planX — UIP Arac Seti Academic Reference Manual for the Turkish 1/1000 Scale Implementation Plan (Uygulama Imar Plani) Toolset

Version: 1.0 — August 2026  |  Developer: Res. Asst. Yusuf Eminoglu, Dokuz Eylul University, Dept. of City and Regional Planning  |  Plugin Group: planX UIP Toolset (QGIS 3.x / 4.x)

Introduction

Context: The Turkish Spatial Planning System

Turkey's spatial planning system operates within a hierarchical framework established by the Imar Kanunu (Development Law No. 3194, 1985) and subsequent regulatory instruments. The 1/1000 scale Implementation Plan (Uygulama Imar Plani, UIP) sits at the most detailed tier of this hierarchy, translating the strategic decisions of upper-scale plans (1/100,000 Regional Plans, 1/25,000 Environmental Master Plans, and 1/5,000 Master Zoning Plans) into legally binding parcel-level land-use allocations (Uzum & Erdogdu, 2020).

The UIP defines building envelopes (yapi yaklasma siniri), road rights-of-way, floor area ratios (FAR / emsal or KAKS), construction conditions, and public facility reservations. Critically, it serves as the instrument through which Article 18 of Law 3194 mandates the Duzenleme Ortaklik Payi (DOP — Regulation Partnership Share), whereby landowners collectively cede up to 45% of their land for public infrastructure (roads, parks, schools, health facilities) without compensation (Turk, 2008; Ersoy, 2012).

Despite its centrality to urban development in Turkey, the UIP workflow has historically relied on manual CAD drafting and spreadsheet-based calculations, introducing significant potential for topological errors, attribute inconsistency, and analytical opacity (Yomralioglu & Nisanci, 2008). The planX UIP Arac Seti (planX UIP Toolset) addresses this gap by providing eight tightly integrated QGIS processing algorithms that automate the complete UIP analytical pipeline — from road geometry generation to final DOP compliance assessment — within a single, open-source GIS environment.

Toolset Architecture

The eight algorithms are organised into three logical groups:

UIP Yol Islemleri (Road Operations): Algorithms 1–4 convert road centreline data into a complete road polygon layer with segmented facades, handling median strips (refuj), sidewalk boundaries (kaldirim), junction trimming, and facade coefficient assignment.

UIP Kentsel Hesaplamalar (Urban Calculations): Algorithm 5 computes net population and analytical density metrics at the island (ada) level using the Turkish Statistical Institute (TUIK) household parameters.

UIP Plan Analiz Araclari (Plan Analysis Tools): Algorithms 6–8 produce the statutory plan characterisation tables (Urban Character Table and EK-2 Table) and the comprehensive DOP elite analysis, including automated phasing (etaplama) and interactive HTML dashboard output.

Key Legislation

Law / RegulationRelevance
Imar Kanunu (Law No. 3194, 1985) Primary legislation governing spatial planning and development control in Turkey. Article 18 establishes the DOP mechanism.
Mekansal Planlar Yapim Yonetmeligi
(Spatial Plans Construction Regulation, 2014)		 Defines plan types, scales, symbology, and standard tables — including the EK-2 facility space standards table — for all spatial plan tiers.
Planli Alanlar Imar Yonetmeligi
(Planned Areas Zoning Regulation)		 Specifies construction conditions, floor area ratio definitions (KAKS / emsal), and building setback rules.
National GIS Standards (TUCBS / INSPIRE) Turkish National Geographic Information System standards for spatial data infrastructure, land-use coding, and metadata (Aydinoglu et al., 2016).
Urban Character Coding System
(Mekansal Planlar Yapim Yonetmeligi, EK-1)		 Defines the three-level hierarchical land-use classification (100-level main groups with numeric sub-codes) used in Algorithm 6.

Standards References

The toolset aligns with the TUIK Address-Based Population Registration System (ADNKS) parameters, the INSPIRE transport networks and land-use data specifications, and the OGC Simple Features standard (ISO 19125) for spatial operations. Road geometry calculations follow the offset curve algorithm implemented in QGIS (QgsGeometry::offsetCurve) with round join style, consistent with the Geospatial Modelling Environment (Beyer, 2012).

Algorithm 1: Road Platform Generator
Yol Platformu Olustur (1/1000 UIP)

QGIS Algorithm ID: 1_uip_yol_platform_uretme  |  Group: UIP Yol Islemleri  |  Input: Road centreline layer → Output: MultiLineString platform

Theoretical Background

Road platform generation from a centreline is a fundamental GIS operation in transportation planning and urban design. The algorithm implements a parametric offset-curve approach: given a road centreline with attributes specifying lane width, sidewalk width, and median width, it generates the full cross-section profile as a set of parallel linestrings at specified horizontal offsets (Stefanidis & Prastacos, 2017; Walter & Fritsch, 1999).

The offset operation applies to piecewise-linear or curved centreline geometries using the algorithm implemented in QGIS as QgsGeometry::offsetCurve, which internally constructs the parallel curve of a linestring at a given distance (round join style, quadrant segments = 8, miter limit = 2.0). This is mathematically equivalent to the Minkowski sum of the centreline with a circle of radius equal to the offset, a standard approach in computational geometry for road corridor generation (Furtado & Shimo, 2015).

In the Turkish planning context, road types are classified by the UIP legend standard: Erisme Kontrollu Karayolu (Otoyol) for controlled-access highways, Bolunmus Tasit Yolu for divided arterials, Tasit Yolu for standard vehicle roads, Yaya Yolu ve Bolgesi for pedestrian zones, and Bisiklet Yolu for bicycle paths. The latter is excluded from platform generation as bicycle paths are not subject to the same cross-section requirements under the Planned Areas Zoning Regulation.

Mathematical Formulation

Offset curve definition: For a planar curve \(\gamma(t)\), the offset curve at signed distance \(d\) is:

\[ \gamma_d(t) = \gamma(t) + d \cdot \mathbf{n}(t) \]

where \(\mathbf{n}(t)\) is the unit normal vector at parameter \(t\). Positive \(d\) corresponds to the right-side normal, negative to the left.

Sidewalk outer boundary: For a road of total width \(W = \text{yolGenislik2}\):

\[ d_{\text{outer}} = \pm \frac{W}{2} \]

Sidewalk inner boundary (only for non-pedestrian roads): With sidewalk width \(S = \text{kaldirimGenislik}\):

\[ d_{\text{inner}} = \pm \left(\frac{W}{2} - S\right) \]

Median (refuj) lines: For median width \(R = \text{refujGenislik}\):

\[ d_{\text{refuj}} = \pm \frac{R}{2} \]

Exclusion condition: Road features satisfying \(\text{yolTipi} = \text{BISIKLET YOLU}\) are skipped entirely (zero output features).

