Urban Land Suitability: Comparison
Please note this is a comparison between Version 2 by Vicky Zhou and Version 1 by Ashutosh Sharma.

Urban land suitability could be evaluated from the aspects such as the imbalance of the existing land-use structure and function distribution, along with the scarcity of land resources, so as to provide people with more a rational use of land service space.

  • geographic information system (GIS)
  • urban land planning
  • suitability evaluation
  • intercepting flood ditch

1. Introduction

The advancement in the economy and technological dependence has led to the increasing demand for urban residential area in various countries around the world. Various serious issues have been raised due to the rapid increase in the urbanization rate such as air pollution, imbalanced land-use structure, lack of traffic management, and many more [1][2]. This situation has raised the social economic burden on the management and environment for optimization of urban residential land, requiring the help of various urban planning agencies. This raises the need for a reliable and quantified urban residential environment for building a more improved understanding of the process of urbanization. In order to analyze this viewpoint, various factors should be kept in mind to understand the complete scenario of a land-use factor. Figure 1 depicts the land-use factors which generally affect the urban land distribution.
Sustainability 13 10521 g001
Figure 1. Land-use factors affecting the urban land distribution.
In recent years, the population has continued to grow, and cities have developed rapidly. The range of activities people need is constantly increasing. Therefore, people’s demand for urban land is becoming stronger and stronger. The random development of urban land resources does not only damage the urban ecosystem but also leads to uncoordinated land use due to the imbalance in land-use structure and its function distribution. This urbanization process also wastes precious land resources and leads to the low utilization rate of land [3]. Therefore, reasonable urban expansion is very important for the sustainable use of land. To better understand the laws of urban land use, the dynamic changes of urban land expansion are studied. It is of great significance to the scientific planning of land expansion and the sustainable development of land. GIS ideas and methods are comprehensively applied in this work based on the ideas of system theory and cybernetics, according to the different functions and characteristics of the city. Based on this scientific hypothesis, the appropriate mathematical models are selected to reflect these functions and characteristics. Achieving the quantification of the analysis process is key to embodying quantitative planning in the evaluation of urban land use [4]. Realizing the quantification, standardization, systemization, and information acquisition of urban land evaluation will become an important subject for scientifically evaluating the suitability of urban land. Based on the analysis and summary of the relevant theories of urban land suitability evaluation, the comprehensive evaluation method of urban land suitability based on GIS technology is explored [5]. There are several land suitability methods defined in the literature for assessing crops using the qualitative and quantitative approaches. Some of these approaches use Boolean algebra [6], weighted linear combination methods [7], and various multiple regression approaches [8] for analyzing the statistics. Among the various traditional approaches, the categorical data for the qualitative approach are depicted in Table 1.
Table 1. Descriptive analysis of various land suitability methods.
Research Crop Methods
Bagherzadeh and Gholizadeh [9] Alfalfa Artificial Neural Network (ANN)
Bagherzadeh et al. [10] Soyabean Fuzzy approach
Danvi et al. [11] Rice Machine Learning (ML)
Deng et al. [12] Rice ANN + Genetic Algorithm (GA)
Estes et al. [13] Maize Machine Learning (ML)
Lopez-Blanco et al. [14] Several Crops Fuzzy approach + ML
Raza et al. [15] Several Crops Fuzzy approach
The literature presented in this table depicts various approaches utilized by food and agricultural organization for land suitability frameworks. Most of the identified tractional methods indicates that the socioeconomic data are minimal, and this is very critical in the case of conducting the assessment for crop suitability [16][17]. Some of the approaches also pointed out the limitations of using the ordinal linear combinations for addressing the problems which are needed to practice the non-linearity. It was also revealed that the suitability and vulnerability of the transcending approaches is more in the cases of qualitative and quantitative analyses.

2. Evaluation of Suitability of Urban Land Using GIS Technology, Take Yan'an as an Example

2.1. Analysis on the Evaluation Results of Land-Use Suitability in the New Planning Area

The suitability level and degree of impact of subdivided construction land need to consider the characteristics and distribution of the current topography of the planning new area [18][19]. The northern New Area of Yan’an has many mountain structures and complex topography, which poses certain limitations to the construction of urban land. Combining the four major influencing factors mentioned above and analyzing through the GIS system, the suitability construction distribution map is obtained. According to the above calculation method, the degree of influence of each evaluation factor is obtained. Then, through the ArcGIS weighted synthesis tool, the weight of the construction land is obtained. Figure 62 shows the resulting weight map. According to the result of the superposition analysis, the suitability construction level of the planning new area is divided into three types: suitable construction area, generally suitable construction area, and unsuitable construction area. Finally, Figure 73 shows the most suitable construction land area, which is about 80% of the total planning area. The banned construction area occupies the smallest land area, about 4% of the total planning area.
Sustainability 13 10521 g006
Figure 62. Index weights of the evaluation system for the suitability of construction land.

