planX — Yerlesim Plani Arac Seti

Theoretical Background

Parcel subdivision is the foundational operation in settlement morphology, translating development islands (imar adası) into individual plots (parsel) that form the basic operational unit of planning control. The algorithm implements a medial-axis subdivision strategy: each island polygon is divided by a centreline oriented along the long axis (the h-line), with perpendicular division lines placed at regular intervals corresponding to the target parcel width. This approach generalises the classical strip-parcelling method documented by Conzen (1960) in his study of Alnwick's burgage plots, extended with parametric controls for organic variation.

The algorithm addresses several well-known challenges in automatic parcel generation: (1) irregular island shapes are pre-processed through oriented minimum bounding box (OBB) normalisation; (2) single-row detection prevents over-subdivision of narrow islands where the short edge is less than 1.8 times the target width; (3) residual sliver polygons are merged through a three-pass spatial adjacency optimisation; (4) optional width variation, fishbone offset, and row-width asymmetry introduce controlled organic irregularity that avoids the mechanical appearance of purely geometric subdivision (Marshall 2009).

Mathematical Formulation

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References

1. Conzen, M.R.G. (1960). Alnwick, Northumberland: A Study in Town-Plan Analysis. Institute of British Geographers, Publication No. 27. London: George Philip. DOI: 10.2307/621094
2. Marshall, S. (2009). Cities, Design and Evolution. Routledge. DOI: 10.4324/9780203886427
3. Kropf, K. (2017). The Handbook of Urban Morphology. Wiley. DOI: 10.1002/9781118747711
4. Parish, Y.I.H. & Müller, P. (2001). Procedural Modeling of Cities. Proceedings of SIGGRAPH 2001, pp. 301–308. DOI: 10.1145/383259.383292
5. Moudon, A.V. (1997). Urban morphology as an emerging interdisciplinary field. Urban Morphology, 1(1), 3–10. DOI: 10.51347/jum.v1i1.4047
6. Oliveira, V. (2016). Urban Morphology: An Introduction to the Study of the Physical Form of Cities. Springer. DOI: 10.1007/978-3-319-32083-0
7. Vanegas, C.A., Aliaga, D.G., Benes, B. & Waddell, P. (2009). Visualization of Simulated Urban Spaces. Computers & Graphics, 33(5), 609–617. DOI: 10.1016/j.cag.2009.06.005

Theoretical Background

Facade detection is a critical intermediary step that establishes the spatial relationship between each parcel edge and the movement network. In planning regulation, the classification of a parcel edge as front (facing a public road), side (lateral boundary), or back (rear boundary) determines the applicable setback distances and thus directly constrains building footprint geometry. The algorithm operationalises principles from space syntax theory (Hillier & Hanson 1984), where the configurational relationship between private parcels and public movement channels is the primary determinant of spatial integration.

The distance-based classification uses an adaptive threshold: when no explicit threshold is provided, it is calculated as 60% of the parcel's minimum bounding rectangle dimension—a heuristic derived from the empirical observation that parcels with standard depth-to-width ratios (typically 1:1.5 to 1:3 in Turkish residential zones) have front edges within approximately half to two-thirds of their depth from the road. Corner parcels are identified when two or more edges fall within the threshold distance, triggering the special setback rules mandated by Article 18 of the Turkish Planned Areas Zoning Regulation, where corner parcels have zero side yards and two rear yards instead of the standard one-side-two configuration.

Mathematical Formulation

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References

1. Hillier, B. & Hanson, J. (1984). The Social Logic of Space. Cambridge University Press. DOI: 10.1017/CBO9780511597237
2. Hillier, B. (1996). Space is the Machine: A Configurational Theory of Architecture. Cambridge University Press.
3. Batty, M. (2013). The New Science of Cities. MIT Press. DOI: 10.7551/mitpress/9399.001.0001
4. Peponis, J., Bafna, S. & Zhang, Z. (2008). The connectivity of streets: reach and directional distance. Environment and Planning B, 35(5), 881–901. DOI: 10.1068/b33088
5. Turner, A. (2007). From Axial to Road-Centre Lines. Environment and Planning B, 34(3), 539–555. DOI: 10.1068/b32067
6. Porta, S., Crucitti, P. & Latora, V. (2006). The network analysis of urban streets: a primal approach. Environment and Planning B, 33(5), 705–725. DOI: 10.1068/b32045
7. Sevtsuk, A. & Mekonnen, M. (2012). Urban network analysis. Revue Internationale de Géomatique, 22(2), 287–305.