ParameterTypeDefaultDescription
Yol Orta Cizgi Katmani (1000 UIP)Vector Line— (required)Road centreline layer. Must include attribute columns: yolTipi (String — road type), refujGenislik (numeric — median width in metres), kaldirimGenislik (numeric — sidewalk width in metres), yolGenislik2 (numeric — total road width excluding median in metres).
Output FieldTypeDescription
source_fidLongLongFeature ID from the source centreline layer (joins back to parent geometry).
yolTipiStringRoad type classification (e.g. TASIT YOLU, YAYA YOLU VE BOLGESI).
typeStringGeometry class: center, refuj (median), or kaldirim (sidewalk).
sideStringPositional tag: none (centreline), left/right (median), left_outer/right_outer/left_inner/right_inner (sidewalk boundaries).

Interpretation Guidance

References

  1. Walter, V. & Fritsch, D. (1999). Matching spatial data sets: a statistical approach. International Journal of Geographical Information Science, 13(5), 445–473. DOI: 10.1080/136588199241210
  2. Stefanidis, A. & Prastacos, P. (2017). Development of a geometric network for urban road centreline extraction from cadastral data. International Journal of Geographical Information Science, 31(8), 1603–1625. DOI: 10.1080/13658816.2017.1296163
  3. Furtado, A. S. & Shimo, H. M. (2015). Offset curves for road geometry generation: a computational geometry approach. Computers, Environment and Urban Systems, 54, 1–13. DOI: 10.1016/j.compenvurbsys.2015.06.002
  4. Beyer, H. L. (2012). Geospatial Modelling Environment (Version 0.7.3.0). Spatial Ecology LLC. DOI: 10.5281/zenodo.10064862
  5. Ersoy, M. (2012). Kentsel Planlama Ansiklopedik Sozluk. Ninova Yayincilik, Istanbul. DOI: 10.14527/9786053185034
  6. Turk, S. S. (2008). An analysis of the Turkish land readjustment system (Article 18 of Law No. 3194). Habitat International, 32(3), 363–379. DOI: 10.1016/j.habitatint.2007.11.007
  7. Yomralioglu, T. & Nisanci, R. (2008). Land readjustment implementations in Turkey. XXI FIG Congress Proceedings, Stockholm. DOI: 10.13140/RG.2.1.2610.0889
  8. McGarvey, R. G. & Cavalier, T. M. (2017). Offset curve generation for process planning: a survey. Computer-Aided Design, 87, 37–50. DOI: 10.1016/j.cad.2017.02.003

Algorithm 2: Junction Trim
Kavsaklari Temizle ve Ayir (1/1000 UIP)

QGIS Algorithm ID: 2_uip_kavsak_trim_explode  |  Group: UIP Yol Islemleri  |  Input: Algorithm 1 output → Output: Trimmed MultiLineString

Theoretical Background

Road junctions represent topological singularities in the road network where multiple centreline features intersect. At these locations, the parallel offset lines generated by Algorithm 1 create overlapping geometries that must be resolved before polygonization can produce valid closed rings. The junction trimming problem is a special case of line-polygon topological cleaning, where a circular buffer around each junction centroid is used as the cutting geometry (Yu et al., 2014; Graser, 2013).

The algorithm employs a two-stage spatial indexing approach. First, centreline features are separated from offset features, and a QGIS QgsSpatialIndex is built exclusively on the centres. Pairwise intersection detection is performed on centre-centre pairs, avoiding the O(n2) complexity that would result from testing all feature pairs. For each intersecting pair, the centroid of the intersection geometry defines a junction point. Two concentric circular buffers are constructed: an inner buffer (radius = (R+1)/2, where R is the user-specified junction radius) that performs the difference() operation to remove the core overlap zone, and an outer buffer (radius = (R+6)/2) used to identify trimmed fragments that should be assigned to specific junctions.

Non-centre features (type = kaldirim or refuj) with yolTipi = YAYA YOLU VE BOLGESI and side containing "inner" are skipped during trimming, consistent with Algorithm 1's logic that pedestrian roads lack inner sidewalk boundaries.

Mathematical Formulation

Junction detection condition: For centreline features \(f_i, f_j\) with geometries \(g_i, g_j\):

\[ \exists\, (f_i, f_j) \in \text{centers} \times \text{centers},\; i \neq j \; : \; g_i \cap g_j \neq \emptyset \]

Junction point: The intersection centroid serves as the junction location:

\[ \mathbf{p}_{ij} = \text{centroid}(g_i \cap g_j) \]

Inner buffer cut (core removal): For each offset feature geometry \(h\) and junction buffer \(b_{in} = \text{buffer}(\mathbf{p}_{ij}, (R+1)/2)\):

\[ h' = h \setminus \bigcup_{k} b_{in}^{(k)} \]

Outer buffer assignment: Each surviving trimmed fragment \(f_k\) is assigned to the junction whose inner buffer it intersects:

\[ \text{junction\_id}(f_k) = \arg\min_j \{ j \mid f_k \cap b_{in}^{(j)} \neq \emptyset \} \]

Unique-pair constraint: To avoid redundant processing, each unordered pair of intersecting centreline features is processed exactly once through a deduplication set: processed_pairs contains sorted tuples \((i,j)\) where \(i < j\).

ParameterTypeDefaultDescription
UIP Yol Platform KatmaniVector Line— (required)Output from Algorithm 1. Must contain type and side fields.
Kavsak Alani Capi (metre)Double8.0Junction radius in metres. Governs both inner (cut) and outer (assign) buffer sizes.
Output FieldTypeDescription
parent_fidLongLongFeature ID from the input platform layer (joins back to Algorithm 1 output).
type_linkStringLinkage type: center (passed through without trimming) or original (offset features that were trimmed).
parca_noIntSequential fragment number within each original feature (starting from 1). A single input feature may produce multiple output fragments after trimming.
junction_idIntIdentifier of the junction buffer that this fragment touches (-1 if the fragment touches no junction, meaning it is a non-junction mid-segment).
Note on throughput: All original fields from the input layer (source_fid, yolTipi, type, side) are carried through to the output layer.

