Digitalization and Spatial Simulation in Urban Management: Land-Use Change Model for Industrial Heritage Conservation
Abstract
:1. Introduction
- Historical study of the neighborhood and identification of the main milestones behind land-use change that affect heritage conservation.
- Creation of digital maps of the Bellavista neighborhood, categorized by lots and for different years (1970, 1992 and 2019).
- Exploratory analysis of the changes between the said periods.
- Spatio-temporal prediction model to identify the drivers of change, and particularly their effect on the loss or conservation of the neighborhood’s historical fabric.
- Projection of the neighborhood’s future land uses through the model to identify the change trends in the coming years.
2. Methodology
2.1. Study Area
- 2008—definition of a Historical Conservation Area (HCA, in Spanish) (Figure 1) in the new Local Plan and some buildings that were built by the textile factory as Historic Conservation Buildings (ICH, within Spanish).
- 2009—on the coastal edge, the Municipality together with the Ministry of Housing and Urbanism (MINVU, in Spanish) define an urban renewal plan that modifies use from industrial to high-rise residential. After the 2010 earthquake—and ensuing housing shortage—this land-use change facilitated the Municipal approval of a multi-story residential real estate development, just a few meters from the HCA.
- 2017—after a highly confrontational process, the Factory was designated as a Historical Monument (HM). Several local conservation organizations and other regional actors supported its designation, but the property’s owners and tenants opposed invoking the unconstitutionality of the National Monuments Law. The designation would be signed after a long process of conflict.
- The residential zone located north of the estuary encompasses the HCA that includes several sets of workers’ housing, following a typical industrial pattern [33,35], and the provision of different equipment (health, education, recreational, basic supplies, etc.). The houses were linked to the factory date from 1905 to 1960.
- The historic factory zone, today an HM, is located to the south of the estuary. Here, the main facilities of the Bellavista de Tomé factory (FBOT) are found, which today are practically abandoned.
- The third zone, part of which was defined as an urban renewal zone, is along the coastal edge. Historically, its northern section was occupied by warehouses and manufacturing facilities, and the south by the houses of the company’s bosses. Currently, just the latter remain, although significant architectural alterations have been made as a result of the incorporation of land uses associated with tourism.
2.2. Phase 1: Digital Mapping
2.3. Phase 2: Exploratory Analysis of Land Cover/Use Change
2.4. Phase 3: Land Cover/Use Change Model
2.4.1. Factors
2.4.2. Steps for Generating a Land-Use Model
- The association of close distance to the beach with the change from “Historical Use” to “New Housing”.
- The association of medium distance to the beach with the change from “Historical Use” to “New Housing”.
- The association of far distance to the beach with the change from “Historical Use” to “New Housing”.
- The association of close distance to the beach with the change from “Historical Use” to “Commercial Use”.
- The association of medium distance to the beach with the change from “Historical Use” to “Commercial Use”.
- The association of far distance to the beach with the change from “Historical Use” to “Commercial Use”.
- There is a complete association between variables.
- There is a strong association between variables.
- There is a moderate association between variables.
- There is minimal and very poor association between variables.
- There is no association between variables
3. Results
3.1. Digital Mapping
- For the 1992 map, aerial photography of that year from the Chilean Military Geographical Institute was used, as well as theses and press archives, especially to know when some historical equipment had stopped working (Figure 5).
- For the 2019 map, the aerial photo interpretation technique was applied. Photos were obtained from the drone flight made in 2019, along with Street View and field verification (Figure 5).
3.2. Exploratory Analysis of Geospatial Data
3.3. Simulation Model of Land-Use Change for New Housing Use between 1992 and 2019
- Increase: defined as the increase in the graphical interface or X-axis (pixel size). Here, an increase of 1 pixel (1 m) has been chosen.
- Minimum and maximum delta: intervals for the number of pixels with the possibility of change being evaluated; 1 was set as a minimum delta and 500 as a maximum (considering an estimation of the maximum lot length).
- Tolerance angle: the curve’s breaking point. This has a degree of 5.
3.3.1. Study of the Influence of Variables on Land-Use Changes
3.3.2. Correlation Analysis and Map Probability
3.3.3. Projecting Future Scenarios
3.4. Discussion
4. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Heritage Tourism Market Size, Share & Trends Analysis Report. 2022. Available online: https://www.grandviewresearch.com/industry-analysis/heritage-tourism-market-report (accessed on 6 July 2022).