Theoretical Background

Floor Area Ratio (FAR)—Emsal or KAKS in Turkish planning terminology—is the primary bulk control in zoning ordinances worldwide. Unlike TAKS (Building Coverage Ratio), which controls only the ground-plane footprint, FAR regulates total built volume by relating total floor area to site area. This algorithm solves the perimeter block typology, characteristic of European urbanism from the 19th-century Gründerzeit blocks to contemporary courtyard housing (Lehnerer 2009).

The algorithm decomposes the FAR constraint into a TAKS target using the building height: $TAKS = FAR / n_{floors}$, where $n_{floors}$ is derived from the maximum height $H_{max}$ divided by estimated floor-to-floor height (typically 3.1m for residential). Building masses are then distributed along the buildable perimeter (the island polygon offset inward by the fixed setback distance), segmented into individual building blocks separated by twice the side-yard distance, and filled with procedural architectural forms (L, U, T, I, courtyard, etc.) that respect a minimum structural depth of 9m. This represents a constraint-based generative design approach (Beirao et al. 2012) where legal and structural constraints drive formal outcomes.

Mathematical Formulation

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References

1. Barnett, J. (1982). An Introduction to Urban Design. Harper & Row.
2. Lehnerer, A. (2009). Grand Urban Rules. 010 Publishers. DOI: 10.5949/mitpress/9780262514781.001.0001
3. Beirão, J.N., Duarte, J.P. & Stouffs, R. (2012). Structuring a Generative Model for Urban Design. Proceedings of eCAADe 2012, pp. 323–332.
4. Schumacher, P. (2009). Parametricism: A New Global Style for Architecture and Urban Design. Architectural Design, 79(4), 14–23. DOI: 10.1002/ad.912
5. Müller, P., Wonka, P., Haegler, S., Ulmer, A. & Van Gool, L. (2006). Procedural Modeling of Buildings. ACM Transactions on Graphics, 25(3), 614–623. DOI: 10.1145/1141911.1141931
6. Batty, M. & Longley, P. (1994). Fractal Cities: A Geometry of Form and Function. Academic Press.
7. Duarte, J.P. (2005). A discursive grammar for customizing mass housing. Automation in Construction, 14(2), 265–275. DOI: 10.1016/j.autcon.2004.07.013

Theoretical Background

Detached order (Ayrık Nizam, abbreviated "A nizam" in Turkish planning codes) is the most common low-to-medium density residential building arrangement, characterised by free-standing buildings separated from all parcel boundaries by mandatory setbacks on all four sides. This typology dominates suburban morphologies globally and is the default zoning category for residential areas in most Turkish municipalities under the Planned Areas Zoning Regulation (Planli Alanlar Imar Yonetmeligi).

The algorithm implements facade-conscious setback, where each parcel edge's setback distance is determined by its facade classification from Step 2: front edges receive the front setback (typically largest, for streetscape definition and traffic safety), side edges receive the lateral setback (for fire separation and daylight access), and back edges receive the rear setback (for privacy and service access). The remaining buildable polygon is then scaled to satisfy the TAKS constraint through a geometry-scaling operation that preserves centroid and orientation. The theoretical basis lies in morphological constraint satisfaction (Kropf 2017), where legal norms (setbacks, coverage ratio) interact with site geometry (parcel shape, orientation, facade classification) to produce the permissible building envelope.

Mathematical Formulation

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Interpretation Guidance

References

1. Conzen, M.R.G. (1969). Alnwick, Northumberland: A Study in Town-Plan Analysis. 2nd ed. Institute of British Geologists.
2. Kropf, K. (2017). The Handbook of Urban Morphology. Wiley. DOI: 10.1002/9781118747711
3. Martin, L. & March, L. (1972). Urban Space and Structures. Cambridge University Press.
4. Levy, A. (1999). Urban morphology and the problem of the modern urban fabric. Urban Morphology, 3(2), 79–89. DOI: 10.51347/jum.v3i2.3880
5. Panerai, P., Castex, J., Depaule, J.C. & Samuels, I. (2004). Urban Forms: The Death and Life of the Urban Block. Architectural Press. DOI: 10.4324/9780080472454
6. Berghauser Pont, M. & Haupt, P. (2010). Spacematrix: Space, Density and Urban Form. NAi Publishers.

Theoretical Background

Block order (Blok Nizam) is a semi-detached arrangement where buildings on adjacent parcels share a common lateral wall, eliminating the side setback between paired parcels while maintaining setbacks on all other sides. This typology represents an intermediate density between detached and fully attached forms, commonly found in 20th-century garden-city and suburban planned developments across Europe and Turkey. The regulatory logic is: internal shared edges within a paired block group have zero setback, while external (free) side edges retain the full side-yard distance, creating a 2× side-yard gap between adjacent block groups.

