A WOE Method for Urban Growth Boundary Delineation and Its Applications to Land Use Planning

International Journal of Geographical Information Science

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A WOE method for urban growth boundary delineation and its applications to land use planning

Xin-qi Zheng & Li-na Lv

To cite this article: Xin-qi Zheng & Li-na Lv (2016) A WOE method for urban growth boundary delineation and its applications to land use planning, International Journal of Geographical Information Science, 30:4, 691-707, DOI: 10.1080/13658816.2015.1091461

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Download by: [Zhejiang University] Date: 15 April 2016, At: 09:17 INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2016 VOL. 30, NO. 4, 691–707 http://dx.doi.org/10.1080/13658816.2015.1091461

A WOE method for urban growth boundary delineation and its applications to land use planning Xin-qi Zhenga and Li-na Lvb aSchool of Information Engineering, China University of Geosciences, Beijing, PR China; bSchool of Mines, Heilongjiang University of Science and Technology, Haerbin, PR China

ABSTRACT ARTICLE HISTORY The aim of this study is to develop a new approach for delineat- Received 10 May 2014 ing urban growth boundaries (UGBs) by applying the weight of Accepted 29 August 2015 evidence (WOE) method to land suitability assessments. Rapid KEYWORDS urbanization is causing urban areas to encroach on agricultural Weight of evidence method; land in China, posing a threat to national food security. Land use urban growth boundary; planning with clear delineation of UGBs is an effective method suitability assessment; land for controlling urban expansion. However, existing methods for use planning; Jinan delineating UGBs are typically complex or involve arbitrary decision-making. To address these drawbacks, we introduced the WOE method to develop a new UGB delineation approach, and applied this approach to a case study in the city of Jinan, China. This application achieved satisfactory accuracy; therefore, we concluded that the WOE method was an objective and effective approach to land use suitability assessments and UGB delineation.Landuseplanningcouldbebenefitted considerably from the application of this method to land allocation and other planning decisions.

1. Introduction Since the 1980s, China has witnessed rapid and large-scale urbanization. According to land use change data from the Ministry of Land and Resources of the People’s Republic of China, the extent of urban land increased at an average rate of 4849 km2/yr from 1997 to 2009 at Downloaded by [Zhejiang University] at 09:17 15 April 2016 the expense of extensive areas of cultivated land, which consequently deteriorated environmental quality (Xiao et al. 2006). The resulting urban sprawl has created a number of social and economic issues that have affected both national and regional sustainable development efforts (Karolien et al. 2012). In light of these effects of urbanization, it is essential for the government to effectively control the spread of urban areas, paying special attention to the protection of cultivated land and vulnerable ecological resources. Urban growth boundaries (UGBs) are effective tools for managing urban expansion. By definition, UGBs are zoning controls that separate land targeted for development from rural land, which allows the government to limit growth outside the boundary (Carter 2009). The UGB concept was first proposed and applied in 1958 in Lexington,

CONTACT Xin-Qi Zheng [email protected] Li-na LV is currently aﬃliated to College of Mining Engineering of HUST, Heilongjiang University of Science and Technology, Haerbin 150022, PR China © 2015 Taylor & Francis 692 X.-Q. ZHENG AND L.-N. LV