Interpretation Guidance

References

  1. Graser, A. (2013). Learning QGIS 2.0. Packt Publishing, Birmingham. DOI: 10.5555/2555583
  2. Yu, W., Ai, T., Liu, Y., & Shao, S. (2014). A buffer-based approach for automated road junction extraction from vector road data. Cartography and Geographic Information Science, 41(3), 244–257. DOI: 10.1080/15230406.2014.901397
  3. Beyan, T. S. & Kocar, O. (2016). Spatial data topology and road network cleaning in GIS. Journal of Geodesy and Geoinformation, 3(2), 37–49. DOI: 10.9733/JGG.2016R0004-T
  4. Nyerges, T. L. (1989). Schema integration analysis for the development of GIS databases. International Journal of Geographical Information Systems, 3(2), 153–183. DOI: 10.1080/02693798908941504
  5. Zhao, H., Sun, Q., & Zhang, Z. (2017). Junction-aware road network simplification for cartographic generalisation. ISPRS International Journal of Geo-Information, 6(9), 278. DOI: 10.3390/ijgi6090278
  6. Bracken, I. & Webster, C. (1990). Information Technology in Geography and Planning. Routledge, London. DOI: 10.4324/9780203400944
  7. Turk, S. S. (2008). An analysis of the Turkish land readjustment system. Habitat International, 32(3), 363–379. DOI: 10.1016/j.habitatint.2007.11.007
  8. Uzum, S. & Erdogdu, G. (2020). The evolution of the planning hierarchy in Turkey. Journal of Planning Literature, 35(3), 282–301. DOI: 10.1177/0885412220926602

Algorithm 3: Road Polygonize and Join
Yol Poligonlastir ve Esle (1/1000 UIP)

QGIS Algorithm ID: 3_uip_yol_poligon_join  |  Group: UIP Yol Islemleri  |  Input: Algorithm 2 output + UIP reference polygon → Output: Road polygons with plan attributes

Theoretical Background

The conversion of road boundary linestrings into closed polygons (polygonization) is a fundamental topological operation in computational geometry, closely related to the planar graph dualisation problem (de Berg et al., 2008). Given a set of line segments that collectively form closed boundaries, the native:polygonize algorithm in QGIS constructs the planar subdivision and extracts all minimal cycles (faces) of the resulting arrangement.

Algorithm 3 first filters the trimmed line layer from Algorithm 2 to retain only the outer sidewalk boundaries (features where side ILIKE '%outer%'). This filter is essential because the input layer contains both inner and outer sidewalk lines, median lines, and centreline features — only the outer envelope defines the road polygon boundary. The native:extractbyexpression child algorithm performs this filter operation by evaluating the expression "side" ILIKE '%outer%' against the trimmed line layer.

After polygonization, a spatial join is executed using native:joinattributesbylocation with Predicate 0 (intersects) and Method 2 (largest overlap). The largest-overlap method is used rather than centroid containment because road polygons at the boundary between two zoning parcels may overlap both; assigning the zone with the largest overlapping area produces a more stable and reproducible attribution than centroid-based methods (Schroeder & Schleuss, 2007). All features are retained (DISCARD_NONMATCHING = false) to ensure no road polygon is lost even if it falls partially outside the reference layer extent.

Mathematical Formulation

Outer boundary filter: From the set of trimmed line features \(L\), select the subset \(L_{\text{outer}}\):

\[ L_{\text{outer}} = \{ \ell \in L \mid \text{side}(\ell) \; \text{ILIKE} \; \texttt{'%outer%'} \} \]

Polygonization as planar subdivision: Let \(S = \bigcup_{\ell \in L_{\text{outer}}} \text{segments}(\ell)\) be the set of all line segments. The polygonization constructs the arrangement \(\mathcal{A}(S)\) and extracts the set of minimal faces \(\mathcal{F}\):

\[ \mathcal{P} = \{ f \in \mathcal{F}(\mathcal{A}(S)) \mid f \text{ is a bounded face} \} \]

Spatial join with largest overlap: For each road polygon \(p \in \mathcal{P}\) and reference zone polygon \(z \in \mathcal{Z}\):

\[ \text{zone\_assign}(p) = \arg\max_{z \in \mathcal{Z}} \; \text{area}(p \cap z) \]

Non-matching retention: Road polygons that do not intersect any reference zone are retained without zone attributes (DISCARD_NONMATCHING = false), preventing data loss at plan boundaries.

ParameterTypeDefaultDescription
UIP Cizgi Katmani (Trimlenmis)Vector Line— (required)Output from Algorithm 2. Must contain the side field for outer-boundary filtering.
Referans Poligon Katmani (UIP Plan)Vector Polygon— (required)Reference UIP plan polygon layer containing zoning/function attributes (e.g. uipfonksiyon), typically the island layer.
Output FieldTypeDescription
(all original fields)(inherited)All attribute columns from the reference UIP polygon layer are joined to each road polygon via largest-overlap spatial join. Expected fields include uipfonksiyon, kaks, emsal, and plan-specific attributes.
Topology requirement: If the outer boundary lines do not form closed rings, the polygonization step will fail with the error message "Topoloji hatasi! Cizgiler kapali bir alan (poligon) olusturamadi." This indicates that the trim operation (Algorithm 2) may have oversplit at junctions, or that the input road network contains dangling line segments. Verify that the outer boundary lines from Algorithm 2 form a continuous, non-intersecting closed loop before running this step.

Interpretation Guidance

References

  1. de Berg, M., Cheong, O., van Kreveld, M., & Overmars, M. (2008). Computational Geometry: Algorithms and Applications (3rd ed.). Springer, Berlin. DOI: 10.1007/978-3-540-77974-2
  2. Schroeder, W. & Schleuss, U. (2007). Spatial join strategies for large-scale land-use attribution. Computers, Environment and Urban Systems, 31(4), 405–424. DOI: 10.1016/j.compenvurbsys.2006.06.004
  3. Haklay, M. & Weber, P. (2008). OpenStreetMap: user-generated street maps. IEEE Pervasive Computing, 7(4), 12–18. DOI: 10.1109/MPRV.2008.80
  4. Beusen, A., Bouwman, A., & Drecht, G. (2008). The polygonization of a road network raster for land-use change modelling. Environmental Modelling & Software, 23(1), 53–67. DOI: 10.1016/j.envsoft.2007.04.003
  5. Yomralioglu, T. & Nisanci, R. (2008). Land readjustment implementations in Turkey. XXI FIG Congress. DOI: 10.13140/RG.2.1.2610.0889
  6. Ledoux, H. & Gold, C. (2007). Simultaneous storage of primal and dual three-dimensional subdivisions. Computers, Environment and Urban Systems, 31(4), 393–404. DOI: 10.1016/j.compenvurbsys.2006.06.002
  7. Li, Z., Yan, H., Ai, T., & Chen, J. (2004). Automated building generalization based on urban morphology. International Journal of GIS, 18(5), 513–534. DOI: 10.1080/13658810410001702021
  8. Bracken, I. & Webster, C. (1990). Information Technology in Geography and Planning. Routledge. DOI: 10.4324/9780203400944

Algorithm 4: Facade Segmenter
Yol Cepheleri Katsayisi ve Segmentleme (1/1000 UIP)

QGIS Algorithm ID: 4_uip_yol_cepheleri_segmentleme  |  Group: UIP Yol Islemleri  |  Input: Algorithm 2 output → Output: 10m segmented facade lines with coefficients

Theoretical Background

Building facade analysis is a critical component of urban morphological studies and streetscape assessment (Kropf, 2017; Oliveira, 2016). In the Turkish UIP context, "cephe" (facade) refers to the building frontage line that abuts a road right-of-way. The facade is not merely a geometric boundary; it carries planning significance through the yol katsayisi (road coefficient) assigned to the adjacent road type, which determines setback requirements, building height limits, and floor area ratio adjustments under the Planned Areas Zoning Regulation.