The algorithm introduces a novel two-layer neighbour detection: first, Step 2's facade classification identifies which edges are nominally "side" edges; second, geometric intersection testing against adjacent parcels determines which of these side edges are physically shared (touching a neighbour). Only edges that pass both tests are assigned zero setback. A graph-based row-neighbour matching algorithm uses oriented bounding box long-axis projection to distinguish genuine lateral (row) neighbours from front-to-back adjacencies, preventing false zero-setback assignments on rear boundaries—a critical safeguard against over-densification.

Mathematical Formulation

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Interpretation Guidance

References

1. Panerai, P., Castex, J., Depaule, J.C. & Samuels, I. (2004). Urban Forms: The Death and Life of the Urban Block. Architectural Press. DOI: 10.4324/9780080472454
2. Marshall, S. (2005). Streets and Patterns. Spon Press. DOI: 10.4324/9780203589397
3. Steadman, P. (2014). Building Types and Built Forms. Troubador Publishing.
4. Jacobs, J. (1961). The Death and Life of Great American Cities. Random House.
5. Kropf, K. (1996). Urban tissue and the character of towns. Urban Design International, 1(3), 247–263. DOI: 10.1057/udi.1996.32
6. Moudon, A.V. (1986). Built for Change: Neighborhood Architecture in San Francisco. MIT Press.

Theoretical Background

Attached order (Bitişik Nizam, "B nizam") represents the densest conventional building arrangement in Turkish planning, where buildings extend to both lateral parcel boundaries, forming a continuous street wall without side yards. This typology corresponds to the row-house, townhouse, and perimeter-block morphologies characteristic of historic urban cores and 19th-century expansion districts across Europe and the Mediterranean basin. The defining regulatory characteristic is the complete absence of lateral setbacks: the building may (and typically does) extend to the full width of the parcel between side boundaries.

The algorithm reflects this with a simplified setback model containing only two non-zero distances: front setback (from the road-facing edge) and rear setback (from the back boundary). All lateral edges unconditionally receive zero setback. The corner-parcel exception mirrors that of the block-coverage algorithm: when a parcel has multiple front facades (a corner condition), the rear setback may be reduced to zero, recognising that corner buildings define two street frontages and the concept of a "rear" becomes ambiguous. The theoretical framework draws on urban block morphology as systematised by Panerai et al. (2004) and the streetscape continuity principle central to both classical urban design (Sitte 1889) and contemporary form-based codes (Parolek et al. 2008).

Mathematical Formulation

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Interpretation Guidance

References

1. Sitte, C. (1889/1965). City Planning According to Artistic Principles. Trans. G.R. Collins & C.C. Collins. Random House.
2. Parolek, D.G., Parolek, K. & Crawford, P.C. (2008). Form-Based Codes: A Guide for Planners, Urban Designers, Municipalities, and Developers. Wiley.
3. Panerai, P., Castex, J., Depaule, J.C. & Samuels, I. (2004). Urban Forms: The Death and Life of the Urban Block. Architectural Press. DOI: 10.4324/9780080472454
4. Kostof, S. (1991). The City Shaped: Urban Patterns and Meanings Through History. Bulfinch Press.
5. Lynch, K. (1981). A Theory of Good City Form. MIT Press.
6. Krier, L. (2009). The Architecture of Community. Island Press. DOI: 10.5822/978-1-61091-124-5
7. Steadman, P. (2014). Building Types and Built Forms. Troubador Publishing.

Theoretical Background

While the coverage footprint algorithms (3.1–3.4) produce rectangular building envelopes satisfying regulatory constraints, real buildings exhibit formal variety—L-shaped wings, courtyard voids, articulated facades—that affect microclimate, daylight access, and architectural character. The macroform placement algorithm introduces template-based form substitution: a library of user-supplied building geometries (loaded from any open QGIS polygon layer) is matched to each building envelope based on aspect ratio compatibility, then fitted within the envelope through affine transformation (translation, rotation, uniform scaling).

This approach echoes the shape grammar formalism (Stiny & Gips 1972; Stiny 1980) where a vocabulary of primitive forms is recursively transformed and composed. The algorithm performs a simplified single-step grammar application: the building envelope acts as the spatial label constraining the transformation, and the template is the shape rule's replacement geometry. Aspect ratio matching (envelope width/height vs. template bounding box width/height) serves as the primary fitness criterion, reflecting the architectural principle that building form should respond to site proportions (Alexander et al. 1977, Pattern 106: "Positive Outdoor Space").