References

  1. Mondal, B.; Das, D.N. How residential compactness and attractiveness can be shaped by environmental amenities in an industrial city? Sustain. Cities Soc. 2018, 41, 363–377.
  2. Zellner, M.L.; Theis, T.L.; Karunanithi, A.T.; Garmestani, A.S.; Cabezas, H. A new framework for urban sus-tainability assessments: Linking complexity, information and policy. Comput. Environ. Urban Syst. 2008, 32, 474–488.
  3. Qiong, J.; Andrey, Z. Study on ecological evaluation of urban land based on GIS and RS technology. Arab. J. Geosci. 2021, 14, 1–8.
  4. Marcella, S.V.; Juan, P.M.D. City Logistics in historic centers: Multi-Criteria Evaluation in GIS for city of Salvador (Bahia–Brazil). Case Stud. Transp. Policy 2019, 7, 772–780.
  5. Qin, X.F.; Qin, P. Evaluation of the Suitability of Urban Construction Land—A Case Study of Hudai Town. Urban. Land Use 2019, 7, 1–10.
  6. Hoseini, Y.; Kamrani, M. Using a fuzzy logic decision system to optimize the land suitability evaluation for a sprinkler irrigation method. Outlook Agric. 2018, 47, 298–307.
  7. Hassan, I.; Javed, M.A.; Asif, M.; Luqman, M.; Ahmad, S.R.; Ahmad, A.; Akhtar, S.; Hussain, B. Weighted overlay based land suitability analysis of agriculture land in Azad Jammu and Kashmir using GIS and AHP. Pak. J. Agric. Sci. 2020, 57, 1509–1519.
  8. Silva-Gallegos, J.J.; Aguirre-Salado, C.A.; Miranda-Aragón, L.; Sánchez-Díaz, G.; Valdez-Lazalde, J.R.; Pedroza-Carneiro, J.W.; Flores-Cano, J.A. Locating potential zones for cultivating Stevia rebaudiana in Mexico: Weighted linear com-bination approach. Sugar Tech 2017, 19, 206–218.
  9. Bagherzadeh, A.; Gholizadeh, A. Modeling land suitability evaluation for wheat production by parametric and TOPSIS approaches using GIS, northeast of Iran. Model. Earth Syst. Environ. 2016, 2, 1–11.
  10. Bagherzadeh, A.; Ghadiri, E.; Darban, A.R.S.; Gholizadeh, A. Land suitability modeling by parametric-based neural networks and fuzzy methods for soybean production in a semi-arid region. Model. Earth Syst. Environ. 2016, 2, 104.
  11. Danvi, A.; Jütten, T.; Giertz, S.; Zwart, S.; Diekkrüger, B. A spatially explicit approach to assess the suitability for rice cultivation in an inland valley in central Benin. Agric. Water Manag. 2016, 177, 95–106.
  12. Deng, F.; Li, X.; Wang, H.; Zhang, M.; Li, R.; Li, X. GIS-based assessment of land suitability for alfalfa cultivation: A case study in the dry continental steppes of northern China. Span. J. Agric. Res. 2014, 12, 364–375.
  13. Estes, L.D.; Bradley, B.A.; Beukes, H.; Hole, D.G.; Lau, M.; Oppenheimer, M.G.; Schulze, R.; Tadross, M.A.; Turner, W.R. Comparing mechanistic and empirical model projections of crop suitability and productivity: Implications for ecological forecasting. Glob. Ecol. Biogeogr. 2013, 22, 1007–1018.
  14. López-Blanco, J.; Pérez-Damián, J.L.; Conde-Álvarez, A.C.; Gómez-Díaz, J.D.; Monterroso-Rivas, A.I. Land suitability levels for rainfed maize under current conditions and climate change projections in Mexico. Outlook Agric. 2018, 47, 181–191.
  15. Raza, S.M.H.; Mahmood, S.A.; Khan, A.A.; Liesenberg, V. Delineation of Potential Sites for Rice Cultivation Through Multi-Criteria Evaluation (MCE) Using Remote Sensing and GIS. Int. J. Plant Prod. 2017, 12, 1–11.
  16. Veselov, G.; Tselykh, A.; Sharma, A.; Huang, R. Applications of Artificial Intelligence in Evolution of Smart Cities and Societies. Informatica 2021, 45.
  17. Leroux, L.; Castets, M.; Baron, C.; Escorihuela, M.-J.; Bégué, A.; Seen, D.L. Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. Eur. J. Agron. 2019, 108, 11–26.
  18. Ruzikulova, O.; Sabitova, N.; Kholdorova, G. The role of GIS technology in determining irrigated geosystems. E3S Web Conf. 2021, 227, 03004.
  19. Hossain, M.; Masud, M. Evaluating software usability of geographic information system. Int. J. Comput. Internet Manag. 2009, 17, 37–54.
More