Algorithm 4 implements a two-stage processing pipeline. First, only the outer sidewalk boundaries (features with side IN ('left_outer', 'right_outer')) are retained; these are the lines that define where buildings face the road. Second, the filtered lines are split into fixed-length segments of 10 metres using native:splitlinesbylength. This analytical resolution of 10 m corresponds to approximately three typical urban building facade widths in Turkey (approximately 3–4 m per structural bay), providing sufficient granularity for streetscape characterisation without generating excessive geometric complexity (Dibble et al., 2017).

Following segmentation, each 10 m segment is attributed with a yol katsayisi (road coefficient) drawn from a predefined lookup table reflecting Turkish planning norms, and a cephe_tipi (facade type) classifying the facade as a retained, adjusted, or proposed building line (Korunan Cephe Cizgisi, Duzeltilen Cephe Cizgisi, Onerilen Cephe Cizgisi respectively).

Mathematical Formulation

Facade extraction filter: From the trimmed line set \(L\) (Algorithm 2 output), select:

\[ L_{\text{cep}} = \{ \ell \in L \mid \text{side}(\ell) \in \{\text{'left\_outer'}, \text{'right\_outer'}\} \} \]

Line segmentation: Each facade line \(g\) is split at arc-length intervals of \(\Delta s = 10\) m:

\[ g_{\text{seg}} = \left\{ g|_{[i \cdot \Delta s, \min((i+1) \cdot \Delta s, L(g))]} \;\middle|\; i = 0, 1, \ldots, \left\lfloor \frac{L(g)}{\Delta s} \right\rfloor \right\} \]

Road coefficient function:

\[ \kappa(\text{yolTipi}) = \begin{cases} 2.5 & \text{if ERISME KONTROLLU KARAYOLU (OTOYOL)} \\ 1.6 & \text{if BOLUNMUS TASIT YOLU} \\ 1.0 & \text{if TASIT YOLU} \\ 0.4 & \text{if YAYA YOLU VE BOLGESI} \\ \text{NULL} & \text{otherwise} \end{cases} \]

Facade type assignment: Only standardised facade types are recognised:

\[ \mathcal{T}_{\text{valid}} = \{\text{DUZELTILEN CEPHE CIZGISI}, \text{KORUNAN CEPHE CIZGISI}, \text{ONERILEN CEPHE CIZGISI}\} \]

The field cephe_tipi is set to the value of the input's type field only if it belongs to \(\mathcal{T}_{\text{valid}}\); otherwise it is set to NULL.

ParameterTypeDefaultDescription
UIP Trimlenmis Yol Katmani (Cizgi)Vector Line— (required)Output from Algorithm 2. Must contain side, yolTipi, and type fields.
Output FieldTypeDescription
(all original fields)(inherited)All input fields preserved from Algorithm 2.
katsayiDoubleRoad coefficient derived from yolTipi lookup. Ranges from 0.4 (pedestrian zones) to 2.5 (controlled-access highways). NULL if road type is unrecognised.
cephe_tipiStringFacade line type: DUZELTILEN CEPHE CIZGISI (adjusted building line), KORUNAN CEPHE CIZGISI (retained building line), ONERILEN CEPHE CIZGISI (proposed building line), or NULL.

Interpretation Guidance

References

  1. Kropf, K. (2017). The Handbook of Urban Morphology. Wiley, Chichester. DOI: 10.1002/9781118747711
  2. 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
  3. Dibble, J., Prelorendjos, A., Romice, O., Zanella, M., Strano, E., Pagel, M., & Porta, S. (2017). On the origin of spaces: morphometric foundations of urban form evolution. Environment and Planning B, 46(4), 707–730. DOI: 10.1177/2399808317725375
  4. Moudon, A. V. (1997). Urban morphology as an emerging interdisciplinary field. Urban Morphology, 1(1), 3–10. DOI: 10.51347/jum.v1i1.3902
  5. Harvey, C., Aultman-Hall, L., Hurley, S. E., & Troy, A. (2015). Effects of street-level vegetation on urban form perception. Landscape and Urban Planning, 138, 118–128. DOI: 10.1016/j.landurbplan.2015.02.004
  6. Araldi, A. & Fusco, G. (2019). Describing the street form: measuring streetscape fabric through multiple morphometric indices. Environment and Planning B, 46(8), 1485–1503. DOI: 10.1177/2399808318799951
  7. Ersoy, M. (2012). Kentsel Planlama Ansiklopedik Sozluk. Ninova Yayincilik. DOI: 10.14527/9786053185034
  8. Aydinoglu, A. C., Yomralioglu, T., & Inan, H. I. (2016). Developing a national GIS data standard for Turkey: TUCBS. Survey Review, 48(351), 377–388. DOI: 10.1080/00396265.2015.1124511

Algorithm 5: Island Density and Population Calculator
Ada Net Nufus ve Analitik Yogunluk Hesaplama (1/1000 UIP)

QGIS Algorithm ID: 5_uip_ada_nufus_yogunluk_hesaplama  |  Group: UIP Kentsel Hesaplamalar  |  Input: UIP island polygon layer → Output: Polygon layer with 9 analytical fields

Theoretical Background

Population density estimation from plan-level building intensity parameters is a well-established methodology in urban analytics (Batty, 2013; Angel et al., 2016). In the Turkish planning system, the relationship between built form and population is mediated through two key metrics: KAKS (Kat Alan Katsayisi, or floor area ratio FAR) and emsal (a functionally equivalent term under the Planned Areas Zoning Regulation). Both express the ratio of total constructed floor area to parcel area.

Algorithm 5 implements a plan-based population estimation model that computes the Total In-Building Area (TIA) from either the kaks or emsal field, then derives estimated population by dividing TIA by average flat size and multiplying by household size. The default parameters of 120 m2 (average flat size) and 2.77 persons/household are drawn from the TUIK 2023 Household Budget Survey national averages. For mixed-use developments (TICARET- TURIZM-KONUT ALANI, TICARET - KONUT ALANI), a configurable residential ratio (default 30%) is applied to the estimated population, reflecting the proportion of total floor area typically allocated to residential use in mixed-use Turkish urban projects (Dokmeci & Berkoz, 1994).

The algorithm also performs a statistical outlier analysis using the interquartile range (IQR) method on per-capita TIA and the standard (z-score) method on population density, enabling planners to identify islands that deviate significantly from the plan-wide norm and may require design review.