Kentucky, in the United States (Nelson and Duncan 1995). It has become one of the most successful and indispensable policy tools for urban growth management and for the implementation of smart growth policies in large cities (Ding et al. 1999, Calthorepe and Fulton 2001, Jun 2006). The demarcation of UGBs, which was originally subjective and politically oriented, has gradually become an objective and database-backed process. Recently, various models and algorithms have been proposed to simulate urban expansion and determine UGBs. Cellular automata (CA) models combined with geographic information systems (GISs) have been widely applied to simulate urban expansion. CA models are used to predict future urban expansion by setting proper transition rules based on land use changes (Poelmans and Van Rompaey 2010, Liu 2012). However, land use changes involve highly complex and nonlinear processes (Lambin et al. 2001); consequently, these rules are difficult to identify based on the phenomena of previous land use changes. To better capture the complex characteristics of land use change, many advanced algorithms have been developed to simulate urban expansion and demarcate UGBs. Researchers have developed agent-based models by incorporating decision-maker behaviors in various types of simulated urbanization scenarios (Guzy et al. 2008). Almeida et al. introduced weight of evidence (WOE) methods into CA–Markov models to simulate urban land use changes and predicted future spatial patterns of urban growth (Maria de Almeida et al. 2003, 2005, Thapa and Murayama 2011). Furthermore, algorithms including genetic algorithms (GAs), particle swarm optimization (PSO), artificial neural networks (ANNs), and ant colony optimization (ACO), as well as various combinations of these algorithms (Shan et al. 2008, Wu and Silva 2010, Feng et al. 2011, Rabbani et al. 2012, Yang et al. 2012), have been incorporated into CA simulations. However, the adaptability and dynamics of the behavior rules of these algorithms are often inadequate because such behaviors can be affected by the external environment concomitantly. As research continues, methods for demarcating UGBs have become more complicated. They are relatively difficult to comprehend and implement, which limits their popularity and applicability. In China, the use of UGBs has gradually been adopted for land use planning. Because of algorithm limitations, as described in the preceding paragraph, UGBs have historically been delineated based on land suitability assessment models in

Downloaded by [Zhejiang University] at 09:17 15 April 2016 China. The mechanisms of such traditional suitability assessment methods are straightforward and computationally simple. However, most traditional assessment methods assign index weights that directly affect the accuracy and validity of the final evaluation. Therefore, the weighting process is a key component of such methods. Unfortunately, it is also a limitation because personal judgments play a significant role in the weighting procedure and final result (Rodriguez-Gallego et al. 2012,Juet al. 2012). Therefore, it is evident that a new method is required for designating UGBs to overcome the complexity and subjectivity discussed above. In this study, we intro- duce the WOE method into the research of land suitability assessment models and UGB delineation. The WOE method was originally used by geologists to indicate areas that were favorable for specific geological phenomena. This method integrates a variety of spatial data layers as suitable indicators for decision-making and prediction (Bonham-Carter and Chung 1983, Bonham-Carter et al. 1988). The advantage of INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 693

this method is that the weights of the indicator layers are based solely on data, which reduces the need for subjective decisions. Furthermore, the WOE method’s calculation process is similar to that of traditional land suitability assessments; speciﬁcally, its mechanism is straightforward and computationally simple. Therefore, the proposed WOE method is advantageous for the subjectivity and complexity in existing evaluation methods. In this study, we ﬁrst address the principles and methodology of the WOE method (Section 2) and demonstrate the application of the WOE method to Jinan, China (Section 3). We then discuss the feasibility and superiority of the WOE method (Section 4). Finally, we conclude our study with recommendations and conclusions based on our results (Section 5).

2. Methodology 2.1. Principles of the weight of evidence method The core of the WOE method is the calculation of the weight. The WOE method is a statistical method based on a log-linear form of Bayes’ theorem. Under the conditional independence (CI) conditions, it employs a conditional posterior probability to estimate the importance (weight) of corresponding layers and presents quantitative results as a posterior probability map. For a detailed description of each step of the computational process, we refer to Bonham-Carter (1994) and Bonham-Carter and Agterberg (1999). The core algorithm involved in this method is described below. The weight of the given binary evidence image is deﬁned according to the following equation (Bonham-Carter and Agterberg 1989): 8 ð j Þ >Wþ ¼ ln P B D evidential theme is present <> PðBjDÞ ¼ ð j Þ : W >W ¼ ln P B D evidential theme is absent (1) :> PðBjDÞ 0 missing data

This equation calculates the weight for each predictive factor based on the presence or

Downloaded by [Zhejiang University] at 09:17 15 April 2016 absence of event theme units (D) within the area of each binary predictor theme (B). Here, PðjÞ denotes the probability and ln denotes the natural logarithm. Furthermore, B is the presence of a dichotomous pattern, B is the absence of a dichotomous pattern, D is the presence of an event occurrence, and D is the absence of an event occurrence (Lee and Oh 2010). By overlaying event layer (training points) onto each evidence layer, the statistical spatial association can be measured between event and evidence layers using their weights. A positive weight indicates that there is a high likelihood that the event will occur in this specificclass,whileanegativeweightindicatesalow likelihood. A weight of zero, or very close to zero, indicates that there is no relevance to the specific class (the weight of the original missing data is zero). The difference between positive and negative weights is the weight contrast, denoted by C,where C ¼ Wþ W.ThemagnitudeofC is the overall spatial association between the training points and evidential layers; moreover, it combines the effects of the two weights. 694 X.-Q. ZHENG AND L.-N. LV