Mathematical Formulation

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Interpretation Guidance

References

1. Stiny, G. & Gips, J. (1972). Shape Grammars and the Generative Specification of Painting and Sculpture. Information Processing 71, pp. 1460–1465.
2. Stiny, G. (1980). Introduction to shape and shape grammars. Environment and Planning B, 7(3), 343–351. DOI: 10.1068/b070343
3. Alexander, C., Ishikawa, S. & Silverstein, M. (1977). A Pattern Language. Oxford University Press.
4. Duarte, J.P. (2005). A discursive grammar for customizing mass housing. Automation in Construction, 14(2), 265–275. DOI: 10.1016/j.autcon.2004.07.013
5. Koning, H. & Eizenberg, J. (1981). The language of the prairie: Frank Lloyd Wright's prairie houses. Environment and Planning B, 8(3), 295–323. DOI: 10.1068/b080295
6. Mitchell, W.J. (1990). The Logic of Architecture: Design, Computation, and Cognition. MIT Press.

Theoretical Background

Dynamic macroform generation implements a procedural building typology with 12 distinct architectural plan configurations, each governed by structural depth constraints (minimum wing depth 7m, minimum wing width 5m) derived from practical residential construction standards. Unlike the template-based macroform (Step 3B), which requires a pre-existing library of building geometries, the dynamic approach generates forms from parametric rules, offering greater flexibility and eliminating library maintenance overhead.

The 12 form types span the morphological spectrum from simple rectangles (I-form) to complex articulations (E-form, H-form, courtyard), covering the majority of multi-family residential plan configurations documented in the architectural typology literature (Steadman 2014; Lehnerer 2009). The diversity parameter ensures that consecutive parcels receive different forms, creating the heterogeneous streetscape that characterises organic urban growth and avoids the visual monotony of purely repetitive generation. Form feasibility filtering prevents complex forms (O, H, E) from being applied to parcels below minimum dimensional thresholds (20m × 20m for courtyard and H-forms), maintaining architectural realism.

Mathematical Formulation

Parameters

Outputs

Interpretation Guidance

References

1. Steadman, P. (2014). Building Types and Built Forms. Troubador Publishing.
2. Lehnerer, A. (2009). Grand Urban Rules. 010 Publishers. DOI: 10.5949/mitpress/9780262514781.001.0001
3. Müller, P., Wonka, P., Haegler, S., Ulmer, A. & Van Gool, L. (2006). Procedural Modeling of Buildings. ACM Trans. Graph., 25(3), 614–623. DOI: 10.1145/1141911.1141931
4. Parish, Y.I.H. & Müller, P. (2001). Procedural Modeling of Cities. Proc. SIGGRAPH 2001, 301–308. DOI: 10.1145/383259.383292
5. Schumacher, P. (2009). Parametricism: A New Global Style for Architecture and Urban Design. Architectural Design, 79(4), 14–23. DOI: 10.1002/ad.912
6. Wonka, P., Wimmer, M., Sillion, F. & Ribarsky, W. (2003). Instant Architecture. ACM Trans. Graph., 22(3), 669–677. DOI: 10.1145/882262.882324
7. Alexander, C. (1964). Notes on the Synthesis of Form. Harvard University Press.

Theoretical Background

Quality assurance is a critical but often under-automated phase in computational planning workflows. The Building Optimizer performs a systematic compliance audit comparing generated building geometries against parcel-level regulatory constraints, analogous to the plan-checking process in municipal planning departments. It operationalises two verification criteria: (1) density compliance—the aggregate building area within each parcel must not exceed the TAKS-mandated maximum ($A_{\text{buildings}} \leq A_{\text{parcel}} \times \text{TAKS}$), with a 1% numerical tolerance; and (2) geometric validity—building geometries must pass the GEOS isGeosValid() check, which detects self-intersections, ring orientation errors, and other topology violations that would cause downstream processing failures.

The algorithm employs a spatial index (QgsSpatialIndex) for efficient building-to-parcel matching, performing geometric intersection tests only for building-parcel pairs whose bounding boxes overlap. This $O(n \log n)$ approach scales to large datasets with thousands of parcels and buildings, and the boolean status flags (taks_ok, geom_valid) facilitate direct filtering in QGIS for targeted manual correction workflows.

