PANELES DE YESO PARA TABIQUES

527

In this study, the city of Jinan, China was used as a case study to demonstrate the potential

528

impacts of planning policies and strategies on urban growth prediction patterns and accuracy using

529

a cellular-automaton-based urban growth model. To date, it has been difficult to integrate planning

530

policies into the conversion rules used by the SLEUTH model (Torrens, 2011), and many case

531

studies have indicated that this model could not effectively characterize the potential impacts of

532

urban development policies on urban land use (Clarke et al., 1997; Silva and Clarke, 2002; Lahti,

533

2008; Wu et al., 2009). However, we found that using an appropriate method to incorporate zoning

534

can help improve simulation accuracy and therefore the capability of simulating the effects of

535

urban development policies (Chaudhuri and Clarke, 2013; Akιn et al., 2014; Onsted and

536

Chowdhury, 2014).

537

Four zoning scenarios (S1–S4,) as well as a scenario that did not include zoning (S0), were

538

developed through the generation of different types of exclusion layers. The SLEUTH 3.0 model

539

was used to simulate urban growth in 2011, 2016, and 2040 under various scenarios and the results

32

were assessed at the pixel level. The main conclusions are as follows. (1) At the pixel level, overall

541

accuracy is not quite meaningful in representing model accuracy; instead, producer or user

542

accuracy of newly urbanized pixels might be more appropriate. (2) Incorporating planning policies

543

into zoning information can help improve the prediction accuracy of newly urbanized pixels, better

544

represent clustered development, and boost the level of spatial matching, while zoning based on

545

land-use type does not offer such improvements. (3) Compared with the no-zoning scenario (S0),

546

the scenario in which zoning was based on development planning subdivisions (S4) generated the

547

largest improvement in the prediction accuracy, followed by scenarios S3, S2, and S1. Using the

548

city of Jinan as a case study, the study demonstrated that more detailed (i.e., more finely divided)

549

zoning, particularly in areas with high probability of urban growth, can yield more accurate

550

predictions. The scenarios taking into account the spatial differentiation of development planning

551

policies (S2–S4) generated better predictions than the scenario considering land-use type only (S1),

552

as the former scenarios incorporated more finely divided zoning schemes. In a summary,

553

incorporating zoning information based on spatial differentiation and growth management policies

554

can help improve simulation accuracy and spatial matching degree, thus allowing the more

555

accurate projection of urbanizing patterns through the use of appropriately designed zoning

556

schemes.

557

Although a number of previous studies examined the impacts of zoning on simulation

558

accuracy (e.g., White and Engelen, 1993; Berling-Wolff and Wu, 2004; Onsted and Chowdhury,

559

2014), the potential impact of different zoning schemes on simulation accuracy has not been

560

thoroughly investigated. For example, Berling-Wolff and Wu (2004) considered agricultural land

561

to be a separate category in simulating the urban landscape dynamics of the city of Phoenix in the

562

United States in an approach similar to that used in other studies that did not consider zoning

563

information (Jantz et al., 2003, 2010; Rafiee et al., 2009; Akιn et al., 2014). Models in which

564

various protection levels (or conversion probabilities) were assigned to different land-use types

565

based on urban development policies have proven capable of capturing the spatial consequence of

33

urban development policies. Onsted and Chowdhury (2014) considered three types of zoning, i.e.,

567

developmental, interim, and agricultural zoning, using various zoning assignment methods and

568

evaluated the model accuracy variation in terms of the amounts or rates of urban growth under

569

different assignment methods using the OSM metric. Unlike these previous studies, this study

570

explored the impacts of several zoning schemes based on land-use type, urbanized suitability,

571

administrative division, and planning subdivision (functional groups), with the prediction accuracy

572

evaluated at the pixel level using the OSM metric. Our findings should be useful in improving the

573

performance of urban growth predictions through the use of appropriately designed zoning

574

scenarios.

575

However, several issues may require further attention. First, an alternative weighting method

576

might help better capture the zoning information within a model, as the demand on urban land use

577

in different areas often varies (Goldstein et al., 2004) and land-use change can be significantly

578

influenced by local land-use policies. Under Scenarios S3 and S4, the development policy impact

579

coefficient layer was used to indicate the impact of development policies on regional differences

580

in urban growth using a user-defined option. However, the relationship among different levels of

581

development policy is usually difficult to quantify precisely, and therefore the values of policies at

582

various levels requires further testing. Second, further research is required on choosing an

583

appropriate zoning scale, as this can significantly affect the simulation outcome. A study conducted

584

by Wu et al. (2009) on the Shenyang Metropolitan area found that the SLUETH model did not

585

perform well when modeling a zoning scheme with large administrative districts (~700 km2_{). In} 586

our study, the use of more detailed zoning schemes in conjunction with development policy, such

587

as the schemes in Scenarios S3 and S4 based on administrative districts (av. 57.43 km2_{) and} 588

development functions (av. 41.02 km2_{), respectively, helped boost the simulation accuracy.} 589

Similarly, the scheme used for S2 featuring more detailed zoning granularity but not considering

590

spatial differences in development policy yielded moderate simulation accuracy. Thus, further

34

research is needed to examine the development policy effects of scale on simulation accuracy

592

through the application of measurable weighting methods under various zoning schemes.

35

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40

Table list

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Table 1 Data layers used in the multi-factor overlay analysis for urbanized suitability assessment

722

and the weights assigned to each of the seven factors

723

Table 2 List of major urban and regional development policies for Jinan since 1996 and their

724

respective policy levels

725

Table 3 Description of metrics used for evaluation of the calibration results

726

Table 4 Summary of calibration metrics for different scenarios

727

Table 5 Final coefficients for respective scenarios

728

Table 6 Accuracy assessments of 2011 predictions under different scenarios

729

Table 7 Accuracy assessment of 2016 prediction under different scenarios

730 731

41

Figure list

732

Fig. 1 Location of the study area

733

Fig. 2 Spatial growth of urban extent in Jinan from 1996 to 2016. A-G: components of urban

734

growth regions

735

Fig. 3 Road networks for 1996, 2001, 2006, and 2011

736

Fig. 4 Two model input layers: slope (a) and hillshade (b)

737

Fig. 5 Exclusion layers used for Scenarios S0 (a) and S1 (b). Note that pixels with the attribute

738

value of 100 represent completely excluded areas

739

Fig. 6 Four factors used in the multi-factor overlay analysis for urbanized suitability assessment

740

Fig. 7 Accessibility factors used in the multi-factor overlay analysis for urbanized suitability

741

assessment and the exclusion layer used in Scenario S2

742

Fig. 8 Administrative division-based development policy impact coefficient layer (a) and

743

exclusion layer used in Scenario S3 (b). Note that pixels with attribute values of 100 represent

744

completely excluded areas

745

Fig. 9 Functional group-based development policy impact coefficient layer (a) and exclusion layer

746

used in Scenario S4 (b). Note that pixels with attribute values of 100 represent completely

747

excluded areas

748

Fig. 10 Exclusion layers used for simulations under Scenarios 1–4. Note that pixels with attribute

749

values of 100 represent completely excluded areas

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Fig. 11 Existing (a) and simulated (b-f) urban extents in 2011 under different scenarios

751

Fig. 12 Simulated urban extents in 2016 and 2040 under different scenarios: (a) actual and (b-f)

752

modeled urban growth

753

Fig. 13 Growth of urban built-up land during 2011–2040 under the five scenarios

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In document 5cf95adb88d58f4b06437fb6f9e1f202 (página 148-150)