# A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System

^{*}

## Abstract

**:**

## 1. Introduction

^{2}and showed the true status of the territory, thus becoming an effective tool for the representation, analysis, comparison, and evolutionary studies of València and its immediate surroundings [3].

## 2. Materials and Methods

#### 2.1. Two-Dimensional Coordinate Transformations

#### 2.1.1. Similarity Transformation

#### 2.1.2. Affine Transformation

#### 2.1.3. Bilinear Transformation

#### 2.1.4. Polynomial Transformation

#### 2.2. Least Squares Adjustment and Akaike Information Criterion

#### 2.2.1. Least Squares Adjustment

#### 2.2.2. The Akaike Information Criterion

_{C}) should be used instead of the regular AIC as proposed by different authors [15,26,30,31]. The alternative version AIC

_{C}means AIC with a correction for small sets of observations.

_{C}is defined as [31]

#### 2.3. Creating a Tile-Based Geospatial System

## 3. Results

#### 3.1. Results of the Transformation from Pixel Reference System to 1929 Reference System

#### 3.1.1. Data Points

#### 3.1.2. Fitting Models

#### 3.1.3. Checking the Selected Model

#### 3.2. Results of the Transformation from 1929 Reference System to UTM-ETRS89 Reference System

#### 3.2.1. Data Points

#### 3.2.2. Fitting Models

#### 3.2.3. Checking the Selected Model

#### 3.3. Using a Tile Map Service (TMS)

## 4. Discussion

^{2}. This large area suggests the use of local transformations in the 1929 to UTM-ETRS89 stage rather than a global transformation to reduce transformation residuals. While this approach sounds interesting to explore, our test on sheet 54II using a global transformation gave satisfactory results in terms of accuracy and image quality

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Table A1.**Affine transformation parameter value p, standard error $\sigma $, t value ($\mathrm{t}=\frac{\mathrm{P}}{\mathsf{\sigma}}$), probability and significance codes.

Parameter | Parameter Value p | $\mathsf{\sigma}$ | t | Pr(>|t|) | Signific Code ^{1} |
---|---|---|---|---|---|

${\mathrm{a}}_{0}$ | 24,865.338377459 | 1.086 × 10^{−1} | 2.28 × 10^{5} | <2 × 10^{−16} | *** |

${\mathrm{a}}_{1}$ | 0.0851460419891 | 3.689 × 10^{−5} | 2308.16 | 2.11 × 10^{−13} | *** |

${\mathrm{a}}_{2}$ | 0.0004141569379 | 2.249 × 10^{−5} | 18.41 | 5.12 × 10^{−5} | *** |

${\mathrm{b}}_{0}$ | 35,033.426316412 | 1.108 × 10^{−1} | 3.161 × 10^{5} | <2 × 10^{−16} | *** |

${\mathrm{b}}_{1}$ | 0.0004270659313 | 3.763 × 10^{−5} | 11.3 | 3.44 × 10^{−4} | *** |

${\mathrm{b}}_{2}$ | −0.08476298350885 | 2.294 × 10^{−5} | −3.69 × 10^{3} | 3.22 × 10^{−14} | *** |

^{1}The significance codes of the linear model parameter are 0 =

*******; 0.001 =

******; 0.01 =

*****.

**Table A2.**Bilinear transformation parameter value p standard error $\sigma $, t value ($\mathrm{t}=\frac{\mathrm{P}}{\mathsf{\sigma}}$), probability and significance codes.

Parameter | Parameter Value p | $\mathsf{\sigma}$ | t | Pr(>|t|) | Signific Code ^{1} |
---|---|---|---|---|---|

${\mathrm{a}}_{0}$ | 24,865.233705395 | 7.958 × 10^{−2} | 312,461.330 | <2 × 10^{−16} | *** |

${\mathrm{a}}_{1}$ | 0.0852476013224 | 4.512 × 10^{−5} | 1889.454 | 3.27 × 10^{−10} | *** |

${\mathrm{a}}_{2}$ | 0.0004870540513 | 3.115 × 10^{−5} | 15.637 | 0.000568 | *** |

${\mathrm{a}}_{3}$ | −0.00000006622813 | 2.515 × 10^{−8} | −2.633 | 0.078114 | .. |

${\mathrm{b}}_{0}$ | 35,033.486118114 | 1.305 × 10^{−1} | 268,360.940 | <2 × 10^{−16} | *** |