Mathematical Formulation

Parameters

Outputs

Interpretation Guidance

References

1. Longley, P.A., Goodchild, M.F., Maguire, D.J. & Rhind, D.W. (2015). Geographic Information Science and Systems. 4th ed. Wiley.
2. O'Sullivan, D. & Unwin, D.J. (2010). Geographic Information Analysis. 2nd ed. Wiley. DOI: 10.1002/9780470549094
3. Burrough, P.A., McDonnell, R.A. & Lloyd, C.D. (2015). Principles of Geographical Information Systems. 3rd ed. Oxford University Press.
4. Rigaux, P., Scholl, M. & Voisard, A. (2001). Spatial Databases: With Application to GIS. Morgan Kaufmann.
5. Worboys, M.F. & Duckham, M. (2004). GIS: A Computing Perspective. 2nd ed. CRC Press. DOI: 10.1201/9780367805777

Theoretical Background

Hard surfaces—pathways, service yards, terraces, and access routes surrounding buildings—constitute the connective tissue of settlement morphology, mediating between private building interiors and the public realm. In Turkish planning regulation, these are classified as sert zemin (impervious/hard ground), distinct from yumusak zemin (soft/landscaped ground), with minimum ratios specified in some municipal codes for stormwater management and urban heat island mitigation.

The algorithm generates hard surfaces through a classical computational geometry pipeline: buffer (dilate) the building footprint by a specified walkway width, intersect the buffered polygon with the parcel boundary to ensure surfaces remain within the property, and subtract the original building footprint to avoid double-counting. This three-step Boolean operation pattern—buffer, intersect, difference—is a common idiom in GIS-based site analysis (Longley et al. 2015) and generates a clean topological result: a polygon (or multi-polygon) representing only the walkable area around the building, clipped to the parcel extent.

Mathematical Formulation

Parameters

Outputs

Interpretation Guidance

References

1. Longley, P.A., Goodchild, M.F., Maguire, D.J. & Rhind, D.W. (2015). Geographic Information Science and Systems. 4th ed. Wiley.
2. Forman, R.T.T. (2014). Urban Ecology: Science of Cities. Cambridge University Press. DOI: 10.1017/CBO9781139030472
3. Lehmann, S. (2016). Sustainable urbanism: towards a framework for quality and optimal density? Future Cities and Environment, 2(1), 1–14. DOI: 10.1186/s40984-016-0021-3
4. Gehl, J. (2011). Life Between Buildings: Using Public Space. Island Press.
5. Whyte, W.H. (1980). The Social Life of Small Urban Spaces. Conservation Foundation.

Theoretical Background

Parking layout design is fundamentally a geometric packing problem: given a spatial container (the parking area polygon), maximise the number of regularly-shaped stalls while respecting manoeuvring aisle widths, edge margins, and vehicle entry corridors. The algorithm employs a concentric-ring tessellation strategy: parking stalls are placed along the perimeter of the parking area polygon, oriented parallel to each edge segment. After exhausting the perimeter, the polygon is shrunk inward by one stall depth plus one aisle width, and the process repeats—forming concentric "rings" of parking until no further stalls can be placed.

This approach is structurally analogous to offset-polygon algorithms in computational geometry (Kallay 1984) and the morphological erosion used in the FAR coverage solver (Step 3.1). The vehicle entry point, when provided via the road layer, creates a 7m-wide corridor (3.5m radius from the nearest approach point on the road) that is excluded from stall placement, ensuring unimpeded vehicle access. The per-segment stall count follows a linear tessellation: $n = \lfloor L / (w + g) \rfloor$, where $L$ is the segment length, $w$ the stall width (default 2.5m, the Turkish standard), and $g$ the inter-stall gap.

Mathematical Formulation

Parameters

Outputs

Interpretation Guidance

References

1. Shoup, D. (2005). The High Cost of Free Parking. APA Planners Press. DOI: 10.4324/9781351179560
2. ITE (Institute of Transportation Engineers). (2010). Parking Generation. 4th ed. ITE.
3. Kallay, M. (1984). The geometry of offset curves. Computer-Aided Design, 16(6), 295–299. DOI: 10.1016/0010-4485(84)90165-9
4. Litman, T. (2020). Parking Management Best Practices. 2nd ed. APA Planners Press. DOI: 10.4324/9781351179492
5. Manville, M. & Shoup, D. (2005). Parking, People, and Cities. Journal of Urban Planning and Development, 131(4), 233–245. DOI: 10.1061/(ASCE)0733-9488(2005)131:4(233)
6. Ben-Joseph, E. (2012). Rethinking a Lot: The Design and Culture of Parking. MIT Press.