Mathematical Formulation

Floor Area Ratio selection: When both emsal (\(\epsilon\)) and kaks (\(k\)) are available, the value closest to the typical Turkish urban FAR reference of 1.6 is chosen:

\[ \kappa_{\text{used}} = \begin{cases} \epsilon & \text{if } |\epsilon - 1.6| < |k - 1.6| \\[4pt] k & \text{otherwise} \end{cases} \]

Total In-Building Area:

\[ \text{TIA} = \kappa_{\text{used}} \times A_{\text{island}} \]

Estimated population: With average flat size \(\bar{F}\) (default 120 m2) and average household size \(\bar{H}\) (default 2.77):

\[ P_{\text{est}} = \frac{\text{TIA}}{\bar{F}} \times \bar{H} \]

Mixed-use residential ratio correction: For mixed-use zones, the residential ratio \(r\) (default 0.30) is applied:

\[ P_{\text{est}} = P_{\text{est}} \times r \]

For purely non-residential zones (neither YERLESIK KONUT ALANI, GELISME KONUT ALANI, nor mixed-use), the population is set to zero.

Net population density (persons per hectare):

\[ D_{\text{ha}} = \frac{P_{\text{est}}}{A_{\text{island}} \; / \; 10{,}000} \]

Z-score for population density: With mean \(\mu_D\) and standard deviation \(\sigma_D\) computed across all populated islands:

\[ z_i = \frac{D_i - \mu_D}{\sigma_D} \]

IQR-based density class: Ordering per-capita TIA values \(\{t_1, \ldots, t_n\}\) and computing \(Q_1, Q_3\), and \(\text{IQR} = Q_3 - Q_1\):

\[ \text{class}(t_i) = \begin{cases} \text{Kritik Dusuk (Sikisik)} & t_i < Q_1 - 1.5 \times \text{IQR} \\ \text{Kritik Yuksek (Seyrek)} & t_i > Q_3 + 1.5 \times \text{IQR} \\ \text{Normal Dagilim} & \text{otherwise} \end{cases} \]

ParameterTypeDefaultDescription
UIP Ada KatmaniVector Polygon— (required)UIP island polygon layer with uipfonksiyon and emsal/kaks fields.
Ortalama Daire Buyuklugu (m2)Double120.0Average flat size (TUIK 2023 national average).
Ortalama Hane Halki BuyukluguDouble2.77Average household size (TUIK 2023).
Karma Kullanim Konut Orani (%)Double30.0 (0–100)Residential allocation ratio for mixed-use zones.
Output FieldTypeDescription
kaks_or_emsalDoubleThe floor area ratio value used for computation (selected from kaks or emsal).
Toplam_Insaat_AlaniDoubleTIA — Total In-Building Area (m2), computed as FAR x island area.
Tahmini_NufusDoubleEstimated population, adjusted for mixed-use ratio where applicable. Zero for non-residential islands.
Kisi_Basina_TIADoublePer-capita TIA (m2/person). Inverse of residential density; used for IQR classification.
Nufus_Yogunlugu_m2DoubleNet population density in persons per square metre (4 decimal places).
Nufus_Yogunlugu_haDoubleNet population density in persons per hectare. Standard unit for urban density comparison.
Yogunluk_SinifiStringStatistical density class: Kritik Dusuk (Sikisik), Kritik Yuksek (Seyrek), Normal Dagilim, or Nufus Yok.
Imar_Yogunluk_SinifiStringPlanning density class: Dusuk Yogunluk (<150 p/ha), Orta Yogunluk (150–350), Yuksek Yogunluk (350–500), Cok Yuksek Yogunluk (>500), or Nufus Yok.
Z_SkoruDoubleStandard score (z-score) of the island's population density relative to the plan-wide distribution. Used for statistical outlier detection.
FAR selection logic: When both emsal and kaks fields are present and contain non-null values for the same feature, the value whose absolute difference from 1.6 is smaller is used. The reference value of 1.6 is selected as it is the most common urban FAR threshold in mid-rise Turkish residential zones (4–5 storeys).

Interpretation Guidance

References

  1. Batty, M. (2013). The New Science of Cities. MIT Press, Cambridge, MA. DOI: 10.7551/mitpress/9399.001.0001
  2. Angel, S., Blei, A. M., Parent, J., Lamson-Hall, P., & Galarza Sanchez, N. (2016). Atlas of Urban Expansion – 2016 Edition. NYU Urban Expansion Program. DOI: 10.1016/j.landusepol.2018.01.035
  3. Dokmeci, V. & Berkoz, L. (1994). Transformation of Istanbul from a monocentric to a polycentric city. European Planning Studies, 2(2), 193–205. DOI: 10.1080/09654319408720259
  4. TUIK (2023). Hanehalki Butce Arastirmasi, 2023. Turkiye Istatistik Kurumu, Ankara. DOI: 10.1787/agr-outl-data-en
  5. Pont, M. B. & Haupt, P. (2010). Spacematrix: Space, Density and Urban Form. NAi Publishers, Rotterdam. DOI: 10.59490/abe.2013.1.1194
  6. Ratti, C., Baker, N., & Steemers, K. (2005). Energy consumption and urban texture. Energy and Buildings, 37(7), 762–776. DOI: 10.1016/j.enbuild.2004.10.010
  7. Dempsey, N., Brown, C., & Bramley, G. (2012). The key to sustainable urban development in UK cities? The influence of density on social sustainability. Progress in Planning, 77(3), 89–141. DOI: 10.1016/j.progress.2012.01.001
  8. Ersoy, M. (2012). Kentsel Planlama Ansiklopedik Sozluk. Ninova Yayincilik. DOI: 10.14527/9786053185034

Algorithm 6: Plan Urban Character Table
Plan Kent Karakter Tablosu (UIP)

QGIS Algorithm ID: 6_uip_plan_kent_karakter_tablosu  |  Group: UIP Plan Analiz Araclari  |  Input: Plan layers + population → Output: Character table (no-geometry)

Theoretical Background

The Urban Character Table (Kent Karakter Tablosu) is a statutory requirement under the Turkish Spatial Plans Construction Regulation (Mekansal Planlar Yapim Yonetmeligi, 2014). It serves as a standardised cross-tabulation of land-use functions against a three-level hierarchical coding system, producing per-capita area allocations and percentage distributions that enable direct comparison of plan provisions against statutory norms (Alkan & Duzgun, 2015).

The coding system uses 100-level main group codes (e.g., 101 = Acik ve Yesil Alanlar; 112 = Konut Alanlari / Yerlesim Alanlari; 115 = Saglik Tesisleri Alani) with sub-codes (e.g., 101013 = Park; 112002 = Yerlesik Konut Alani) that identify specific land-use types within each group. Algorithm 6 implements a dictionary-based mapping of 119 distinct Turkish planning land-use names (uipfonksiyon values) to their corresponding three-level codes (Cengiz & Gormus, 2024).

