Predicting multiple land use transitions under rapid urbanization and implications for land management and urban planning: The case of Zhanggong District in central China

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Predicting multiple land use transitions under rapid urbanization and implications for land management and urban planning: The case of Zhanggong District in central China Lingzhi Wanga,b,c,∗, Bryan Pijanowskic, Weishi Yangd,e, Ruixue Zhaif, Hichem Omranig, Ke Lih a

College of Earth Sciences, Jilin University, Changchun, Jilin, 130061, China Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China c Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, 47906, USA d School of Geography and Planning, Sun Yat-Sen University, Guangzhou, Guangdong, 510275, China e Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China f Zhejiang Huanke Environment Consultancy Co., Ltd, Zhejiang, 311100, China g Urban Development and Mobility Department, LISER, Luxembourg h Key Laboratory of Songliao Aquatic Environment, Ministry of Education, Jilin Jianzhu University, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Multiple land use transitions Urbanization Artificial neural networks Land use management Urban planning China

Numerous machine learning-based land change models have been presented by researchers over the last two decades. To date, however, far less have simulated multiple land use classes and specific land use subclasses at the same time. In some areas of the world, it is important to simulate both of these dynamics to understand fully the drivers and consequences of land change. One important example is the process of urbanization in China, as urban policies have been developed that guide urban expansion, rural protections, and urban subclass development. This paper presents a new model integrating geographic information systems (GIS) with artificial neural networks (ANNs) to predict multiple transitions among land use types and urban subclasses in the Zhanggong District of Ganzhou city in China. We show that the model produces satisfactory goodness of fit values-based on location, quantity and spatial configuration-between simulated and observed land use maps for 2015. Our simulated future maps produced by the model for 2020 and 2025 demonstrate that transitions from farmland and forest to urban will remain the main pathway of urbanization although we predict that the rate will slow after 2025. The goals of urban planning should be aligned with land use planning according to "Combining multiple laws and regulations" in China. Taking into account the current and future land use transitions will enhance the accuracy and timeliness of land use policy making and urban land planning. For the sustainable land use in Zhanggong District, we argue for a strengthened regulation of the land market by government and believe that planning officials should guide the spatial distribution of land supply actively. Furthermore, improving the production, living and ecological functions of land resources are the key points to optimize urban land use functions and the allocation of land resources. We discuss how our model can be adapted to other areas to benefit land use management and urban planning in China.

1. Introduction Urban areas contain high concentrations of people, infrastructure and natural resource consumption (Chen & Chen, 2015; Fuseini & Kemp, 2016; Seto, Güneralp, & Hutyra, 2012). As urban areas expand, a knowledge of the nature of the drivers and consequences of these land changes are necessary to develop prudent land use policies that make our use of land more efficient (Angel, Parent, Civco, Blei, & Potere,

2011; Vaz & Nijkamp, 2015). Urban expansion is often at the expense of farmland, forest, or grass (Tsutsumida, Saizen, Matsuoka, & Ishii, 2015), which provides essential ecosystem services to society. There has been considerable evidence presented that transitions from other land use types to urban triggered by the urbanization may have a range of environmental, social and economic consequences (Chen, Gao, & Chen, 2016; Kantakumar, Kumar, & Schneider, 2016; Parece & Campbell, 2013; Pickett et al., 2011). Urban expansion converts natural

∗

Corresponding author. College of Earth Sciences, Jilin University, Changchun, Jilin, 130061, China. E-mail addresses: [email protected] (L. Wang), [email protected] (B. Pijanowski), [email protected] (W. Yang), [email protected] (R. Zhai), [email protected] (H. Omrani), [email protected] (K. Li). https://doi.org/10.1016/j.habitatint.2018.08.007 Received 14 May 2018; Received in revised form 17 August 2018; Accepted 22 August 2018 0197-3975/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Wang, L., Habitat International, https://doi.org/10.1016/j.habitatint.2018.08.007