- Hernández, J.; Moyano, A. Patrimonio Cultural, Movimientos sociales y Construcción de la Identidad en Andalucía, Memoria Proyecto de Investigación 2006, Centro de Estudios Andaluces Patrimonio Cultural, Movimientos Sociales y Construcción de la Identidad, 2007, 52. Available online: https://centrodeestudiosandaluces.es/datos/paginas/proyectos06/ATL063.pdf (accessed on 6 June 2022).
- Maillard, C. Construcción social del patrimonio. In Hecho en Chile. Reflexiones en Torno al Patrimonio Cultural; Marsal, E.D., Ed.; Consejo Nacional de la Cultura y las Artes: Santiago, Chile, 2012; pp. 15–33. [Google Scholar]
- Berger, S.; Wicke, C. Introduction: Deindustrialization, Heritage, and Representations of Identity. Public Hist. 2017, 39, 10–20. [Google Scholar] [CrossRef]
- Cole, D. Exploring the sustainability of mining heritage tourism. J. Sustain. Tour. 2004, 12, 480–494. [Google Scholar] [CrossRef]
- Homobono, J. Del patrimonio cultural al industrial: Una mirada socioantropológica. In Patrimonios Culturales: Educación e Interpretación. Cruzando líMites y Produciendo Alternativas; Pereiro, E.X., Prado, S., Takenaca, H., Eds.; Ankulegi: San Sebastian, España, 2008; pp. 57–74. Available online: https://www.researchgate.net/publication/46389052_Del_patrimonio_cultural_al_industrial_Una_mirada_socioantropologica (accessed on 8 July 2022).
- UNESCO World Heritage Centre. The UNESCO Recommendation on the Historic Urban Landscape, Report of the Second Consultation on its Implementation by Member States; UNESCO World Heritage Centre: Paris, France, 2019. [Google Scholar]
- Ripp, M.; Rodwell, D. The geography of urban heritage. Hist. Environ. Policy Pract. 2015, 6, 240–276. [Google Scholar] [CrossRef]
- Capel, H. La Morfología de las Ciudades, Cap. III Agentes urbanos y Mercado Inmobiliario. Ediciones Del Serbal, Barcelona, España 2013. p. 461. Available online: https://www.scribd.com/document/484306437/Capel-Horacio-La-morfologi-a-de-las-ciudades-III-Agentes-urbanos-y-mercado-inmobiliario (accessed on 15 June 2022).
- Whitehand, J.W.R. British urban morphology: The Conzenion tradition. Urban Morphol. 2001, 5, 103–109. [Google Scholar] [CrossRef]
- Nikologianni, A.; Moore, K.; Larkham, P.J. Making sustainable regional design strategies successful. Sustainability 2019, 11, 1024. [Google Scholar] [CrossRef]
- Abdullahi, S.; Pradhan, B.; Mansor, S.; Shariff, A.R.M. GIS-based modeling for the spatial measurement and evaluation of mixed land use development for a compact city. GIScience Remote Sens. 2015, 52, 18–39. [Google Scholar] [CrossRef]
- Zhang, D.; Liu, X.; Wu, X.; Yao, Y.; Wu, X.; Chen, Y. Multiple intra-urban land use simulations and driving factors analysis: A case study in Huicheng, China. GIScience Remote Sens. 2019, 56, 282–308. [Google Scholar] [CrossRef]
- Wu, R.; Wang, J.; Zhang, D.; Wang, S. Identifying different types of urban land use dynamics using Point-of-interest (POI) and Random Forest algorithm: The case of Huizhou, China. Cities 2021, 114, 103202. [Google Scholar] [CrossRef]
