In the definition phase, regions within the study area were evaluated based on data derived from analysis maps and transformed into heat maps representing lighting demand intensity. A structured scoring system was developed to rank areas according to relative lighting demand.
3.3.2. Scoring
The Lighting Demand Index (LDI) is a composite indicator representing the relative need for lighting interventions across urban streets, derived from the integration of multiple spatial analysis maps.
In this study, the evaluation criteria were defined as six spatial analysis maps (Map1–Map6), each representing a thematic layer produced during the analysis phase. These maps were treated as independent decision layers within a multi-criteria decision-making framework.
To quantify lighting needs for the selected streets and avenues, a scoring methodology was applied based on the results obtained from the spatial analysis maps. Each street segment was evaluated in terms of lighting demand and subsequently represented through color-coded heat maps, enabling a visual interpretation of spatial variations in lighting priority (
Figure 9).
Each street and avenue was assessed across the six thematic maps, which reflect key spatial and cultural parameters relevant to heritage-sensitive urban lighting, including historical and conservation value, urban imageability, functional hierarchy, and built environment characteristics. Rather than relying on user surveys, the evaluation process was grounded in spatial data interpretation, expert judgment, and on-site observations to ensure methodological consistency.
To ensure methodological transparency, the Analytic Hierarchy Process (AHP) was employed as a validation tool to examine whether a hierarchical dominance existed among the six spatial criteria. Pairwise comparisons were conducted using the Saaty fundamental scale. The Consistency Ratio (CR) remained below the acceptable threshold (CR < 0.10), confirming the reliability of expert judgments.
The resulting weight vector converged toward a uniform distribution (w
i = 0.167), indicating comparable contribution of each criterion. Therefore, equal weights were adopted as an AHP-validated outcome rather than a predefined assumption. Based on this validated weighting structure, the Lighting Demand Index (LDI) for each street segment was calculated as follows:
where
represents the AHP-derived weight of each spatial criterion and
denotes the corresponding map score. Given the AHP-validated equal-weight distribution, the LDI corresponds to the cumulative contribution of Map1–Map6 scores for each street segment.
The resulting individual heat maps for each thematic criterion are presented in
Table 4. The following scoring criteria were used to create the heat maps:
Zoning and Conservation Priority Heat Map (Map1*): Areas with high historical value, such as the Tepebağ Mound and surrounding urban conservation zones, were identified as priority zones due to their high heritage value and limited nighttime integration.
Registered Buildings and Parcels Heat Map (Map2*): Regions surrounding officially registered buildings were scored higher. A hierarchical scoring system was used to differentiate between registered, unregistered, and incompatible structures.
Lynch Analysis Heat Map (Map3*): Emphasis was placed on urban nodes and landmarks—such as the Tepebağ Mound and intersections of main arteries—which are critical for tourism and orientation, and thus require enhanced visibility.
Street and Road Classification Heat Map (Map4*): Roads were prioritized based on their traffic intensity and functional importance:
Primary roads (first-degree, high-traffic): highest priority
Secondary connectors: moderate priority
Tertiary roads and cul-de-sacs: lower priority
Functional Use Heat Map (Map5*): Areas with commercial and touristic functions were scored highest due to higher user intensity. Religious, educational, governmental, and lodging facilities were rated next. Residential zones were given lower scores to preserve privacy and reduce excessive lighting.
Building Density and Height Heat Map (Map6*): Zones with taller and denser building configurations—typically located along the boundary streets—were considered to require higher levels of lighting than sparsely built interior areas.
For each street segment, weighted scores derived from the six spatial analysis maps were aggregated to calculate an AHP-based Lighting Demand Index (LDI). The resulting values ranged from 12 to 23 and were categorized into four lighting demand levels to support decision-making:
Low Demand (12–14): Areas with sufficient lighting or low heritage and functional priority, requiring minimal intervention.
Moderate Demand (15–17): Streets with moderate deficiencies or contextual importance, suitable for selective improvements.
High Demand (18–20): Routes with pronounced spatial or cultural significance where lighting upgrades would substantially enhance visibility and identity.
Very High Demand (21–23): Strategically critical zones requiring immediate and intensive lighting interventions due to their role in cultural representation, tourism, and safety.
The resulting lighting demand distributions are visualized in
Figure 10, while the integrated prioritization outcome is presented in the final composite map (
Figure 11).
This AHP-validated, map-driven scoring framework provides a transparent, reproducible, and methodologically robust basis for prioritizing lighting interventions in historic urban environments (
Table 5).
Since the entire study area possesses historic urban environment characteristics, all spatial and physical environmental parameters were evaluated with equal significance. The equal weighting was not predetermined but emerged from consistent pairwise comparisons (CR < 0.10), reflecting the homogeneous conservation value and morphological integrity of the area.
By organizing the urban network according to standardized lighting classes, a structured basis was established for the development of typology-specific lighting proposals in the subsequent phase. The summarized Lighting Demand Index (LDI) results for neighborhood streets and major avenues are presented in
Table 6 and
Table 7, respectively.
The complete LDI dataset, including all evaluated street segments and major avenues, is provided in
Appendix B (
Table A2 and
Table A3).
The results indicate that major avenues exhibit consistently very high lighting demand due to their traffic intensity and strategic role within the urban structure. In contrast, neighborhood streets demonstrate more differentiated demand levels, reflecting variations in conservation value, functional use, and spatial morphology.
3.3.3. Classification
The streets and avenues identified for lighting design within the study area were classified according to international lighting standards. Specifically, two major road lighting categories were utilized:
CE Class (Conflict Areas): Includes zones such as commercial streets, intersections, and mixed-use pedestrian–vehicular routes, where lighting must accommodate complex traffic and human activity patterns.
S Class (Subsidiary Roads): Covers access roads, residential streets, pedestrian paths, and cycleways where lighting is primarily intended for pedestrian safety and comfort.
This classification was made in alignment with the SLL Code for Lighting (2022) and the TS EN 13201-2 (2016) standard. Streets and avenues within the study area were categorized according to their usage characteristics and lighting performance requirements, as shown in
Figure 12.
Typology Development for Lighting Design
At this stage, lighting typologies were developed for the areas within Adana’s historic city center that were planned for urban lighting design, based on their specific lighting requirements. Utilizing both the analysis maps and the heat maps illustrating lighting demand intensity, seven different area typologies—labeled A, B, C, D, E, F, and G—were defined for the streets, avenues, and spaces within the study area. These typologies, representing varying levels of illuminance needs, are described as follows (
Table 8).
Type A: Represents avenues with the highest level of lighting demand. These roads carry heavy vehicular traffic, and pedestrian and vehicular circulation occur on separate surfaces.
Type B: Also represents areas with the highest lighting demand, but lighting design is developed only for pedestrian walkways, as vehicular lighting already exists. The surfaces for vehicles and pedestrians are separate.
Type C: Refers to streets with high lighting demand. These are typically streets that connect to the boundary avenues of the study area. Pedestrian and vehicular circulation occur on the same surface.
Type D: Includes streets with a moderate level of lighting demand. Pedestrian and vehicular use is shared on a single surface.
Type E: Refers to streets with a low level of lighting demand, where pedestrian and vehicular circulation is also on the same surface. These are the areas with the least lighting need.
Type F: Designates dead-end spaces within blocks, where pedestrian and vehicular surfaces are shared.
Type G: Refers to special areas where pedestrian and vehicular movement occurs on the same surface, and which require customized lighting solutions.
Table 9 and
Table 10 present a summarized version of the typology assignments for neighborhood streets and major avenues within the study area, based on their corresponding lighting classes and calculated demand levels.