness: p < 0.001).

3.2 Reassurance and perceived lighting parameters

The Spearman correlation test was performed to examine the relationship between reassurance and perceived lighting parameters, namely POQ and brightness, across safe and unsafe reported places. The Spearman rho showed a significantly strong correlation between reassurance and POQ, R(282) = 0.684, (p < 0.001, two-tailed), as well as between reassurance and brightness, R(272) = 0.645, (p < 0.001, two-tailed). Mann–Whitney U-tests were performed to investigate differences in perceived lighting parameters between places reported as safe and unsafe. The results indicated significant differences between groups of safe (POQ: Mdn = 4.00, IQR = 1.00; brightness: Mdn = 4.00, IQR = 1.00) and unsafe places (POQ: Mdn = 2.00, IQR = 2.00; brightness: Mdn = 2.00, IQR = 1.00) for the studied parameters, specifically for POQ (U = 2095, p < 0.001, two-tailed, r = 0.68) and brightness (U = 2255, p < 0.001, two-tailed, r = 0.66).

When we asked our participants to describe the reasons that make the place safe and unsafe, we also found that many responses were related to lighting conditions and visibility for both safe and unsafe places (Appendix 2, Tables A1 and A2, respectively). Furthermore, additional comments about the lighting of mapped safe and unsafe places are presented in Appendices (Appendix 2, Tables A3 and A4, respectively). All these comments can be divided into those describing the presence or absence of lighting and brightness, and those concerning lighting quality (i.e. colour, aesthetics and design). It is interesting to note that comments which belong to safe places were predominantly on lighting quality theme (e.g. ‘The lighting overall is not only good but also aesthetically pleasing in general’, ‘I am a foreigner, so I am still getting used to the very warm/reddish lightning in Finland. I rather prefer white lighting (6000K)’). In contrast, the responses to the same question but concerning unsafe places were mainly dedicated to the presence of lighting and brightness perceptions (e.g. ‘No lighting in the forest’, ‘I do not feel particularly unsafe here, but the old red Alvar Aalto lamps are just not enough’).

4. Discussion

4.1 Validation and complementation

As previous studies have shown,^12,23 the presence of lighting can positively contribute to people’s reassurance. Through the analysis of the gathered PPGIS dataset, we discovered that the presence of lighting plays a significant role in determining whether a person perceives a place as safe or unsafe, aligning with earlier findings on the strong association between lighting and reassurance (e.g. Fotios et al., ¹² Haans and de Kort, ¹⁷ Painter ¹⁸ ). Additionally, the spatial analysis provided complementary insights, showing that unsafe places were, on average, farther from the nearest lighting feature compared to safe places. Moreover, the number of lighting features within the 30-m buffers of safe places was significantly higher than within those of unsafe places. To the best of the authors’ knowledge, these findings provide added value to the literature on urban lighting by highlighting the importance of lighting infrastructure and its spatial distribution in shaping reassurance.

Our results further showed that places with any lighting demonstrated higher reassurance ratings than UR, with significant differences observed between groups GP3 (i.e. M3b + P3 and P3 lighting classes), GP4 (i.e. M4 + P4, M5 + P4 and P4 lighting classes) and UR. However, despite the highest reassurance ratings being attributed to areas expected to have better lighting (i.e. GP3), no significant differences in reassurance ratings were found when comparing places within GP3 and GP4. This may suggest that the difference in lighting quantity between P3 and P4 lighting classes does not lead to perceptible differences in reassurance, a finding consistent with previous research indicating that increases in illuminance beyond certain levels yield diminishing returns in reassurance.^6,12,34 On the other hand, since lighting classes specify required values rather than fully capturing actual lighting conditions, the distinctions in lighting between neighbouring classes, such as P3 and P4, may be minimal. Additionally, the lighting requirements for these classes are below the optimal values recommended for supporting reassurance in previous studies,^6,12,33 suggesting that higher reassurance could potentially be achieved with improved lighting. Future studies should incorporate objective lighting measurements in spatial analysis, including metrics related to lighting distribution and spectral composition, as they have been previously linked to reassurance.^6,7,34,35

Furthermore, our study examined the perceived lighting parameters, specifically POQ and brightness, and their relationship with reassurance. The results showed a strong positive correlation between reassurance and these perceived lighting parameters. Notably, participants attributed higher POQ and brightness ratings to safe places, indicating that these elements are closely associated with positive safety experiences. This aligns with previous research, which has demonstrated a correlation between reassurance and scene brightness perceptions.^35,73 Additionally, a prior analysis of this dataset revealed a significantly strong positive correlation between POQ and brightness, R(272) = 0.848, (p < 0.001), ⁴⁰ showing that the participants consistently linked the quality of road lighting with brightness perceptions.

In addition, open-ended responses added valuable insights into how participants distinguished between safe and unsafe places. Comments about safe areas often focused on lighting quality, while those describing unsafe places emphasised the presence or absence of lighting and brightness levels. This distinction suggests that while lighting quality considerations plays a significant role in reassurance perceptions for lit areas, the mere presence of lighting is crucial in areas perceived as unsafe. The tendency for participants to prioritise lighting quality in safe places underscores its importance in increasing reassurance and should be considered in future research exploring urban lighting and safety.

4.2 Limitations and future development of the PPGIS methodology

As discussed above, the application of PPGIS in this study yielded findings that align with the existing body of knowledge while also offering added value. It suggests that PPGIS is an applicable method for studying the relationship between reassurance and lighting. Nevertheless, the designed PPGIS approach applied in this study has several limitations and areas for improvement that should be considered when interpreting the findings or planning future research in urban lighting.