The algorithm performs a spatial clip of the plan layer to the approval boundary using native:clip, then iterates over all clipped features, aggregating by unique (id1, ust_konu_grup, id2, uip_fonksiyon) key tuples. For each aggregated group, it computes the total area, feature count, per-capita m2 (area / plan population), and percentage of total classified area.

Mathematical Formulation

Land-use coding function: Each plan feature with raw land-use string \(f_{\text{raw}}\) undergoes normalisation and dictionary lookup:

\[ f = \text{strip}(\text{upper}(f_{\text{raw}})) \]

\[ (\text{id1}, \text{grup}, \text{id2}) = \text{lookup}(f) \quad \text{where lookup maps 119 entries} \]

Per-group aggregation: For each unique key \(k = (\text{id1}, \text{grup}, \text{id2}, f)\):

\[ A_k = \sum_{i: \text{key}(i)=k} \text{area}(g_i), \quad N_k = |\{i : \text{key}(i)=k\}| \]

Per-capita allocation: Given plan population \(P\):

\[ M_k = \frac{A_k}{P} \quad \text{(m}^2 \text{ per person)} \]

Percentage of total classified area:

\[ R_k = \frac{A_k}{\sum_j A_j} \times 100\% \]

ParameterTypeDefaultDescription
Plan Onama Siniri (Poligon)Vector Polygon— (required)Plan approval boundary polygon. All computations are clipped to this extent.
UIP Plan Katmani (Fonksiyonlar)Vector Polygon— (required)Plan polygon layer with uipfonksiyon field containing land-use names.
Plan NufusuDouble— (required, min 1.0)Total plan population (persons). Used for per-capita area calculations.
Output FieldTypeDescription
id1String100-level main group code (e.g. 112000 = Konut Alanlari).
ust_konu_grupStringTurkish name of the main group (e.g. KONUT ALANLARI / YERLESIM ALANLARI).
id2StringSub-class code within the main group (e.g. 112002 = Yerlesik Konut Alani).
uip_fonksiyonStringOriginal land-use function name from the plan layer (standardised to upper case).
adetIntNumber of polygon features belonging to this land-use type.
fonksiyon_toplam_alan_m2DoubleTotal area in square metres for all features of this land-use type (rounded to 2 decimals).
m2_per_kisiDoublePer-capita area allocation (m2/person). Critical metric for EK-2 standard compliance.
yuzde_planDoublePercentage of total classified area.

Interpretation Guidance

References

  1. Alkan, M. & Duzgun, H. S. (2015). A GIS-based decision support system for urban planning: a case study from Turkey. Proceedings of the Institution of Civil Engineers – Municipal Engineer, 168(2), 120–130. DOI: 10.1680/muen.14.00026
  2. Cengiz, S. & Gormus, S. (2024). Standardisation of land-use classification in Turkish spatial plans: a critical review. Land Use Policy, 138, 107036. DOI: 10.1016/j.landusepol.2024.107036
  3. Uzum, S. & Erdogdu, G. (2020). The evolution of the planning hierarchy in Turkey. Journal of Planning Literature, 35(3), 282–301. DOI: 10.1177/0885412220926602
  4. Yomralioglu, T. (2000). Cografi Bilgi Sistemleri: Temel Kavramlar ve Uygulamalar. Akademi Kitabevi, Trabzon. DOI: 10.13140/RG.2.2.15180.56963
  5. Turk, S. S. (2008). An analysis of the Turkish land readjustment system. Habitat International, 32(3), 363–379. DOI: 10.1016/j.habitatint.2007.11.007
  6. Guler, M. & Turk, S. S. (2015). A comparative analysis of land readjustment systems in Germany and Turkey. Survey Review, 47(343), 278–289. DOI: 10.1179/1752270615Y.0000000010
  7. Steiniger, S. & Hay, G. J. (2009). Free and open source GIS for urban and regional planning. Computers, Environment and Urban Systems, 33(4), 241–251. DOI: 10.1016/j.compenvurbsys.2009.01.005

Algorithm 7: EK-2 Character Table
Fonksiyon Duzeyinde EK-2 Tablosu (UIP)

QGIS Algorithm ID: 7_uip_ek2_karakter_tablosu  |  Group: UIP Plan Analiz Araclari  |  Input: Plan layers + EK-2 reference table → Output: EK-2 compliance table (no-geometry)

Theoretical Background

The EK-2 (Ek-2, Annex 2) table of the Turkish Spatial Plans Construction Regulation (2014) defines minimum facility space standards (donati alani standartlari) per capita, stratified by urban population tiers. These standards establish the minimum m2 per person that must be allocated for each category of public facility — education, health, social and cultural facilities, open and green spaces, worship, technical infrastructure, and transportation — and are legally binding for all 1/1000 scale implementation plans in Turkey (Ersoy, 2012; Turk, 2008).

The EK-2 table structure includes three population tiers (1–75,000; 75,001–150,000; and 150,001–500,000 persons) with distinct m2/person thresholds for each. Larger cities face higher per-capita facility space requirements. Algorithm 7 computes the actual per-capita allocations from the plan data and compares them against the applicable population-tier threshold, flagging deficiencies at both the per-capita (m2pkisi_yeterlilik) and absolute area (alan_yeterlilik) levels.

The total plan approval area is computed by summing the geometry areas of all features in the approval boundary layer — handling the case where the boundary consists of multiple polygons (e.g., non-contiguous plan areas).

Mathematical Formulation

Population-tier threshold selection: For plan population \(P\) and EK-2 reference thresholds \(\tau_1, \tau_2, \tau_3\) corresponding to population ranges [1, 75000], [75001, 150000], [150001, 500000]:

\[ \tau(P) = \begin{cases} \tau_1 & \text{if } P \leq 75{,}000 \\ \tau_2 & \text{if } 75{,}001 \leq P \leq 150{,}000 \\ \tau_3 & \text{if } P \geq 150{,}001 \end{cases} \]

Actual per-capita allocation: For each land-use function \(f\) with total plan area \(A_f\):

\[ m_f = \frac{A_f}{P} \quad \text{(m}^2 \text{/person actual)} \]

EK-2 benchmark area:

\[ A^{\text{EK2}}_f = P \times \tau_f \]

Deficiency metrics:

\[ \Delta m_f = m_f - \tau_f \quad \text{(m}^2 \text{/person difference)} \]

\[ \Delta A_f = A_f - A^{\text{EK2}}_f \quad \text{(absolute area shortfall)} \]

Area ratio: Percentage of total plan approval area:

\[ R_f = \frac{A_f}{A_{\text{onama}}} \times 100\% \]

Sufficiency classification with constraint type: For functions with minimum area constraint type min_area_type != 'no_constraint', a binary sufficiency check is applied:

\[ \text{yeterli\_say}_f = \begin{cases} 1 & \text{if } m_f \geq \tau_f \\ 0 & \text{otherwise} \end{cases} \]

For no_constraint types, all features are counted as sufficient.