${\mathrm{b}}_{1}$ | 0.0003690426076 | 7.401 × 10^{−5} | 4.986 | 0.0155 | * |

${\mathrm{b}}_{2}$ | −0.08480463140732 | 5.110 × 10^{−5} | −1.659 × 10^{3} | 4.82 × 10^{−10} | *** |

${\mathrm{b}}_{3}$ | 0.0000000378377 | 4.126 × 10^{−8} | 0.917 | 0.4267 | . |

^{1}The significance codes of the linear model parameter are 0 =

*******; 0.01 =

*****; 0.05 =

**..**; 0.1 =

**.**

Residuals | R_{x} (m) | R_{y} (m) |
---|---|---|

R_{1} | 0.042 | 0.067 |

R_{2} | 0.115 | −0.088 |

R_{3} | −0.031 | −0.092 |

R_{4} | −0.063 | 0.088 |

R_{5} | 0.047 | −0.012 |

R_{6} | −0.058 | 0.023 |

R_{7} | −0.051 | 0.015 |

Residuals | R_{x} (m) | R_{y} (m) |
---|---|---|

R_{1} | 0.031 | 0.068 |

R_{2} | 0.029 | −0.039 |

R_{3} | −0.039 | −0.088 |

R_{4} | −0.064 | 0.089 |

R_{5} | 0.002 | 0.014 |

R_{6} | 0.021 | −0.023 |

R_{7} | 0.012 | −0.021 |

## Appendix B

**Table A5.**Affine transformation parameter value p, standard error $\sigma $, t value ($\mathrm{t}=\frac{\mathrm{P}}{\mathsf{\sigma}}$), probability and significance codes.

Parameter | Parameter Value p | $\mathsf{\sigma}$ | t | Pr(>|t|) | Signific Code ^{1} |
---|---|---|---|---|---|

${\mathrm{a}}_{0}$ | 0.9682348838144 | 9.585 × 10^{−6} | 505.01 × 10^{3} | 0 | *** |

${\mathrm{a}}_{1}$ | 0.9996937418504 | 5.521 × 10^{−5} | 54.32 × 10^{3} | 0 | *** |

${\mathrm{a}}_{2}$ | −0.02863532893969 | 8.926 × 10^{−5} | −961.229 | 7.03 × 10^{−12} | *** |

${\mathrm{b}}_{0}$ | 0.9918701185392 | 1.441 × 10^{−6} | 3.44 × 10^{6} | 0 | *** |

${\mathrm{b}}_{1}$ | 0.0287069419929 | 4.998 × 10^{−5} | 1.72 × 10^{3} | 6.80 × 10^{−13} | *** |

${\mathrm{b}}_{2}$ | 0.9997689179512 | 8.080 × 10^{−5} | 37.12 × 10^{3} | 0 | *** |

^{1}The significance codes of the linear model parameter are 0 =

*******; 0.001 =

******; 0.01 =

*****.

**Table A6.**Bilinear transformation parameter value p, standard error $\sigma $, t value ($\mathrm{t}=\frac{\mathrm{P}}{\mathsf{\sigma}}$), probability and significance codes.

Parameter | Parameter Value p | $\mathsf{\sigma}$ | t | Pr(>|t|) | Signific Code ^{1} |
---|---|---|---|---|---|

${\mathrm{a}}_{0}$ | 0.9682339291628 | 4.259 × 10^{−5} | 2.273 × 10^{4} | 1.87 × 10^{−13} | *** |

${\mathrm{a}}_{1}$ | 0.99971906885450 | 0.0011283 | 8.860 × 10^{2} | 3.17 × 10^{−9} | *** |

${\mathrm{a}}_{2}$ | −0.02861583497542 | 8.690 × 10^{−4} | −32.926 | 6.15 × 10^{−5} | *** |

${\mathrm{a}}_{3}$ | −0.00000000071164 | 3.170 × 10^{−8} | 0.4314 | 0.6953 | . |

${\mathrm{b}}_{0}$ | 0.99187279797093 | 6.217 × 10^{−6} | 1.595 × 10^{5} | 4.441 × 10^{−16} | *** |

${\mathrm{b}}_{1}$ | 0.02827912024804 | 9.919 × 10^{−4} | 28.5093 | 9.475 × 10^{−5} | *** |