Firstly, the methodology needs to be tested in more diverse and distinctly different lighting environments, including a broader range of real-world lighting situations. From a GIS perspective, we limited our lighting datasets to a utility lighting map (i.e. indicating the presence of road lighting poles) and the classification of roads according to existing lighting standards. However, real lighting environments are more complex, often influenced by additional elements like commercial and architectural lighting. To better capture these complexities, future research should expand datasets to include more comprehensive measurements, mapping and categorisation techniques. For example, Rabaza et al. ⁷⁴ suggested an innovative method for measuring lighting levels and other lighting parameters through aerial imagery of towns and cities, which can undoubtedly complement the digitalisation of lighting layers. Furthermore, GIS datasets can attribute vector data, such as lighting points, with specific characteristics (e.g. types of lighting sources, CCTs, optics, pole heights and design), enabling a more detailed analysis of lighting environments.

Secondly, validating PPGIS-based findings through complementary research methods and field experiments, where objective and subjective data are collected from participants at specific urban sites, would strengthen their reliability. For instance, comparing recollection-based evaluations with on-site assessments may provide further evidence in favour of the applicability of the PPGIS methodology. Moreover, incorporating tools such as after-dark images, virtual tours or follow-up surveys into the PPGIS survey can help to improve spatial accuracy and supplement participants’ memories.

In this study, participants were required to be familiar with the area, but they were not necessarily present at the mapped locations when responding. The results of our study revealed significant differences in reported perceived lighting parameters, specifically POQ and brightness, across the groups GP3, GP4 and UR. These results suggest that differences in lighting parameters are perceptible to participants and can be shared through PPGIS methodology. Future research should further explore how well non-professionals can assess technical aspects of lighting, such as distribution and spectral composition, using PPGIS.

The lack of a temporal component in current PPGIS practices is another limitation. Future studies should explore the application of geographically explicit ecological momentary assessment (GEMA) methodologies,^75,76 which capture where and when participants provide their assessments, adding valuable time-based data to the analysis.

The study also had a relatively small sample size, with underrepresentation of certain age groups, such as the elderly and children. Volunteer recruitment via traditional and social media channels may have contributed to an unbalanced respondent profile. Future studies should consider random sampling strategies to improve demographic balance. Research has shown that personal invitations based on random sampling promote better representativeness compared to open calls for volunteers, which tend to skew respondent profiles.^64,77

Finally, one of the observations of this study is that locations within the same lighting conditions were sometimes perceived as both safe and unsafe by different participants. This finding suggests that lighting alone may not fully explain reassurance after dark, and other social and environmental factors, such as individual experiences, cultural background and environmental characteristics, could play a role. For instance, some respondents in our study noted that Finland’s outdoor environment is generally perceived as safe, which may have influenced their perceptions. Recent research also has shown that environmental, cultural and climatic characteristics may influence reassurance.^7,11 Testing the methodology across diverse cultural and environmental contexts can enhance its broader applicability and provide deeper insights into the factors influencing reassurance.

Although personal factors such as age and gender were not consistently linked to reassurance in recent reviews, ¹¹ these demographic factors remain important areas for future research.^7,34,78 Future studies should aim to collect more detailed background information, such as education and other population-level variables, to better understand the role of individual characteristics and user demographics in shaping reassurance.

Further research can also benefit from incorporating related geospatial data, such as the spatial distribution of incidents,^79–81 to explore reassurance more comprehensively. Supplementing this with environmental data, such as maintenance records or the physical layout of spaces, may also help to explain why locations within the same lighting class are perceived as both safe and unsafe. Multivariate analyses should be applied to account for these social and environmental factors, offering a more nuanced understanding of how these variables interact with lighting to influence reassurance. Alternatively, the method used in this study can be expanded to adopt a day-dark approach, capturing reassurance ratings under both daylight and after-dark conditions for the same study area, as previously applied in lighting research. ⁶

5. Conclusions

The primary objective of this study was to employ the PPGIS to explore the relationship between reassurance and actual outdoor lighting conditions, as well as to examine potential correlations between reassurance and perceived lighting parameters such as POQ and brightness, by focusing on participants’ everyday experiences after dark. The analysis revealed that the presence of lighting itself can positively contribute to reassurance and unlit areas are perceived as less safe in comparison with areas with any lighting, specifically P3 and P4 lighting classes. Furthermore, the study established a strong positive correlation between reassurance and perceived lighting parameters, such as POQ and brightness. When comparing safe and unsafe areas, distinct differences emerged in how these lighting parameters were perceived, with higher ratings consistently linked to areas perceived as safe.

These findings are consistent with the current body of knowledge, suggesting that PPGIS is applicable to the study of reassurance and lighting relationships. By using a participatory mapping approach, we were able to gather meaningful place-based insights and demonstrate the potential of PPGIS as a tool for urban lighting research. This approach offers several key benefits, including the ability to investigate lighting impacts based on participants’ real-world experiences, actively engage citizens in the research process and develop experiential data layers for lighting maps.

The practical implications of these findings are notable for urban lighting design and planning. By identifying specific areas where people feel safe or unsafe after dark, targeted lighting interventions can be prioritised to enhance reassurance. Moreover, the participatory nature of PPGIS-based web surveys fosters a sense of civic engagement in urban planning, adding valuable insights from the public to the research process.

By adopting and refining PPGIS for future studies, we can strengthen the connection between lighting research and practical urban design applications. This will support evidence-based approaches to enhancing safety and improving urban lighting environments.