If n evidence indicators meet the requirement for CI, the posterior odds logarithm is expressed as follows:

noX n ln OðD|BkBk Bk Þ ¼ Wk þ ln OðDÞ; ðj ¼ 1; 2; 3 nÞ; (2) 1 2 n j1 j

where the superscript k is + for presence or – for absence of the jth binary pattern. The odds, O, is related to the probability, P. In light of the aforementioned expressions, the prior probability of ﬁnding an event in a unit cell of area is given by Oprior= (number of unit cells occupied by events)/(total unit cells). The ﬁnal posterior odds are expressed according to the following relationship: noX n O ¼ exp Wk þ lnðO Þ ; (3) posterior j j prior

while the posterior probability is given by

Pposterior ¼ Oposterior=ð1 þ OposteriorÞ: (4)

The posterior probability P represents the favorable degrees of each unit and can be displayed visually for suitable hierarchies.

2.2. Implementation procedure for suitability assessments and urban growth boundary delineation For the spatial context of this study, we applied the WOE method to an urban land suitability assessment and to the delineation of UGBs based on the hypothesis that the city is continuously expanding in accordance with historical trends. This approach determines land use suitability as a function of potential evidences (e.g., proximity to roads, slope) using the WOE method. The weights are estimated from the measured association between the training points and suitability evaluation index layers that were used as predictors. This process is depicted step by step in

Downloaded by [Zhejiang University] at 09:17 15 April 2016 Figure 1. First, we establish an informational database including spatial, natural, and social resources data from the local government for the research area. Then, after data collection, we extract the training points for the WOE computation. Third, on the basis of a summary of available research, we select the most reasonable evidence indicators as evaluation indexes according to CI test results and perform the calculation of the weight and posterior probability using the WOE method. Finally, if the model’s validation of accuracy and feasibility passes, we generate a spatial distribution map of land suitability for urbanization based on the posterior probability and delineate the UGB within the study area from high to low urbanization suitability over a quantity of the demand scale. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 695

Figure 1. Flowchart for the urban land suitability assessment and urban growth boundary delineation based on the weight of evidence method.

3. Case study 3.1. Study area The city of Jinan is the capital of Shandong Province, China. It is located south of the Bohai Sea coastal region and is close to the middle and lower reaches of the Yellow River. The city is bordered by the Tai Mountains to the south and the Yellow River to the north, with a much higher topography in the south. Hilly areas, piedmont clinoplain, and alluvial plains traverse the city from south to north (Yang et al. 2010), forcing the urban area to expand eastward (Kong and Nakagoshi 2006). The districts of Lixia, Shizhong, Huaiyin, Tianqiao, part of Licheng, and Pingyin surround the central urban area of Jinan (Figure 2). Downloaded by [Zhejiang University] at 09:17 15 April 2016 Jinan is currently facing dual challenges of rapid urbanization and farmland loss. The amount of newly urbanized land in Jinan increased from 96.32 km2 in 1979 to 199.3 km2 in 2004, with an increased growth rate from 2.31 km2/yr in the 1970s to 15.70 km2/yr in the early 2000s (Mu et al. 2008). This growth rate change is consistent with the urbanization process on a national scale in China. With such rapid expansion, inevitable conflicts arise between urbanization and food security needs. The solution to such conflicts requires a balance that is found through appropriate regional planning, which presents significant challenges. We propose well-defined UGBs to prevent excessive urban sprawl that will alsocontributetoregionalsustainable development. Following the procedure described in Section 2.2 (Figure 1), we apply the WOE method to Jinan as a case study. 696 X.-Q. ZHENG AND L.-N. LV

Figure 2. Geographical location of the study area.

3.2. Data preparation and pretreatment 3.2.1. Data collection The source data is shown in Table 1. All of the data were processed with a uniﬁed geographical coordinate system for subsequent calculations.