- França, D.G.M.; Lotte, R.G.; de Almeida, C.M.; Siani, S.M.O.; Körting, T.S.; Fonseca, L.G.M.; da Silva, L.T. Object-based image analysis for urban land cover classification in the city of Campinas-SP, Brazil. In 2015 Joint Urban Remote Sensing Event; IEEE: Piscataway, NJ, USA, 2015; pp. 1–4. [Google Scholar]
- Naikoo, M.W.; Rihan, M.; Shahfahad; Peer, A.H.; Talukdar, S.; Mallick, J.; Ishtiaq, M.; Rahman, A. Analysis of peri-urban land use/land cover change and its drivers using geospatial techniques and geographically weighted regression. Environ. Sci. Pollut. Res. 2023, 30, 116421–116439. [Google Scholar] [CrossRef]
- Azari, M.; Tayyebi, A.; Helbich, M.; Reveshty, M.A. Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: Application to Maragheh, Iran. GIScience Remote Sens. 2016, 53, 183–205. [Google Scholar] [CrossRef]
- Chen, Y.; Li, X.; Liu, X.; Ai, B. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. Int. J. Geogr. Inf. Sci. 2014, 28, 234–255. [Google Scholar] [CrossRef]
- He, J.; Huang, J.; Li, C. The evaluation for the impact of land use change on habitat quality: A joint contribution of cellular automata scenario simulation and habitat quality assessment model. Ecol. Modell. 2017, 366, 58–67. [Google Scholar] [CrossRef]
- Jokar Arsanjani, J.; Helbich, M.; Kainz, W.; Boloorani, A.D. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int. J. Appl. Earth Obs. Geoinf. 2013, 21, 265–275. [Google Scholar] [CrossRef]
- Lagarias, A. Urban sprawl simulation linking macro-scale processes to micro-dynamics through cellular automata, an application in Thessaloniki, Greece. Appl. Geogr. 2012, 34, 146–160. [Google Scholar] [CrossRef]
- Ma, J.; Hipel, K.W.; Hanson, M.L.; Cai, X.; Liu, Y. An analysis of influencing factors on municipal solid waste source-separated collection behavior in Guilin, China by Using the Theory of Planned Behavior. Sustain. Cities Soc. 2018, 37, 336–343. [Google Scholar] [CrossRef]
- Al-shalabi, M.; Billa, L.; Pradhan, B.; Mansor, S.; Al-Sharif, A.A.A. Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: The case of Sana’a metropolitan city, Yemen. Environ. Earth Sci. 2013, 70, 425–437. [Google Scholar] [CrossRef]
- Morshed, S.R.; Fattah, M.A.; Hoque, M.M.; Islam, M.R.; Sultana, F.; Fatema, K.; Rabbi, M.F.; Rimi, A.A.; Sami, F.Y.; Rezvi Amin, F.M. Simulating future intra-urban land use patterns of a developing city: A case study of Jashore, Bangladesh. GeoJournal 2023, 88, 425–448. [Google Scholar] [CrossRef]
- Simwanda, M.; Murayama, Y.; Phiri, D.; Nyirenda, V.R.; Ranagalage, M. Simulating scenarios of future intra-urban land-use expansion based on the neural network–Markov model: A case study of Lusaka, Zambia. Remote Sens. 2021, 13, 942. [Google Scholar] [CrossRef]
- Mitsova, D.; Shuster, W.; Wang, X. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landsc. Urban Plan. 2011, 99, 141–153. [Google Scholar] [CrossRef]
- Cao, J.; Li, T. Analysis of spatiotemporal changes in cultural heritage protected cities and their influencing factors: Evidence from China. Ecol. Indic. 2023, 151, 110327. [Google Scholar] [CrossRef]
- Meles, T.H.; Mekonnen, A.; Beyene, A.D.; Hassen, S.; Pattanayak, S.K.; Sebsibie, S.; Klug, T.; Jeuland, M. Households’ valuation of power outages in major cities of Ethiopia: An application of stated preference methods. Energy Econ. 2021, 102, 105527. [Google Scholar] [CrossRef]