${\mathrm{b}}_{2}$ | 0.99943949643420 | 7.640 × 10^{−4} | 1.308 × 10^{3} | 9.853 × 10^{−10} | *** |

${\mathrm{b}}_{3}$ | 0.00000001202562 | 2.787 × 10^{−8} | 0.4314 | 0.695 | . |

^{1}The significance codes of the linear model parameter are 0 =

*******; 0.001 =

******; 0.01 =

*****; 0.1 =

**.**

Residuals | R_{x} (m) | R_{y} (m) |
---|---|---|

R_{1} | 0.004 | 0.011 |

R_{2} | −0.002 | 0.007 |

R_{3} | −0.097 | 0.017 |

R_{4} | 0.022 | −0.021 |

R_{5} | 0.038 | 0.023 |

R_{6} | −0.016 | 0.051 |

R_{7} | 0.050 | −0.087 |

Residuals | R_{x} (m) | R_{y} (m) |
---|---|---|

R_{1} | −0.003 | −0.015 |

R_{2} | 0.001 | −0.001 |

R_{3} | 0.097 | −0.014 |

R_{4} | 0.023 | 0.032 |

R_{5} | −0.038 | −0.011 |

R_{6} | 0.016 | −0.063 |

R_{7} | −0.049 | 0.073 |

## References

- Azzari, M.; Marcaccini, P.; Pizziolo, G. A geographical information system in Tuscan coastal wetlands. In Actes du II Congres International sur la Science et la Technologie pour la Sauveguarde du Patrimoine Culturel dans les Pays du Bassin Méditerranéen; Elsevier: Amsterdam, The Netherlands, 1999. [Google Scholar]
- Oreni, D.; Brumana, F.; Scaioni, M.; Prandi, F. Navigating on the past, as a bird flight, in the territorial scale of historical topographic maps: WMS on the “Corografie delle Province del Regno Lombardo-Veneto”, for accessing cadastral map catalogue. e-Perimetron
**2010**, 5, 194–211. [Google Scholar] - Llopis Alonso, A.; Perdigón Fernández, L. Cartografía Histórica de la Ciudad de Valencia (1608–1944); Editorial Universitat Politècnica de València: València, Spain, 2016. [Google Scholar]
- Bitelli, G.; Gatta, C. Digital processing and 3D modelling of an 18th century scenographic map of Bologna. Adv. Cartogr. GISci.
**2011**, 2, 129–146. [Google Scholar] - Guarducci, A.; Rombai, L.; Piccardi, M. Mare Oraque Tusciae. e-Perimetron
**2011**, 6, 114–121. [Google Scholar] - Brovelli, M.A.; Gianluca, G.; Minghini, M.; Beretta, M. Web Geoservices and Ancient Cadastral Maps: The Web C.A.R.T.E. Project. Trans. GIS
**2012**, 16125–16142. [Google Scholar] [CrossRef] - Bitelli, G.; Cremonini, S.; Gatta, C. Cartographic heritage: Toward unconventional methods for quantitative analysis of pregeodetic maps. J. Cult. Herit.
**2014**, 15, 183–195. [Google Scholar] [CrossRef] - Cardesín Díaz, J.M.; Mirás Araujo, J. Historic Urbanization Process in Spain (1746–2013): From the Fall of the American Empire to the Real Estate Bubble. J. Urban Hist.
**2015**, 4333–4352. [Google Scholar] [CrossRef] - Sigalat Vaya, C. Estudio de los trazados de la ciudad de Valencia según el plano parcelario municipal realizado por el Instituto Cartográfico y Catastral en 1929. In Proceedings of the Actas Congreso Internacional de Rehabilitación del Patrimonio Arquitectónico y Edificación, Sevilla, Spain, 9–11 July 2008. [Google Scholar]
- Villar-Cano, M.; Marqués-Mateu, A.; Jiménez-Martínez, M.J. Triangulation network of 1929–1944 of the first 1:500 urban map of València. Surv. Rev.
**2019**. [Google Scholar] [CrossRef] - Unesco. Report by the Director-General on the Implementation of the Information for All Programme; UNESCO House: Paris, France, 2004. [Google Scholar]
- Canters, F. Small-Scale Map Projection Design; Taylor and Francis: London, NK, 2002. [Google Scholar]
- Kennie, T.J.M.; Petrie, G. Engineering Surveying Technology; Taylor and Francis: London, UK, 2010. [Google Scholar]
- Leick, A.; Rapoport, L.; Tatarnikov, D. GPS Satellite Surveying; Wiley: Hoboken, NJ, USA, 2015. [Google Scholar]