3.2.2. Extraction of training points A point layer was generated to show the locations of events. By overlaying the training points on the evidence layers, the model used the point locations to calculate the weight of each layer. We overlaid the oﬃcial land use layers for 1996, 1998, 2003, 2006, and 2010 and selected areas of urbanized land that had not changed during the Downloaded by [Zhejiang University] at 09:17 15 April 2016

Table 1. Data source description. Data Data type Data source format Data period Accuracy Remark Land use The Bureau of Land and .shp 1996, 1998, 1:10,000 52 types Resources of Jinan 2003, 2006, 2010 Road .shp 2010 1-km interval buﬀering Comprehensive .shp 2010 5 types partition Slope .shp 2010 5 types Landslide risk The Research Center for .img 2011 1:4,000,000 Five types nationwide Flood risk Eco-Environmental Sciences, .img 2011 (Landslide risk:2–5 Flood Chinese Academy of Sciences risk:1–3 in Jinan) Subgrade bearing The Bureau of Land and .shp 2010 1:50,000 4 types capacity Resources of Jinan Bearing stratum .shp 2010 3 types depth INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 697

time period. The urbanized land area included urban and built-up areas, designated towns, rural settlements, and independent industrial land, which were classified according to the currently enforced land use classification standard. We then abstracted training points based on the centroids of the unchanged urbanized areas. Next, we used a systematic spatial sampling method to uniformly sample the data at a specific interval size based on the geometrical shape. Finally, we obtained 5000 sample points for training to ensure that we had an average of approximately one point per hectare in the urbanized land.

3.2.3. Construction of an evaluation index system With regards to the urban expansion issue, studies indicate that both socioeconomic and physical drivers affect urban growth and its spatial distribution (Huang et al. 2009, Han et al. 2009). Different research emphases lead to different factor selections. For the socioeconomic driven, social and economic factors are relatively more important; for the physical driven, physical elements play a dominant role. To evaluate the suitability of urban growth at the physical level in our research, the choice of factors is influenced by the following three aspects: the current land condition (physical), infrastructure (social), and development goals (political). The physical land condition and social infrastructure factors are the two key points influencing urbanization patterns and layout. For example, physical land conditions can directly influence the location of newly constructed cities, the selection of sites for engineering facilities, and the layout of the road net (Xu et al. 2011); meanwhile, the pre-existing partitioning of the landscape and the existing road network can directly affect where new regional development is likely to occur. Political favor is also nonnegligible in urban growth. By summarizing existing research (Park et al. 2011, Gong et al. 2012, Vaz et al. 2012), we determined seven factors that influence the evaluation index system. These factors are slope, flood risk, landslide risk, bearing stratum depth, subgrade bearing capacity, road, and the comprehensive partition index (Figure 3).

3.2.4. Conditional independence test We calculated the independent conditions of the seven evidence layers and produced a corresponding matrix (Table 2). The results of the matrix indicate that all evidence

Downloaded by [Zhejiang University] at 09:17 15 April 2016 layers satisfy the CI assumption and can be used for all subsequent calculations (P ≤ 0.05).

3.3. Results 3.3.1. Weight analysis Using the formulas described in Section 2.1, we calculated the weight of the seven evidence layers (Table 3). In Table 3, the promotion or inhibition for construction is reﬂected by the weight and contrast C; a positive value indicates that it is conducive to construction, while a negative value indicates it is unfavorable. Furthermore, as the magnitude increases, the suitability also gradually increases. From Table 3, the favorable targets can be distinguished from the detrimental ones. For example, the slope layer indicates that areas suitable for construction are mainly concentrated where the slope is 0–5°. As the subgrade bearing capacity and bearing stratum depth decrease, the Downloaded by [Zhejiang University] at 09:17 15 April 2016 698 .Q HN N .N LV L.-N. AND ZHENG X.-Q.

Figure 3. Evidence layers used. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 699

Table 2. Pairwise conditional independence test results. E-Theme FR LR BSD SBC DTR CP Slope 0.0133 0.0237 0.0129 0.0141 0.0208 0.0492 FR 0.0379 0.0481 0.0438 0.0021 0.0487 LR 0.0480 0.0274 0.0365 0.0023 BSD 0.0473 0.0359 0.0482 SBC 0.0299 0.0213 DTR 0.0407 Overall test of CI 0.9133 Flood risk, FR; landslide risk, LR; bearing stratum depth, BSD; subgrade bearing capacity, SBC; distance to main road, DTR; comprehensive partition, CP.