Theoretical Background

The landscape generator implements a Poisson-disc-like point distribution within the softscape area of each parcel (total parcel area minus building footprints, optionally further restricted to designated green-space polygons). Trees are placed through rejection sampling within the axis-aligned bounding box of the available area, with each candidate point tested against two constraints: (1) it must fall within the available (non-building) area of the parcel, and (2) it must maintain a minimum distance $d_{\text{tree-tree}}$ from all previously placed trees. Each placement attempt is limited to 500 random trials per tree, after which the tree is abandoned if no valid location is found.

The tree density parameter is expressed per 500m² of available area—a unit chosen because it corresponds approximately to a 22m × 22m garden zone, a typical Turkish residential garden parcel scale. The algorithm distinguishes between trees placed in parcel-level gardens (source_type='parsel') and those placed in dedicated green/recreation areas (source_type='yesil_alan'), supporting differentiated styling and counting in later analysis stages.

Mathematical Formulation

Parameters

Outputs

Interpretation Guidance

References

1. Corner, J. (1999). Recovering Landscape: Essays in Contemporary Landscape Architecture. Princeton Architectural Press.
2. Waldheim, C. (2006). The Landscape Urbanism Reader. Princeton Architectural Press. DOI: 10.1007/1-56898-672-6
3. McHarg, I.L. (1969). Design with Nature. Natural History Press. DOI: 10.1016/0016-7185(70)90053-8
4. Nowak, D.J. & Dwyer, J.F. (2007). Understanding the Benefits and Costs of Urban Forest Ecosystems. In: Kuser, J.E. (ed.) Urban and Community Forestry in the Northeast. Springer, pp. 25–46. DOI: 10.1007/978-1-4020-4289-8_2
5. Jim, C.Y. & Chen, W.Y. (2009). Ecosystem services and valuation of urban forests in China. Cities, 26(4), 187–194. DOI: 10.1016/j.cities.2009.03.003
6. Cook, E.A. (2002). Landscape structure indices for assessing urban ecological networks. Landscape and Urban Planning, 58(2–4), 269–280. DOI: 10.1016/S0169-2046(01)00226-2

Theoretical Background

The settlement finalizer synthesises the outputs of all preceding steps into quantitative planning metrics that directly inform regulatory review and decision-making. It implements what urban analytics literature terms a planning support system (PSS) function (Geertman & Stillwell 2009): the automated calculation of density indicators (TAKS, FAR/emission), population estimates, and infrastructure adequacy ratios from spatial data. The algorithm's two-output design separates aggregate statistics (attribute-only table, no geometry) from parcel-level diagnostics (spatial polygon layer with per-parcel parking adequacy), supporting both high-level reporting and targeted intervention.

Population estimation uses the standard density formula common to Turkish planning practice: total construction area divided by average dwelling size yields estimated dwelling count; dwellings multiplied by average household size yields estimated population. The default values—120m² average dwelling size and 2.77 persons per household—reflect 2026 Turkish Statistical Institute (TUIK) data and Izmir regional planning norms. The parking adequacy check applies the 2026 Turkish Parking Regulation (Otopark Yonetmeligi) standard of one parking space per dwelling unit for residential uses.

Mathematical Formulation

Parameters

Outputs

Output 1 — General Statistics (OUTPUT_STATS):

Statistics produced: parsel_sayisi, bina_sayisi, toplam_parsel_alan_m2, toplam_bina_alan_m2, ortalama_taks, tahmini_toplam_daire, tahmini_toplam_nufus, gerekli_otopark_toplam, mevcut_otopark_toplam, otopark_fark, and optionally agac_sayisi and sert_zemin_alan_m2.

Output 2 — Parking Adequacy Report (OUTPUT_PARKING_REPORT):

Interpretation Guidance

References

1. Geertman, S. & Stillwell, J. (eds.). (2009). Planning Support Systems: Best Practice and New Methods. Springer. DOI: 10.1007/978-1-4020-8952-7
2. Batty, M. (2013). The New Science of Cities. MIT Press. DOI: 10.7551/mitpress/9399.001.0001
3. Berghauser Pont, M. & Haupt, P. (2010). Spacematrix: Space, Density and Urban Form. NAi Publishers.
4. Shoup, D. (2005). The High Cost of Free Parking. APA Planners Press. DOI: 10.4324/9781351179560
5. Ewing, R. & Cervero, R. (2010). Travel and the Built Environment: A Meta-Analysis. Journal of the American Planning Association, 76(3), 265–294. DOI: 10.1080/01944361003766766
6. Brail, R.K. & Klosterman, R.E. (eds.). (2001). Planning Support Systems: Integrating Geographic Information Systems, Models, and Visualization Tools. ESRI Press.
7. TUIK (Turkish Statistical Institute). (2026). Address-Based Population Registration System Results. Ankara.