- Felus, Y.A.; Felus, M. On choosing the right coordinate transformation method. In Proceedings of the FIG Working Week, Eilat, Israel, 3–8 May 2009. [Google Scholar]
- Brimicombe, A.; Li, C. Location-Based Services and Geo-Information Engineering; Wiley: Hoboken, NJ, USA, 2015. [Google Scholar]
- Van Diggelen, F.; Abraham, C.; De Salas, J.; Silva, R. GNSS Inside Mobile Phones: GPS, GLONASS, QZSS, and SBAS in a Single Chip. Inside GNSS
**2011**, 6, 50–60. [Google Scholar] - Sample, J.T.; Ioup, E. Tile-Based Geospatial Information Systems. Principles and Practices; Springer: Berlin/Heidelberg, Germany, 2010. [Google Scholar]
- Chen, W.; Hill, C. Evaluation Procedure for Coordinate Transformation. J. Surv. Eng.
**2005**, 131, 43–49. [Google Scholar] [CrossRef] - Maling, D.H. Coordinate Systems and Map Projections; Elsevier: Amsterdam, The Netherlands; Pergamon Press: Oxford, UK, 1992. [Google Scholar]
- Iliffe, J.; Lott, R. Datums and Map Projections for Remote Sensing, GIS and Surveying; Whittles Publishing: Dunbeath, UK, 1992. [Google Scholar]
- Tóth, K.; Tomas, R.; Nunes de Lima, V.; Cetl, V. INSPIRE: Balancing Legal Obligations with Technical Aspects; Publications Office of the European Union: Brussels, Belgium, 2013. [Google Scholar]
- ISO 19157:2013: Geographic Information—Data Quality. Available online: https://www.iso.org/standard/32575.html (accessed on 1 January 2013).
- ASPRS Positional Accuracy Standards for Digital Geospatial Data. Edition 1, Version 1.0. Available online: https://www.asprs.org/news-resources/asprs-positional-accuracy-standards-for-digital-geospatial-data (accessed on 1 November 2014).
- Luhmann, T.; Robson, S.; Kyle, S. Close-Range Photogrammetry and 3D Imaging; Walter de Gruyter Gmbh: Berlin, Germany, 2014. [Google Scholar]
- Even-Tzur, G. Coordinate transformation with variable number of parameters. Surv. Rev.
**2018**. [Google Scholar] [CrossRef] - Strang, G.; Borre, K. Linear Algebra, Geodesy and GPS; Wellesley-Cambridge Press: Wellesley, MA, USA, 1997. [Google Scholar]
- Yuanxi, Y.; Tianhe, X. Combined method of datum transformation between different coordinates systems. Geo-Spat. Inf. Sci.
**2002**, 5, 5–9. [Google Scholar] [CrossRef] - Lehman, R. Transformation model selection by multiple hypotheses testing. J. Geod.
**2014**. [Google Scholar] [CrossRef] - Kresse, W.; Danko, D.M. Handbook of Geographic Information; Springer: Berlin/Heidelberg, Germany, 2012. [Google Scholar]
- Burnham, K.P.; Anderson, D.R. Model Selection and Multimodel Inference; Springer: Berlin/Heidelberg, Germany, 2002. [Google Scholar]

**Figure 2.**Grid frame corner of the map number 54 II. The X axis and the Y axis show the local coordinates, on this image from 24,900 to 25,100 m and from 35,000 to 34,900 m respectively.

**Figure 3.**Two representations of vertex 298 (

**a**) the original mark carved on the pavement; (

**b**) location of the vertex 298 on the 1929 map.

**Figure 4.**(

**a**) The global navigation satellite system (GNSS) antenna and receptor during the resurvey of the vertex 86A; (

**b**) the original mark of the vertex 86A.

**Figure 6.**A detail of the former Aragón Railway Station at zoom levels 17 (left) to 20 (right). Note the increasing detail of the map elements in upper levels.

**Figure 7.**Screenshot of the viewer combining the 1929 map together with OpenStreetMap and vector data. The viewer allows replacing OpenStreetMap layer with an orthoimage.