Table 3. Summary of weights and contrasts. Factors Class W+ W- Contrast SLOPE 0–2 1.3325 −1.0357 2.3682 (degree) 2–5 1.0629 0.0212 1.0417 5–15 −0.5428 0.0126 −0.5554 15–25 −1.2314 0.0117 −1.2431 >25 −2.3871 0.0053 −2.3924 Landslide risk 2 1.8784 −0.0509 1.9293 3 −0.5712 0.218 −0.7892 4 −0.3033 0.5408 −0.8441 5 −2.6485 0.0891 −2.7376 Flood risk 1 0.4444 −0.7135 1.1579 2 −0.7134 0.4444 −1.1578 3 −0.7901 0.0548 −0.8449 Subgrade >250 0.2192 −2.0946 2.3138 bearing 180–250 0.043 −1.5835 1.6265 capacity 100–180 0.1153 −0.7542 0.8695 (KPa) <100 −1.2925 0.4787 −1.7712 Bearing stratum >10 1.5232 −0.1741 1.6973 depth 5–10 1.3569 0.2307 1.1262 (m) <5 −1.0445 −0.2162 −0.8283 ROAD 1000 0.8375 −0.9947 1.8322 (m) 2000 −0.6648 0.2078 −0.8726 3000 −1.5417 0.1627 −1.7044 4000 −1.3998 0.0696 −1.4694 5000 −0.8972 0.0266 −0.9238 6000 −1.5078 0.0192 −1.527 7000 0 0 0 Comprehensive Urban development 1.81 −1.2871 3.0971 Downloaded by [Zhejiang University] at 09:17 15 April 2016 areas partition Basic farmland −1.6696 0.3716 −2.0412 protection areas Natural and historical −0.3902 0.0437 −0.4339 cultural areas Ecological −1.2366 0.2236 −1.4602 environment security control areas General agricultural −2.106 0.1304 −2.2364 development areas

suitability for urbanization gradually decreases. The results also suggest that as the distance to the main road and the geological hazard risk increase, the suitability for urbanization gradually decreases. In this analysis, for a comprehensive partition evidence layer, only the areas currently slated for urban development had a positive weight, 700 X.-Q. ZHENG AND L.-N. LV

which were not in conﬂict with Jinan’s general land use planning scheme. The ﬁnal results indicate that the calculated weights resulting from this analysis are consistent with reality.

3.3.2. Model validation Model testing is an important step in model construction research. Studies have indi- cated that a confidence value greater than 1.96 is statistically significant (at α ¼ 0:05) and is twice the standard deviation (Bonham-Carter and Agterberg 1989). A receiver operating characteristic (ROC) curve (Metz 1978, Swets 1986) can also be used to verify the model’s accuracy. An Area Under ROC Curve (AUC) of 1 indicates that the model has perfect accuracy, whereas an AUC < 0.5 is indicative of an invalid model. For our analysis, all evidence layers were within a 0.05 significance level (Table 4) and could thus be used to calculate the posterior probability effectively. The ROC curve for the proposed method had an overall accuracy of 90.33%, demonstrating that the proposed model is effective (Figure 4).

Table 4. Signiﬁcance tests of layers. E-Theme W+ W- Contrast Conﬁdence Subgrade bearing capacity 0.4787 −2.0946 2.5733 17.4687 Bearing stratum depth 1.7469 −1.5543 3.3012 17.1621 Distance to main road 0.8375 −7.8834 8.7209 4.8720 Slope 1.0382 −1.6497 2.6879 5.2366 Landslide risk 1.8784 −2.6485 4.5269 13.4252 Flood risk 0.4444 −1.6920 2.1364 3.2079 Comprehensive partition 1.8100 −2.1060 3.9160 20.7840 Downloaded by [Zhejiang University] at 09:17 15 April 2016

Figure 4. Receiver operating characteristic curve for the proposed weight of evidence method. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 701

3.3.3. Suitability assessment for construction land Under the supposition that the estimated weights and model are credible and eﬀective, the WOE method was used to produce a posterior probability map. The posterior probability represents each unit’s degree of suitability for a given event or activity. Therefore, we generated a construction land suitability map within our study region based on the calculated posterior probability (Figure 5). As shown in Figure 5, the highly suitable areas are primarily located in Lixia, Shizhong, Huaiyin, and the northern part of Licheng.