Theoretical Background

Urban furniture—benches, lighting, waste receptacles, signage, fountains, and play equipment—constitutes the "micro-architecture" of public space that significantly influences perceived quality, safety, and usability (Whyte 1980; Gehl 2011). This algorithm creates a stylised point layer pre-populated with 12 furniture types as template features (all located at the coordinate origin), each configured with SVG icon symbology, categorised rendering, and ValueMap-driven attribute forms. It functions as a digital catalogue that students and planners populate manually by copying template features to desired locations on the map.

The post-processing phase (postProcessAlgorithm) automatically applies a QgsCategorizedSymbolRenderer with SVG marker symbols and configures the attribute form to use dropdown (ValueMap) widgets for furniture type selection, hiding internal metadata fields. This represents a pedagogical design pattern for QGIS-based planning education, where the tool provides the structured framework and the user provides the site-specific placement decisions—an instance of scaffolded computational support in planning pedagogy.

Parameters

Furniture Catalogue

Outputs

Interpretation Guidance

References

1. Whyte, W.H. (1980). The Social Life of Small Urban Spaces. Conservation Foundation.
2. Gehl, J. (2011). Life Between Buildings: Using Public Space. Island Press.
3. Marcus, C.C. & Francis, C. (eds.). (1997). People Places: Design Guidelines for Urban Open Space. 2nd ed. Wiley.
4. Carmona, M., Heath, T., Oc, T. & Tiesdell, S. (2010). Public Places, Urban Spaces: The Dimensions of Urban Design. 2nd ed. Routledge. DOI: 10.4324/9780080515427
5. Mehta, V. (2013). The Street: A Quintessential Social Public Space. Routledge. DOI: 10.4324/9780203067635

Theoretical Background

Staircase geometry in two-dimensional plan representation follows standardised dimensional conventions derived from building codes and ergonomic research. The rise-run relationship (Blondel's formula) governs comfortable stair proportions: $2h_{\text{rise}} + g_{\text{going}} \approx 63\text{cm}$ (Neufert & Neufert 2012). In plan view, a staircase is represented as a rectangular projection whose length equals the total going ($n_{\text{steps}} \times \text{tread depth}$) and whose width equals the stair flight width. The algorithm generates this rectangular projection oriented along the direction of the input line geometry, with optional hatch-pattern symbology applied in post-processing to indicate tread lines.

This computational representation abstracts the staircase to its planimetric envelope, suitable for settlement-plan scale drawings (1:200 to 1:1000) rather than detailed construction documents. The simplification is consistent with the geometric level of detail (LOD) taxonomy proposed by Biljecki et al. (2016) for 3D city models, adapted here to 2D plan representation.

Mathematical Formulation

Parameters

Outputs

References

1. Neufert, E. & Neufert, P. (2012). Architects' Data. 4th ed. Wiley-Blackwell. DOI: 10.1002/9781118255346
2. Biljecki, F., Ledoux, H. & Stoter, J. (2016). An improved LOD specification for 3D building models. Computers, Environment and Urban Systems, 59, 25–37. DOI: 10.1016/j.compenvurbsys.2016.04.005
3. Templer, J. (1992). The Staircase: Studies of Hazards, Falls, and Safer Design. MIT Press.
4. Preiser, W.F.E. & Ostroff, E. (eds.). (2001). Universal Design Handbook. McGraw-Hill.

Theoretical Background

Accessibility ramps are mandatory elements of inclusive urban design, governed by strict slope and dimensional requirements. International standards (ADA Standards for Accessible Design 2010; TS 12576 in Turkey) specify a maximum running slope of 1:12 (8.33%) for ramps, with a minimum clear width of 0.90m for single-direction travel and 1.50m for bidirectional. The ramp generator creates a rectangular plan projection oriented along the input geometry direction, with a gradient-fill symbol applied in post-processing to visually distinguish ramps from stairs and level surfaces.

The ramp length and width parameters, together with the implied rise, determine the effective slope. Users must independently verify that the resulting slope ratio complies with applicable accessibility codes. The algorithm provides the geometric envelope; regulatory compliance verification remains the planner's responsibility.