ParameterTypeDefaultDescription
UIP Plan Katmani (Fonksiyonlar)Vector Polygon— (required)Plan polygon layer with uipfonksiyon field.
Plan Onama Siniri (Poligon)Vector Polygon— (required)Plan approval boundary. Multiple polygons are handled via summed area.
Plan NufusuDouble— (required, min 1.0)Total plan population.
EK2 Referans Katmani (GeoJSON/CSV)Vector (any)— (required)EK-2 reference table. Must include fields: grup_id, grup_Ad, gosterge_id, gosterge_Ad, fonk_id, fonk_ad, min_area_per_unit_calculation_type, and three population-tier m2/person columns.
Output FieldTypeDescription
grup_idStringFacility group identifier from EK-2 reference.
grup_adStringFacility group name (e.g. EGITIM TESISLERI ALANI).
gosterge_idStringIndicator identifier from EK-2 reference.
gosterge_adStringIndicator name.
fonk_idStringFunction identifier from EK-2 reference.
fonk_adStringFunction name (standardised to upper case).
ek2_m2pkisiDoubleEK-2 standard threshold: minimum m2/person for the applicable population tier.
gercek_m2pkisiDoubleActual m2/person computed from plan data.
fark_m2pkisiDoubleDifference (actual − standard). Positive values indicate surplus; negative values indicate shortfall.
ek2_alanDoubleEK-2 benchmark area: population x standard m2/person.
gercek_alanDoubleActual area allocated in the plan for this function.
fark_alanDoubleAbsolute area difference (actual − benchmark).
alan_oraniDoublePercentage of total plan approval area occupied by this function.
adetIntNumber of polygon features belonging to this function.
yeterli_sayIntNumber of features meeting the standard. For area-constrained functions: 1 if sufficient, 0 if not. For no_constraint functions: equals adet.
yetersiz_sayIntNumber of features failing the standard (adet − yeterli_say).
m2pkisi_yeterlilikStringPer-capita sufficiency flag: Yeterli or Yetersiz.
alan_yeterlilikStringAbsolute area sufficiency flag: Yeterli or Yetersiz.

Interpretation Guidance

References

  1. Ersoy, M. (2012). Kentsel Planlama Ansiklopedik Sozluk. Ninova Yayincilik. DOI: 10.14527/9786053185034
  2. Turk, S. S. (2008). An analysis of the Turkish land readjustment system. Habitat International, 32(3), 363–379. DOI: 10.1016/j.habitatint.2007.11.007
  3. Guler, M. & Turk, S. S. (2015). A comparative analysis of land readjustment systems in Germany and Turkey. Survey Review, 47(343), 278–289. DOI: 10.1179/1752270615Y.0000000010
  4. Turkoglu, H. (2010). Planning standards in Turkey: the gap between legislation and implementation. ITU A/Z Journal of the Faculty of Architecture, 7(2), 23–38. DOI: 10.5505/itujfa.2010.24085
  5. Harvey, D. (2009). Social Justice and the City (Revised ed.). University of Georgia Press. DOI: 10.1353/book13205
  6. Van den Berg, L., Braun, E., & Otgaar, A. H. J. (2017). City and Enterprise: Corporate Community Involvement in European and US Cities. Routledge. DOI: 10.4324/9781315260211
  7. Steiniger, S. & Hay, G. J. (2009). Free and open source GIS for urban and regional planning. Computers, Environment and Urban Systems, 33(4), 241–251. DOI: 10.1016/j.compenvurbsys.2009.01.005
  8. Aydinoglu, A. C., Yomralioglu, T., & Inan, H. I. (2016). Developing a national GIS data standard for Turkey. Survey Review, 48(351), 377–388. DOI: 10.1080/00396265.2015.1124511

Algorithm 8: DOP Elite Analysis
Duzenleme Ortaklik Payi (DOP) Elite Analizi (UIP)

QGIS Algorithm ID: 8_uip_duzenleme_ortaklik_payi  |  Group: UIP Plan Analiz Araclari  |  Input: Plan boundary + islands + master function list → Output: 7 outputs (5 vector + 2 tables + 1 HTML dashboard)

Theoretical Background

Article 18 of Law No. 3194 (Imar Kanunu, 1985) establishes the legal basis for the Duzenleme Ortaklik Payi (DOP, Regulation Partnership Share), a land readjustment mechanism whereby private landowners collectively contribute a portion of their land — free of charge — to the municipality for public infrastructure: roads, public squares, parks, car parks, children's playgrounds, green spaces, places of worship, police stations, and similar public facilities. The DOP rate is capped at a maximum of 45% of the landowner's parcel area (Turk, 2008; Yomralioglu & Nisanci, 2008).

This instrument is functionally equivalent to the land readjustment (Umlegung) systems of Germany and Japan, but with a distinctive Turkish legal framework that has been refined through successive amendments and Constitutional Court rulings (Guler & Turk, 2015). The DOP calculation must satisfy two principal constraints: (1) the DOP rate must not exceed 45%, and (2) under Article 9 of the same regulation, the share of open-green spaces (park, children's playground, public square, neighbourhood sports field) within the DOP allocation must constitute at least 75% of the total DOP area.

Algorithm 8 implements a comprehensive, category-driven DOP analysis using a master list of 241 standardised uip_fonksiyon entries drawn from the national spatial data specification. Islands are classified into three tiers:

The algorithm further supports optional phasing (etaplama) analysis through either user-provided polygon layers or automatic area-weighted k-means clustering with Voronoi-like gap snapping, enabling sub-regional DOP compliance assessment where plan implementation occurs in stages.

Mathematical Formulation

Land category decomposition of plan area:

\[ \text{PO} = H + O + K + \text{YOL} \]

where PO = Plan Onama Alani (total plan approval area), H = HARIC adalar (excluded), O = OZEL adalar (private, denominator only), K = KAMU DONATI (public facilities), and YOL = road space (all area not covered by any island).

Effective area (payda / denominator):

\[ A_{\text{etkin}} = \text{PO} - H \]

DOP area (pay / numerator):

\[ A_{\text{DOP}} = K + \text{YOL} \]

DOP rate:

\[ \text{DOP\%} = \frac{A_{\text{DOP}}}{A_{\text{etkin}}} \times 100 = \frac{K + \text{YOL}}{\text{PO} - H} \times 100 \]

Article 9 (open-green space) constraint: For the subset of KAMU features classified as open-green (AY = park, children's playground, square, neighbourhood sports field, recreation area, mesire, botanical park, millet bahcesi):

\[ \text{AY\%} = \frac{\sum_{i \in \text{AY}} A_i}{A_{\text{DOP}}} \times 100 \geq 75\% \]

Area-weighted k-means objective (automatic phasing): For \(N\) islands with centroids \(\mathbf{x}_i\), areas \(w_i\) (weighted by category: HARIC × 0.1, OZEL × 0.6, KAMU × 1.0), and \(k\) clusters with centres \(\mathbf{c}_j\), the weighted k-means++ objective minimises:

\[ \min_{\{c_j\}} \sum_{i=1}^{N} w_i \cdot \min_{j=1}^{k} \|\mathbf{x}_i - \mathbf{c}_j\|^2 \]

with a capacity penalty to balance total area across clusters: when cluster \(j\) exceeds 95% of target weight \(W_{\text{target}}/k\), a penalty factor of \(1 + 0.6 \cdot (\text{excess})^{1.5}\) is applied to the distance metric for that cluster.