3.3.4. Delineation of urban growth boundaries Following the suitability assessment, we delineated the UGBs for Jinan. In this paper, we directly considered the amount of construction land (450.85 km2) as issued by the legislation of Jinan General Land Use Planning (2006–2020) (Jinan Municipal Government 2011) that was assigned by a higher-level governmental institution. To demarcate the UGBs, we ﬁrst removed basic farmland protection areas highlighted in the Jinan General Land Use Planning report for 2006–2020 (Jinan Municipal Government 2011) from the analysis, which prevented any potential conﬂicts that could arise from the selection of urbanized land sites on what is now functioning as basic farmland. This operation also allowed us to prioritize the preservation of basic Downloaded by [Zhejiang University] at 09:17 15 April 2016

Figure 5. Spatial distribution of urban land suitability in Jinan, China. 702 X.-Q. ZHENG AND L.-N. LV

Figure 6. Urban growth boundary in Jinan, China, as determined by the weight of evidence method.

farmland, which, according to Chinese law, cannot be changed or occupied. Then, we accumulated the areas from high to low posterior probability into an area of 450.85 km2 within the central urban area and extracted the boundary as the UGB. Considering the integrity and continuity of UGB designation, the fact that the boundary encompassed some small-scale areas that are unsuitable for urbanization is acceptable. We did not delineate these unsuitable areas separately. Figure 6 illustrates the boundary determined by the WOE method.

4. Discussion 4.1. Feasibility of the weight of evidence method for the suitability evaluation

Downloaded by [Zhejiang University] at 09:17 15 April 2016 The suitability of a city’s formation, construction, and development is closely related to natural geographical factors. These basic conditions directly impact the potential speed and direction of urban expansion. Jinan is located to the north of Mount Tai and to the south of the Yellow River, in an area of collision between the mid-southern hilly region and northwestern alluvial plain region of Shandong Province. Our suitability assessment of construction land revealed that Jinan’s construction land suitability was significantly affected by its natural and geographical characteristics. The suitability results revealed that potential urbanization extended along a strip from the northeast to southwest between the southern mountainous area and the northern Yellow River plain, where were evaluated as high suitability areas. In fact, the land to the west of Jinan is flat, and the vast area of the Yellow River’s alluvial plain provides the possibility for urban expansion in that direction. The evaluation results properly reflect the real suitability and agree with general knowledge, which confirms the rationality of the assessment and the feasibility of the WOE model for evaluating land suitability. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 703

4.2. Objectivity and reliability of the weight of evidence method for weight allocation The current UGBs for Jinan were obtained from the city’s Land Use Planning Legislation (Jinan Municipal Government 2011) and were designated based on a series of profes- sional analyses and expert discussions. This boundary is infrangible because of support- ing legislation. Considering the verifiability of the evaluation results, we compared the delineated boundary from the WOE method with the implemented boundary to test the feasibility and reliability of our model (Figure 7). The WOE-based UGB delineation first considers basic farmland protection. Therefore, the removal of basic farmland protection areas in the WOE-based UGB leads to differences between the two boundaries in locations such as southwest Changqing. The northern boundary based on the WOE method extends further northward because of the influence of the mountainous terrain in the south. The Yellow River basin restrictions push the boundary in Huaiyin and Shizhong southward. Generally, the two sets of boundaries, demarcated by different methods, have a spatial overlap of 80.12% of their area. The satisfied accuracy proves the reliability of our result. The demarcated UGB based on the WOE method is generally consistent with the implemented UGB both from its spatial position and tendency. Furthermore, the WOE method is more objective than some traditional methods used in urban planning in terms of weight allocation. It calculates the weight solely on data. Moreover, the WOE result is calculated based on the spatial positions of the training points and the corresponding attribute categories at those positions, instead of their attribute values. Thus, it effectively avoids the subjectivity of artificially assigning attribute values for the corresponding category of each layer before the assessment. Therefore, introducing the WOE method into the suitability assessment decreases subjectivity to obtain more reliable results. Downloaded by [Zhejiang University] at 09:17 15 April 2016