Mathematical Formulation

Parameters

Outputs

References

1. ADA (Americans with Disabilities Act). (2010). ADA Standards for Accessible Design. U.S. Department of Justice.
2. Iwarsson, S. & Ståhl, A. (2003). Accessibility, usability and universal design. Disability and Rehabilitation, 25(2), 57–66. DOI: 10.1080/0963828021000007965
3. Preiser, W.F.E. & Ostroff, E. (eds.). (2001). Universal Design Handbook. McGraw-Hill.
4. Imrie, R. & Hall, P. (2001). Inclusive Design: Designing and Developing Accessible Environments. Spon Press. DOI: 10.4324/9780203362501
5. Neufert, E. & Neufert, P. (2012). Architects' Data. 4th ed. Wiley-Blackwell. DOI: 10.1002/9781118255346

Theoretical Background

Pedestrian crossings are critical nodes in the multimodal transportation network, representing the formalised intersection of pedestrian desire lines with vehicular carriageways. Their geometric representation in settlement plans follows cartographic conventions that prioritise visual legibility: a transverse line (or polygon) spanning the carriageway width, rendered with a distinctive zebra-stripe or dashed pattern. The algorithm functions primarily as a symbology pass-through, copying the input line geometry to the output while applying map-unit-based dashed-line rendering that creates the characteristic crossing pattern at any scale.

The post-processing symbology applies a dashed line style (dash pattern 3:3) with a line width set in map units (metres), ensuring that the crossing symbol scales correctly with the plan—a cartographic design principle documented in Slocum et al. (2009) for thematic mapping.

Parameters

Outputs

Interpretation Guidance

References

1. Slocum, T.A., McMaster, R.B., Kessler, F.C. & Howard, H.H. (2009). Thematic Cartography and Geovisualization. 3rd ed. Pearson.
2. ITE (Institute of Transportation Engineers). (2010). Designing Walkable Urban Thoroughfares: A Context Sensitive Approach. ITE/CNU.
3. Gehl, J. (2010). Cities for People. Island Press.

Theoretical Background

Axis-based parking generation represents a sketch-and-generate paradigm where the designer draws a spatial control line (the parking axis) and a subsequent algorithm (H6) populates parking stalls along it according to configurable geometric rules. This two-step approach separates creative spatial judgment (where should parking go?) from mechanical tessellation (how should stalls be laid out?)—a design pattern that echoes the separation of concerns principle in parametric design workflows (Woodbury 2010).

This algorithm creates the scratch (in-memory) line layer with preconfigured attribute fields and ValueMap widgets, making it a configuration tool rather than a geometry generator. The output layer has no features initially; the user draws them interactively in QGIS, selecting parking angle (90°, 60°, 45°, 0°/parallel), generation side (right, left, both), stall gap, and axis offset for each drawn axis line via dropdown forms. This interactive parametric approach has roots in direct manipulation interfaces for spatial design.

Scratch Layer Schema

References

1. Woodbury, R. (2010). Elements of Parametric Design. Routledge. DOI: 10.4324/9780203800201
2. Shoup, D. (2005). The High Cost of Free Parking. APA Planners Press. DOI: 10.4324/9781351179560

Theoretical Background

Axis-based parking generation solves the problem of directed, geometry-controlled stall placement along a user-defined control line—a common requirement when parking layout must conform to irregular site boundaries, curved roads, or specific access patterns that the polygon-based perimeter algorithm (Step 6) cannot express. The algorithm processes each axis line segment independently, computing the effective stall width and depth based on the parking angle, then placing stalls at regular intervals along the segment, offset perpendicularly by the axis distance and stall depth.

The parking angle transformation uses trigonometric decomposition derived from standard traffic engineering design guidelines (ITE 2010; Neufert & Neufert 2012). For angled parking (60° or 45°), the effective width along the axis is the stall width divided by the cosine of the complement angle: $w_{\text{eff}} = w / \cos(90° - \alpha)$. The effective depth (perpendicular projection) is $d_{\text{eff}} = d \cos(90° - \alpha) + w \sin(90° - \alpha)$, accounting for the angular geometry.

Mathematical Formulation

Parameters

Outputs

Interpretation Guidance

References

1. ITE (Institute of Transportation Engineers). (2010). Parking Generation. 4th ed. ITE.
2. Neufert, E. & Neufert, P. (2012). Architects' Data. 4th ed. Wiley-Blackwell. DOI: 10.1002/9781118255346
3. Shoup, D. (2005). The High Cost of Free Parking. APA Planners Press. DOI: 10.4324/9781351179560
4. Litman, T. (2020). Parking Management Best Practices. 2nd ed. APA Planners Press. DOI: 10.4324/9781351179492
5. Ben-Joseph, E. (2012). Rethinking a Lot: The Design and Culture of Parking. MIT Press.