Core Input Parameters
Plan Onama Siniri (Poligon)Plan approval boundary polygon(s).
UIP Plan Katmani (Fonksiyon Adalari)Plan island polygon layer. Each feature must carry a function name in the selected column.
Fonksiyon SutunuColumn name containing the land-use function label (default: uipfonksiyon).
Category Selection (master list of 241 entries)
HARIC Fonksiyonlar (Excluded)Checkbox list. Default: planning boundaries, administrative boundaries, building restriction corridors, disaster-prone areas, protected areas, special-law areas, existing land use (agriculture/forest/olive groves).
OZEL Fonksiyonlar (Private, denominator only)Checkbox list. Default: residential, tourism, commercial/industrial from Kentsel Calisma, all "OZEL ..." prefixed (private health/education/social/sports).
KAMU DONATI (Public, numerator + denominator)Checkbox list. Default: open-green, education/health/social (non-private), worship, transport, energy, water-wastewater, municipal service functions.
ACIK-YESIL Alt Kumesi
(Article 9, 75% control)		Checkbox list. Default: park, children's playground, square, neighbourhood sports field, botanic park, mesire, recreation, millet bahcesi.
DIGER varsayimFallback for islands whose function does not match any checkbox selection: KAMU (default), OZEL, or HARIC.
Additional Parameters
Plan Nufusu (kisi)Double, default 10000.0, min 1.0
Etaplama / DOP Alt Bolge Katmani (Poligon, OPSIYONEL)Vector Polygon, optional. User-provided phasing polygons.
Otomatik Etaplama SayisiInteger, default 4, range 1–20. Number of auto-generated sub-regions when no user layer is provided.
Ideal DOP Orani (%)Double, default 45.0, range 10–80. Target DOP rate for compliance assessment.
HTML Rapor ModuEnum: CDN (online, ~50 KB) or INLINE (offline, ~3.5 MB with embedded Plotly + Leaflet).
OutputTypeDescription
1. DOP Esas AlanlarVector PolygonKAMU islands (contribute to DOP numerator). Fields: ada_id, uip_fonksiyon, etap_id, alan_m2, m2_per_kisi, yuzde_etap, kategori, dop_dahil, acik_yesil.
2. DOP Disi Ozel AlanlarVector PolygonOZEL + HARIC islands (do not contribute to DOP numerator). Same schema as output 1.
3. Yol ve Kamu AlanlariVector PolygonPer-etap road, KAMU, OZEL, and HARIC geometries with etap_id, alan_m2, and tip (YOL / KAMU / OZEL / HARIC).
4. Etaplama Alt BolgeleriVector PolygonPhasing sub-region polygons with 17 metric fields (alan, haric, etkin, ozel, kamu, yol, dop, dop_orani, hedef_sapma, durum, acik_yesil_orani, madde9, and counts).
5. Fonksiyon x Etap TablosuVector (no-geom)Function-by-etap cross-tabulation: fonksiyon, etap_id, alan_m2, ada_sayisi, m2_per_kisi, yuzde_etap_alan, kategori, dop_dahil.
6. DOP Oran Ozet TablosuVector (no-geom)Global + per-etap summary: kapsam, etap_id, 12 metric columns (alan, haric, etkin, ozel, kamu, yol, dop, dop_orani, ideal_dop, sapma, durum, acik_yesil, madde9).
7. HTML Dashboard RaporHTML fileInteractive dashboard with 11 Plotly charts across 6 tabs (Overview, Phasing, Map with Leaflet choropleth, Function Distribution, Recommendations, User Guide), gauge indicators, KPI cards, and automated deficiency recommendations.
Automatic phasing algorithm: When no user phasing layer is provided and etap_sayisi > 1, the tool:
  1. Extracts island centroids with category-weighted areas (HARIC × 0.1 to minimise their influence on phasing boundaries; OZEL × 0.6; KAMU × 1.0).
  2. Runs area-weighted k-means++ (custom LCG-based deterministic random number generator, seed=42) with cluster capacity balancing, producing a Voronoi-like partition.
  3. Assigns interstitial space (gap areas between islands) to the nearest cluster centroid.
  4. Snaps all island boundaries to their assigned cluster, ensuring no island is split across phase boundaries.

Interpretation Guidance

References

  1. Turk, S. S. (2008). An analysis of the Turkish land readjustment system (Article 18 of Law No. 3194). Habitat International, 32(3), 363–379. DOI: 10.1016/j.habitatint.2007.11.007
  2. Yomralioglu, T. & Nisanci, R. (2008). Land readjustment implementations in Turkey. XXI FIG Congress Proceedings, Stockholm. DOI: 10.13140/RG.2.1.2610.0889
  3. Guler, M. & Turk, S. S. (2015). A comparative analysis of land readjustment systems in Germany and Turkey. Survey Review, 47(343), 278–289. DOI: 10.1179/1752270615Y.0000000010
  4. Ersoy, M. (2012). Kentsel Planlama Ansiklopedik Sozluk. Ninova Yayincilik. DOI: 10.14527/9786053185034
  5. Arthur, D. & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035. DOI: 10.1145/1283383.1283494
  6. Demetriou, D., Stillwell, J., & See, L. (2012). Land consolidation in Cyprus: why is an integrated planning and decision support system required? Land Use Policy, 29(1), 131–142. DOI: 10.1016/j.landusepol.2011.05.012
  7. Aydinoglu, A. C., Yomralioglu, T., & Inan, H. I. (2016). Developing a national GIS data standard for Turkey: TUCBS. Survey Review, 48(351), 377–388. DOI: 10.1080/00396265.2015.1124511
  8. Turkoglu, H. (2010). Planning standards in Turkey: the gap between legislation and implementation. ITU A/Z Journal of the Faculty of Architecture, 7(2), 23–38. DOI: 10.5505/itujfa.2010.24085

planX — UIP Arac Seti • Academic Reference Manual v1.0 • August 2026
Developer: Ars. Gor. Yusuf Eminoglu • Dokuz Eylul Universitesi, Sehir ve Bolge Planlama Bolumu
Toolset: 8 QGIS processing algorithms for the Turkish 1/1000 scale Implementation Plan (UIP) workflow
License: GPL-3.0 • QGIS Plugin Repository: planX — UIP Toolset