- Jokar Arsanjani, J.; Helbich, M.; Bakillah, M.; Hagenauer, J.; Zipf, A. Toward mapping land-use patterns from volunteered geographic information. Int. J. Geogr. Inf. Sci. 2013, 27, 2264–2278. [Google Scholar] [CrossRef]
- de Almeida, C.M.; Gleriani, J.M.; Castejon, E.F.; Soares-Filho, B.S. Using neural networks and cellular automata for modelling intra-urban land-use dynamics. Int. J. Geogr. Inf. Sci. 2008, 22, 943–963. [Google Scholar] [CrossRef]
- Mas, J.F.; Pérez-Vega, A.; Clarke, K.C. Assessing simulated land use/cover maps using similarity and fragmentation indices. Ecol. Complex. 2012, 11, 38–45. [Google Scholar] [CrossRef]
- Shahfahad; Naikoo, M.W.; Das, T.; Talukdar, S.; Asgher, M.S.; Asif; Rahman, A. Prediction of land use changes at a metropolitan city using integrated cellular automata: Past and future. Geol. Ecol. Landsc. 2022, 1–19. [Google Scholar] [CrossRef]
- Brito, A.; Cerda, G.; Fuentes, P.; Pérez, L.; Ambrosetti, D.; Barría, T.; Becerra, M.; Bustos, A.; Cvitanic, D.; Figueroa, N.; et al. Industria y Habitar Colectivo. Conjuntos habitacionales en el sur de Chile; Editorial STOQ: Concepción, Chile, 2018; p. 176. [Google Scholar]
- Montory, A.C.; Luppi S.M., R.; López, T.L. Bellavista Oveja Tomé. Una Fábrica en el Tiempo; Ediciones Universidad de San Sebastián: Concepción, Chile, 2012; Available online: https://www.researchgate.net/publication/338717831_Bellavista_Oveja_Tome_Una_fabrica_en_el_tiempo (accessed on 6 June 2020).
- Herrera Ojeda, R.; Lopez Meza, M.I.; Morajes Ortiz, M.F. Procesos contemporáneos de activación patrimonial: Tensiones, disputas y consensos entre las comunidades, El caso de Bellavista en Tomé, Chile. Atenea 2021, 524, 195–217. [Google Scholar] [CrossRef]
- Matus Madrid, C.; Zúñiga-Becerra, P.; Pérez-Bustamante, L. Patrimonialización de sitios industriales textiles: Más de una década de puesta en valor por las comunidades de Tomé. Sophia Austral 2019, 23, 235–256. [Google Scholar] [CrossRef]
- Hübscher, M.; Ringel, J. Opaque Urban Planning. The Megaproject Santa Cruz Verde 2030 Seen from the Local Perspective (Tenerife, Spain). Urban Sci. 2021, 5, 32. [Google Scholar] [CrossRef]
- Lange, D.; Wang, D.; ‘Mark’ Zhuang, Z.; Fontana, W. Brownfield development selection using multiattribute decision making. J. Urban Plan. Dev. 2014, 140, 04013009. [Google Scholar] [CrossRef]
- Agterberg, F.P.; Bonham-Carter, G.F. Deriving weights of evidence from geoscience contour maps for the prediction of discrete events. In Proceedings of the 22nd APCOM Symposium, Berlin, Germany, 17–21 September 1990; Volume 2, pp. 381–395. [Google Scholar]
- Bonham-Carter, G. Geographic Information Systems for Geoscientists: Modelling with GIS; Elsevier: Amsterdam, The Netherlands, 1994. [Google Scholar]
- Piontekowski, V.; da Silva, S.; Mendoza, E.; de Souza Costa, W.; Ribeiro, F.; Ribeiro, C. Modelagem do Desmatamento para o Estado do Acre Utilizando o Programa DinamicaEGO. Embrapa Informática Agropecuária/INPE, Bonito, Brazil 2012. pp. 1064–1075. Available online: https://www.geopantanal.cnptia.embrapa.br/2012/cd/p183.pdf (accessed on 6 June 2020).