Point | X-Pixel | Y-Pixel | X-1929 (m) | Y-1929 (m) |
---|---|---|---|---|

1 | 989 | 990 | 24,950 | 34,950 |

2 | 3337 | 1000 | 25,150 | 34,950 |

3 | 987 | 1578 | 24,950 | 34,900 |

4 | 2749 | 1589 | 25,100 | 34,900 |

5 | 966 | 5708 | 24,950 | 34,550 |

6 | 2156 | 2765 | 25,050 | 34,800 |

7 | 2746 | 2178 | 25,100 | 34,850 |

**Table 2.**Number of parameters, significant parameters, residuals sum of parameters (RSS) and values of AIC and AICc for each model.

Model | Number of Parameters | Significant Parameters | $\mathbf{RSS}-\mathbf{X}\text{}\left({\mathit{m}}^{2}\right)$ | RSS-Y $\left({\mathit{m}}^{2}\right)$ | AIC X | AIC Y | AICc X | AICc Y |
---|---|---|---|---|---|---|---|---|

Affine | 6 | 6 | 0.028 | 0.029 | −12.785 | −12.539 | −11.029 | −10.783 |

Bilinear | 8 | 7 | 0.008 | 0.023 | −17.121 | −12.162 | −19.798 | −12.406 |

Polynomial | 12 | 5 | 0.0037 | 0.0008 | −18.938 | −29.832 | ---- | ---- |

**Table 3.**Transformed coordinates from pixel coordinates in bilinear model, grid coordinates, and differences in between.

Point | X-Pixel | Y-Pixel | X-Transf. (m) | Y-Transf. (m) | X-Grid (m) | Y-Grid (m) | $\mathsf{\Delta}\mathbf{X}\text{}\left(\mathbf{m}\right)$ | $\mathsf{\Delta}\mathbf{Y}\text{}\left(\mathbf{m}\right)$ |
---|---|---|---|---|---|---|---|---|

1 | 1555 | 5711 | 24,999.983 | 34,550.080 | 25,000 | 34,550 | −0.017 | 0.080 |

2 | 1576 | 991 | 24,999.959 | 34,950.089 | 25,000 | 34,950 | −0.041 | 0.089 |

3 | 2159 | 2176 | 25,050.029 | 34,849.929 | 25,050 | 34,850 | 0.029 | −0.071 |

4 | 4510 | 1006 | 25,249.890 | 34,950. 008 | 25,250 | 34,950 | −0.110 | 0.008 |

5 | 4510 | 1596 | 25,249.997 | 34,900.078 | 25,250 | 34,900 | −0.003 | 0.078 |

6 | 3921 | 2184 | 25,199.982 | 34,850.048 | 25,200 | 34,850 | −0.018 | 0.048 |

7 | 5684 | 2194 | 25,350.020 | 34,849.998 | 25,350 | 34,850 | 0.020 | −0.002 |

8 | 2165 | 405 | 25,049.930 | 34,999.976 | 25,050 | 35,000 | −0.070 | −0.024 |

**Table 4.**Transformed coordinates from pixel coordinates in affine model, grid coordinates, and differences in between.

Point | X-Pixel | Y-Pixel | X-Transf. (m) | Y-Transf. (m) | X-Grid (m) | Y-Grid (m) | $\mathsf{\Delta}\mathbf{X}\text{}\left(\mathbf{m}\right)$ | $\mathsf{\Delta}\mathbf{Y}$(m) |
---|---|---|---|---|---|---|---|---|

1 | 1555 | 5711 | 25,000.106 | 34,550.009 | 25,000 | 34,550 | 0.106 | 0.009 |

2 | 1576 | 991 | 24,999.939 | 34,950.099 | 25,000 | 34,950 | −0.061 | 0.099 |

3 | 2159 | 2176 | 25,050.070 | 34,849.904 | 25,050 | 34,850 | 0.070 | −0.096 |

4 | 4510 | 1006 | 25,249.763 | 34,950.008 | 25,250 | 34,950 | −0.236 | 0.008 |

5 | 4510 | 1596 | 25,250.008 | 34,900.071 | 25,250 | 34,900 | 0.008 | 0.071 |

6 | 3921 | 2184 | 25,200.101 | 34,849.978 | 25,200 | 34,850 | 0.101 | −0.022 |

7 | 5684 | 2194 | 25,350.217 | 34,849.884 | 25,350 | 34,850 | 0.217 | −0.116 |

8 | 2165 | 405 | 25,049.847 | 35,000.022 | 25,050 | 35,000 | −0.153 | 0.022 |

Point | X-1929 (m) | Y-1929 (m) | X-UTM-ETRS89 (m) | Y-UTM-ETRS89 (m) |
---|---|---|---|---|