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compared (Pontius et al., 2008). Many land change models have focused on single transition modeling, such as urban expansion (Charif, Omrani, Abdallah, & Pijanowski, 2017; Omrani, Abdallah, Charif, & Longford, 2015; Shahraki et al., 2011), far less attention has been given to the prediction of the sub-categories of urban-rural construction land, such as urban construction land, rural construction land and other construction land (Henderson, 2003). Here, we examine the Zhanggong District of China where land use changed intensively through urban expansion with complex changes. The objectives of this study were to: (1) develop a method to simulate multiple LUCs among land use classes using geographic information systems (GIS) with ANNs; (2) test whether traditional drivers of urban change are sufficient to capture sub-class transitions in China and expand drivers of land change to includes those that are not considered in traditional urban modeling based on the new transition rules and updated urban development phases; these included new factors such as soil fertility, land price and distance to amenities, which directly influence transitions between sub-categories of urbanrural construction land and surroundings were chosen in order to increase prediction accuracy of the model; (3) quantify the model goodness of fit using confusion matrices, spatial pattern metrics and errors in quantity of change; and (4) provide some implications for land use management and urban land planning.

lands into impervious surfaces that increase runoff (Grimm, Grove, Pickett, & Redman, 2000; Li, Zhou, & Ouyang, 2013). Poor land use management may also lead to soil degradation, reduce green spaces, increase land fragmentation and reduce air and water quality (DeFries, Rudel, Uriarte, & Hansen, 2010; Rojas, Pino, Basnou, & Vivanco, 2013). Converting from farmland to urban areas threatens food security (Jiang, Deng, & Seto, 2012; Song, Pijanowski, & Tayyebi, 2015). Excess urban expansion of the city may cause the loss of natural areas for wildlife and also increase the management costs of urban (Frenkel & Ashkenazi, 2008). Despite these negative effects, urbanization also fosters economic growth for a nation and its people (Ameen & Mourshed, 2017; Seto, Fragkias, Güneralp, & Reilly, 2011). As cities all over the world continue to expand, many are doing so at unprecedented rates (Jiao, 2015). Land use transitions triggered by urban development have gained much attention of the public and by the research community. China has the largest population of any nation in the world with urbanization being very unique due to a variety of land use policies (Luo & Wei, 2009; Wang, Krstikj, & Koura, 2017). Indeed, urban development policies in China have several distinctive features. First, China's new urbanization policy recognizes that urban and rural infrastructure integration and equalization needs to be designed to sustain public services and to promote economic and social development in order to achieve a common prosperity; land changes should also not be at the expense of food production, ecology and the environment (Long et al., 2018). Thus, to study the dynamics of such urban systems, it is necessary to focus on the transitions between urban areas and nonurban areas such as farmland, forestland and grassland (Xiao, Orford, & Webster, 2015). A second feature of urbanization policy in China is the simultaneous goal of rural restructuring (Tu and Long, 2017). In the context of China's new urbanization, rural settlement pattern and the geographical layout of all land cover types becomes very important to all land use plans (Long, Tu, Ge, Li, & Liu, 2016). In particular, land types transitions among “special” sub-categories of urban-rural construction land such as urban construction land, rural construction land and other construction land, are distinguished from conventional sub-categories of urban-rural construction land like commercial, industrial and residential areas, which is common in the land use planning literature (Porta et al., 2009). Predicting land use change models is one possible approach to quantifying urban change for policy purposes (Gong, Hu, Chen, Liu, & Wang, 2018; Wu et al., 2006; Zheng et al., 2012). Land change models have been widely used, for instance, to predict urban expansion (Gong, Chen, Liu, & Wang, 2014; Omrani, Tayyebi, & Pijanowski, 2017; Verburg, Koning, Kok, Veldkamp, & Bouma, 1999). Indeed, dozens of land change models have been developed over the last 30 years using common simulation tools, some of which are coupled to environmental and economic models (Orford, 2002). Of note are vast array of cellular automata (CA) based models (Batty, Xie, & Sun, 1999; Mustafa et al., 2018a; White & Engelen, 1997). The SLEUTH model of Clarke, for example, has been used extensively to forecast urban development (Clarke & Gaydos, 1998; Jantz, Goetz, Donato, & Claggett, 2010; Onsted & Clarke, 2011). Artificial neural networks (ANNs) based land change models have also been successfully employed. The Land Transformation Model (LTM) of Pijanowski has been used to simulate urban expansion in the US, Europe, and Asia, agricultural expansion in Africa, with model output often coupled to hydrologic, climate, stream biodiversity and food production models (Moore et al., 2012; Pijanowski & Robinson, 2011; Washington-Ottombre et al., 2010). Other popular LUC simulation tools include the Conversion of Land Use and its Effects or CLUE model (Veldkamp & Fresco, 1996), the Agent-based Modeling (ABM) (Hosseinali, Alesheikh, & Nourian, 2013; Mustafa, Cools, Saadi, & Teller, 2017), the California Urban Futures model (Landis, 1995), DELTA (Simmonds, 1999), and UrbanSim (Waddell, 2002). All of these models use a variety of statistical or data mining tools to simulate LUC between two or more time periods and several of them have been