- Godoy, M.; Soares-Filho, B.S. Modelling intra-urban dynamics in the Savassi neighbourhood, Belo Horizonte city, Brazil. In Modelling Environmental Dynamics; Springer: Berlin/Heidelberg, Germany, 2008; pp. 319–338. [Google Scholar]
Approach | Article | Model | Scale | Resolution |
---|---|---|---|---|
Land-Use Change | [12] | WoE-MCDA | Region | 5 m |
[13] | RF-CA | Intra-urban | 10 m | |
[14] | RF-CA | Intra-urban | 30 m | |
[15] | ANN-CA | Intra-urban | 50 m | |
[16] | RF-MCDA-Fuzzy | Region | 30 m | |
[17] | CA-ANN-Fuzzy | Region | 30 m | |
[18] | Patch-LR-CA | Region | 30 m | |
Urban Growth | [19] | AIS-CA | Region | 30 m |
[20] | LR-MC-CA | Region | 30 m | |
[21] | LR-CA | Region | 25 m | |
[22] | GRADIANTE-CA | National | 100 m | |
Both | [23] | CA-Sleuth | Region | 45 m |
[24] | MLC-MC-CA-MPNNMC | Intra-urban | 10 m | |
[25] | MC-CA | Intra-urban | 30 m | |
[26] | MC-CA | Region | 10 m |
1992 | |||||
---|---|---|---|---|---|
1970 | NUV | SE | NUE | NUEC | Total |
HV | 0.03% | 0.02% | 0.02% | 0.00% | 0.07% |
HEAI | 1.36% | 1.12% | 0.00% | 0.22% | 2.70% |
HE | 0.00% | 0.00% | 1.40% | 0.00% | 1.40% |
Change of Use | Beach Distance Variable | Historic Circulation Distance Variable | Historical Monument (HM) | Historical Conservation Area (HCA) | Coastal Edge Section Plan (CES) |
---|---|---|---|---|---|
HV (1) to NUV (10) | 0 to 24 mts decreases the probability of change; from 24 to 200 mts slightly increases the probability of change; from 200 the probability of change begins to decrease | 0 to 17.4 mts decreases the probability of change; from 17.4 to 29 slightly increases the probability of change; from 29 greatly increases | It is not associated (there is no evidence that it increases or decreases) | Not protected increases slightly (0.65); protected decreases probability (−3.54) | Not associated |
HE (2) to NUV (10) | 0 to 66 increases the probability of change; 66 to 594 decreases the probability of change; from 594 increases the probability of change | 0 to 22 is not associated; 22 to 47 decreases; from 47 the probability of change increases | Not associated | Not protected probability of change increases by 3.73; the probability of change decreases by −17.35 | Non-urban renewal decreases probability (−1.18), urban renewal increases the probability of change (17.01) |
HEAI (3) to NUV (10) | 0 to 130 mts decreases the probability of change; 608 to 666 there is a striking increase in the probability | 0 to 6 mts decreases the probability; 6 to 56 slightly increases the probability of change; from 56, it decreases | Not associated | Not protected not associated; protected increases the probability of change (1.9) | Not associated |
HP (4) to NUV (10) | 0 to 67 increases the probability of change; from 67 decreases the probability of change | 0 to 49 slightly increases the probability of change; from 49 the probability of change begins to decrease | Unprotected increases probability (5.0), protected decreases probability (weight −19) | Not associated | No urban renewal repels (−19), urban renewal favors change (+19) |
SE (11) to NUV (10) | From 0 to 350, it increases; 350 to 383, it decreases; 383 to 422, it increases slightly; from 422 to 509 it decreases; 509 to 559: from there it decreases | From 0 to 50 it increases very slightly and then decreases very slightly; from 50, the probability of change decreases | Not associated | Unprotected decreases (−0.1); protected favors increases (1.13) | Not associated |
NUE (12) to NUV (10) | Up to 381, the probability of change increases; from 381 to 541, it decreases; from 541, the probability increases | It is not associated until 49; from there the probability begins to increase | Not associated | Unprotected increases probability (14.82); protected decreases (−0.21) | Not associated |
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González-Albornoz, P.; López, M.I.; Carmona, P.; Rubio-Manzano, C. Digitalization and Spatial Simulation in Urban Management: Land-Use Change Model for Industrial Heritage Conservation. Appl. Sci. 2024, 14, 7221. https://doi.org/10.3390/app14167221
González-Albornoz P, López MI, Carmona P, Rubio-Manzano C. Digitalization and Spatial Simulation in Urban Management: Land-Use Change Model for Industrial Heritage Conservation. Applied Sciences. 2024; 14(16):7221. https://doi.org/10.3390/app14167221
Chicago/Turabian StyleGonzález-Albornoz, Pablo, María Isabel López, Paulina Carmona, and Clemente Rubio-Manzano. 2024. "Digitalization and Spatial Simulation in Urban Management: Land-Use Change Model for Industrial Heritage Conservation" Applied Sciences 14, no. 16: 7221. https://doi.org/10.3390/app14167221