Mislata | 20,310.3 | 35,452.4 | 722,137.615 | 4,372,684.928 |

Sancho | 27,377.67 | 31,860.15 | 729,305.691 | 4,369,296.395 |

Miguelete II | 23,915.43 | 35,480.6 | 725,740.934 | 4,372,816.608 |

Pechina | 22,514.06 | 35,662.39 | 724,334.668 | 4,372,958.164 |

298 | 24,453.03 | 35,745.74 | 726,270.642 | 4,373,097.113 |

Puente del Mar | 25,029.35 | 34,925.5 | 726,870.327 | 4,372,293.579 |

Puente del Mar II | 24,849.59 | 34,912.4 | 726,690.931 | 4,372,275.460 |

**Table 6.**Number of parameters, significant parameters, residuals sum of parameters (RSS), and values of AIC and AICc for each model.

Model | Number of Parameters | Significant Parameters | $\mathbf{RSS}-\mathbf{X}\text{}\left({\mathbf{m}}^{2}\right)$ | RSS-Y $\left({\mathbf{m}}^{2}\right)$ | AIC X | AIC Y | AICc X | AICc Y |
---|---|---|---|---|---|---|---|---|

Affine | 6 | 6 | 0.014 | 0.012 | −17.637 | −18.716 | −15.88 | −16.960 |

Bilinear | 8 | 6 | 0.014 | 0.011 | −15.637 | −17.325 | −1.881 | −3.6329 |

Polynomial | 12 | 4 | 0.239 | 1.41 | 10.231 | 22.685 | ---- | ---- |

Point | X-1929 (m) | Y-1929 (m) |
---|---|---|

67A | 24,736.67 | 35,580.69 |

86A | 25,008.74 | 35,060.06 |

299 | 24,542.65 | 35,672.22 |

299A | 24,530.53 | 35,701.03 |

Model | Vertex | X-UTM GNSS (m) | Y-UTM GNSS (m) | X-UTM Transf. (m) | Y-UTM Transf. (m) | $\mathsf{\Delta}\mathbf{X}$(m) | $\mathsf{\Delta}\mathbf{Y}$(m) |
---|---|---|---|---|---|---|---|

Affine | 67A | 726,510.529 | 4,372,928.407 | 726,510.601 | 4,372,928.482 | −0.070 | −0.069 |

Bilinear | 67A | 726,510.529 | 4,372,928.407 | 726,510.602 | 4,372,928.476 | −0.074 | −0.060 |

Affine | 86A | 726,845.851 | 4,372,427.521 | 726,845.854 | 4,372,427.567 | −0.003 | −0.046 |

Bilinear | 86A | 726,845.851 | 4,372,427.521 | 726,845.838 | 4,372,427.576 | −0.003 | −0.055 |

Affine | 299 | 726,362.333 | 4,373,026.171 | 726,362.378 | 4,373,026.206 | −0.045 | −0.035 |

Bilinear | 299 | 726,362.333 | 4,373,026.175 | 726,362.378 | 4,373,026.196 | −0.045 | −0.026 |

Affine | 299A | 726,349.437 | 4,373,054.591 | 726,349.465 | 4,373,054.661 | −0.028 | −0.070 |

Bilinear | 299A | 726,349.437 | 4,373,054.591 | 726,349.471 | 4,373,054.650 | −0.034 | −0.059 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Villar-Cano, M.; Jiménez-Martínez, M.J.; Marqués-Mateu, Á.
A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System. *ISPRS Int. J. Geo-Inf.* **2019**, *8*, 378.
https://doi.org/10.3390/ijgi8090378

**AMA Style**

Villar-Cano M, Jiménez-Martínez MJ, Marqués-Mateu Á.
A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System. *ISPRS International Journal of Geo-Information*. 2019; 8(9):378.
https://doi.org/10.3390/ijgi8090378

**Chicago/Turabian Style**

Villar-Cano, Miriam, María Jesús Jiménez-Martínez, and Ángel Marqués-Mateu.
2019. "A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System" *ISPRS International Journal of Geo-Information* 8, no. 9: 378.
https://doi.org/10.3390/ijgi8090378