2. Study area and data sources 2.1. Study area The Zhanggong District of China is located in the southern part of the city of Ganzhou (Fig. 1), which is the most developed area in the city. The total area of Zhanggong district is 446 square kilometers. By the end of 2015, the population amount to 558 000 people and gross product reached 27.57 billion yuan. Zhanggong District is a low hilly area, with the southeast and northwest areas composed of significant hills and mountains; the central part, however, is a flat valley. The minimum and maximum elevations of this District are 57 m and 963 m respectively, and land with a slope of less than 10° accounts for 82.37%. The Zhanggong District is the direct hinterland of the Pearl River Delta and the Fujian Triangle, and an important channel connected inland route to the southeast coast. The location condition of Zhanggong District is unique. Since administrative division reform of the district, the process of industrialization has accelerated significantly. The development of urban and the improvement of urban functions have brought about significant changes in land use. Predicting multiple land use transitions under rapid urbanization of Zhanggong District could be definitely benefit the optimized allocation of land resources and sustainable development of urban and the regional economy. 2.2. Data sources We focused on analyzing and developing a model using data from 2005 to 2015 at five-year intervals. The land use data of 2005, 2010 and 2015 were rectified based on maps from Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC). According to these land use maps, the entire Zhanggong District was divided into farmland, forest, grass, urban construction land, rural construction land, other construction land, water and unused areas. All land use maps were stored as raster GIS data at a resolution of 100 × 100 m. Most of the predictor variables are 2005,2010 and 2015 except the soil fertility and land price data. The soil fertility data and relative changes in land prices in different areas of Zhanggong District are very limited, so soil fertility and land price data of 2015 are used in the paper. Soil fertility was extracted from the soil distribution map of RESDC (The higher value the better soil fertility), and land price was calculated based on the real estate price derived from the social survey 2

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Fig. 1. Location of Zhanggong district. Table 1 Summary of the sub-categories of urban-rural construction land predictor variables. Sub-categories of urban-rural construction land

Description

Urban construction land Rural construction land Other construction land

Refers to large, medium and small cities and other areas above county and town level. Refers to the independence of rural settlements outside the town. Refers to factories and mines, large industrial areas, oil, salt, quarry and traffic roads, airports and special sites.

Variable names

Description

Soil fertility Land price National road distance Railway distance Highway distance Ordinary road distance School distance Hospital distance Large market distance Water distance Park distance

The fertility level of the soil, higher rank means less transition to urban areas The land price of every cell, higher price means less likely to change to lower price areas Distance to the nearest national roads Distance to the nearest railways Distance to the nearest highways Distance to the nearest ordinary roads Distance to the nearest schools Distance to the nearest hospitals Distance to the nearest large markets Distance to the nearest rivers and streams Distance to the nearest parks

3. Methods

in real estate development companies and real estate agencies (The higher rank the lower land price, the value of 0 belongs to water). The transportation network GIS files (national road, railway, highway and ordinary road) were obtained from ETM images and Google Earth. The locations of public schools, hospitals above the second level, open parks (except parks within the communities) and large markets were interpreted from Google maps, Google Earth and web portals of the Chinese government (Table 1).

3.1. Data processing Land use maps for 2005 and 2010 were assembled from the above sources and placed in our GIS system. In this paper, an array of binary codes was employed to describe different land use types. For example, “10000000” codes for urban construction land and “00001000” contains the code for farmland (Table 2). We selected 11 factors as model predictor variables (Table 1, Fig. 2): soil fertility and land price represent physical and economic attributes of land resources, respectively. Land resources with higher soil fertility in China are always 3

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Table 2 Binary code of land use classes in Zhanggong District. land use classes