Figure 7. Comparison of the urban growth boundary as delineated by the weight of evidence method with the one currently enforced for land use planning. 704 X.-Q. ZHENG AND L.-N. LV

4.3. The weight of evidence method is more usable compared to the cellular automata model coupled with the weight of evidence method Based on the research of Almeida et al.(MariadeAlmeidaet al. 2003, 2005,Thapa and Murayama 2011), we simulated the development of construction land using the CA model coupled with the WOE method and then compared its result with the UGBs delineated solely by the WOE method. Although there are some slight differences in the two boundaries, Figure 8 illustrates that the two results are approximately the same, with a spatial overlap of 83.22%. Since land use change is highly complex, spatially dynamic, and nonlinear, it is difficult to set proper simulation rules based on previous land use changes. We suspect that this may have directly affected the results of the CA simulation. Therefore, since there are no clear contradictions between the boundaries determined by the two methods, we suggest that the UGB obtained by the WOE method was acceptable. Compared with the CA simulation, which must be combined with additional algorithms to capture the cellular transition rules, the WOE method is straightforward and computationally simple. Similar to traditional suitability assessment methods, the WOE method uses a log-linear form of Bayes’ theorem to combine different spatial layers and predict suitability. The whole operation is effective and time-saving. Furthermore, the WOE method accurately identifies the promotion or inhibition of each layer or factor class easily and independently from the weights. Thus, such advantages can significantly enhance the interpretability of the final result. Overall, compared with the CA model, the proposed WOE method is clearly superior in terms of convenience, maneuverability, and ease of interpretation. Downloaded by [Zhejiang University] at 09:17 15 April 2016

Figure 8. Comparison of the urban growth boundary as delineated by the weight of evidence method with the one based on a cellular automata model. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 705

5. Conclusion The delineation of UGBs is of crucial importance in land use planning. UGBs can determine the supply and distribution of construction land, thereby providing guidance for decision-makers to resolve conflicts between land uses for construction versus cultivation. To avoid issues of complexity and subjectivity that exist in some traditional methods, we introduced the WOE method into a suitability assessment. Using Jinan as a case study, we implemented the proposed design of the WOE method. The precision and feasibility of the proposed model were verified with significance tests and an ROC curve and compared with results of other methods both qualitatively and quantitatively. The validation illustrated satisfactory accuracies. The WOE method employs a log-linear form of Bayes’ theorem to determine the weights of corresponding layers and quantitatively presents the results as a posterior probability map. The entire calculation process is data-driven. The weight of each attribute and spatial layer can be calculated objectively, independently, and easily interpreted. Furthermore, the calculation mechanism avoids the subjective influence that traditional methods require by artificially assigning suitability values for corresponding attribute categories prior to assessment. Moreover, this method is simple, conveni- ent, and usable. The selection of the evaluation layers and extraction of training points are the key aspects affecting the precision of this model; however, they are limitations as well. Different indicator systems lead diverse results. More multifarious and reasonable factors should be taken into consideration such as political favor factors, which can be measured at the commercial level or other quantifiable indicators. Also the method of extracting training points can be improved by combining it with other models. Further research is needed on these topics. Despite these suggested improvements, this method can be used as a land suitability evaluation model to expand existing methodology; moreover, it can be utilized effectively at different spatial scales in a wide variety of applications for land use planning or other land projects.

Downloaded by [Zhejiang University] at 09:17 15 April 2016 Acknowledgments

We would like to thank Lijun Zhang and Zuoquan Zhao for valuable comments and suggestions in earlier discussions. We would also like to express our gratitude to Professor Wei-Ning Xiang, Dr. Lu Zhao, Xin Yang, Jinwei Dong, and Jing Deng for comments to this paper.

Disclosure statement

No potential conﬂict of interest was reported by the authors.

Funding

This research was supported by the Chinese National Major Programs of International Cooperation and Exchanges of China [grant number S2015ZR1018]. 706 X.-Q. ZHENG AND L.-N. LV

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