Binary code

Urban areas

Non-urban areas

Urban construction land

Rural construction land

Other construction land

Grass

Farmland

Forest

Water

Unused areas

1 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0

0 0 1 0 0 0 0 0

0 0 0 1 0 0 0 0

0 0 0 0 1 0 0 0

0 0 0 0 0 1 0 0

0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 1

weights achieved through training) and land use inputs from 2010. The model goodness of fit was assessed by comparing simulated and observed maps in 2015. We replicated this process until we reached convergence. Last, we used optimal weights to generate land use maps for 2020, 2025 and 2030 (We assume drivers are stable after 2015 because we did not forecast drivers in 2020 and 2025; we believe that these essentially remain the same is a reasonable assumption). 3) Test the network with “partially reversible” transition rules: We compared the simulation results with observed data in 2015 to quantify the accuracy of the ANNs land change model. We trained and tested 10 times to generate refined weights. This replication (i.e., training and testing procedure) was conducted to reduce the selection randomness (i.e., sampling bias) of a particular partition for learning and testing processes (Omrani, Charif, Gerber, Awasthi, & Trigano, 2013). 4) We used the optimal network (i.e., optimal weights) from the training of 2005–2010 time steps and then predictor variables in a base year (e.g., 2020) to produce land use maps in subsequent time steps (e.g., 2025). The result is a probability map where each cell has a value between 0 and 1 for each transition into land use class. The final land use class assignment is based on the largest value of suitability; for example, output values of 0.5,0.1,0.05,0,0,0.05,0.2,0.1 means the cells gets assigned to class 1 (urban construction land = 10000000).

treated as reserved agricultural resources. Thus, it is possible that higher soil fertility of land use became an impediment for any subsequent urban development. The land price represents land value condition (thus is considered a high transition cost by developers), and therefore there is a very small possibility for the city core area with high land value to become an industrial area, which always is set to a lower price. Soil fertility indicators include organic matter, pH, total nitrogen, total phosphorus and total potassium. The soil fertility level map was obtained by membership function (Mokarram & Hojati, 2017). Land price were estimated according to real estate price investigated from real estate development companies and real estate agencies. Land price level map was generated based on the reversion of real estate price and the natural breakpoint method. The minimum Euclidean distance to each feature of urban expansion was also calculated. The following urban expansion inputs were used in our model: (1) soil fertility level (2) land price level as it is known to affect urban development significantly, (3) distance to national road, (4) distance to railway, (5) distance to highway, and (6) distance to ordinary road. Distance from urban amenities were also considered as input variables to the LUC model, these included distance to (1) schools, (2) hospitals, (3) large markets, (4) water, and (5) parks. These urban amenities were hypothesized to be in great demand for residential use. All model predictor variables were normalized across the map with values between 0 and 1, and then converted into ASCII representations, which are the required format of our model (Chang, 2011; Mustafa, Rienow, Saadi, Cools, & Teller, 2018b).

3.3. Temporal and spatial analysis of land change 3.2. Model parameterization Based on the land use transition matrix and landscape pattern metrics, we analyzed temporal and spatial pattern of land change from 2005 to 2015 in Zhanggong District. Landscape pattern metrics quantify spatial configurations of land use/cover. We used the FRAGSTATS 4.2 spatial pattern analysis program (Andre Botequilha Leitao, Jack, & McGarigal, 2012) to calculate several landscape metrics to characterize urban form and urban expansion patterns (Arribas-Bel, Nijkamp, & Scholten, 2011; Herold, Couclelis, & Clarke, 2005). FRAGSTATS metrics that we report on here are:

Our model uses artificial neural networks (ANNs) and we follow the general procedures of LTM (Pijanowski, Shellito, Pithadia, & Alexandridis, 2002). The land change model was employed to predict the land use change of this study (Fig. 3 and Fig. 4). We employed the following four phases for model development and prediction (Fig. 5): 1) Network design: The input layer included binary codes of eight land use classes and 11 predictor variables calculated using Eq. (1) above for a total of 19 input nodes. We also configured the ANNs to have 8 output nodes, each identified as a binary code. All the values of nodes belong to (0, 1), the position of ‘1’ among 8 nodes determined the land use classes of output. For the “optional” number of nodes in the hidden layer, we varied the number of nodes between 1 and 32, considered convergence accuracy and speed, and the occurrence of overtraining based on a trial-and-error approach (Omrani, 2015). We determined that 16 hidden nodes in the hidden layer was optimal. 2) Train the network: The training and testing procedure of the network is shown in Fig. 5. It comprises four steps. First, we trained the ANNs algorithm using inputs in 2005 and output in 2010 assigning random weights at the start of training. Second, we predicted the simulated map in 2015 using network parameters (i.e., optimal

(1) (2) (3) (4) (5) (6) (7) (8) (9)

class area (CA), number of patches (NP), edge density (ED), landscape shape index (LSI), largest patch index (LPI), mean fractal dimension index (FRAC_MN), mean contiguity index (CONTIG_MN), and contagion index (CONTAG), Shannon's diversity index (SHDI).

These nine FRAGSTATS metrics, their descriptions, units, and range of values are summarized in Table 3.

4

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Fig. 2. Maps of predictor variables used as ANNs inputs.

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Fig. 3. Structure of ANNs model.

Fig. 4. Data processing and forecasting. 6

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Fig. 5. Training and testing procedures.

composed almost solely of farmland (75.91%) and forest (23.91%). Most of transitions were other classes transfer to urban construction land (60.78%) and other construction land (26.5%). Grass remained stable between 2005 and 2015. The transitions within urban including transitions from rural construction land and other construction land to urban construction land, as well as mutual transitions between rural construction land and other construction land. Influenced by the city development strategies, industry of this area developed very rapidly and as a result urban expanded significantly. Landscape metrics of observed maps at different time periods have relatively similar trends of increasing fragmentation (Fig. 6). The number of patches (NP) increased from 2005 to 2010, and then slightly decreased in 2015. The landscape shape index (LSI) reflects the complexity of land use patches, increased from 2005 to 2010 and then decreased in 2015, suggesting that the patch shapes become more complex especially between 2005 and 2010. The decreasing CONTAG indicates more scatter of similar land use patches of three years, while the increasing SHDI – which represents the amount of diversity of land use patches-suggests the arrangement of land use patches is becoming more diverse over time. The area and number of farmland patches have decreased while the area of the forest has increased, which is reflective that “return the farmland to forest” policy existed according to the land use laws of China during this period. FRAGSTATS metrics calculated for each land use class provides details of spatial patterns of change. Based on the increase of NP, LSI and FRAC_MN, urban expansion in Zhanggong District over 2005–2015 was characterized by disperse and complex sprawl of the urban patches (Fig. 7). The increasing ED, LSI and FRAC_MN indicates an increase in the fragmentation and complexity of the rural construction land. The

3.4. Validation of the model For validating the simulation, we measured the goodness-of-fit of the model in three ways. First, we created confusion matrices of observed and simulated land uses in 2015 and compared all land uses between these two maps at each position to quantify location errors (Kok, Farrow, Veldkamp, & Verburg, 2001; Pontius, Huffaker, & Denman, 2004; Pontius, Thontteh, & Chen, 2008; Rgjr, Cornell, & Cas, 2001). A second measure of model goodness-of-fit focused on quantity of errors of each land use classes between observed and simulated maps in 2015. We used area under the receiver operating characteristic curve (AUC) to quantify location and quantity errors (Pijanowski, Brown, Shellito, & Manik, 2002; Tayyebi, Pijanowski, & Tayyebi, 2011). Our third model accuracy focused on comparing spatial patterns of land use patch between observed and simulated maps in 2015, as described in 3.3 above. 4. Results 4.1. Temporal and spatial analysis of land change We found that the most common land use transitions between 2005 and 2015 were from farmland to either urban construction land or other construction land, which made up 46.14% and 18.1% of all the land use transitions, respectively (Table 4). The second common set of transitions was forest converted to urban construction land and to other construction land (14.6% and 8.33%, respectively). Other common transitions included farmland to forest (7.63%) and farmland to rural construction land (4.05%). Sources of land use transitions were Table 3 Description of metrics used in this study. Metrics

Abbreviation

Description

Units

Range

CA NP ED LSI LPI

Class area Number of patches Edge density Landscape shape index Largest patch index

Hectares N/A Meters per Hectare N/A Percent

CA > 0, no limit NP ≥ 1, no limit ED ≥ 0, no limit LSI≥1, no limit 0 < LPI≤100

FRAC_MN

Mean fractal dimension index

A measure of landscape composition A simple measure of the extent of subdivision or fragmentation of the patch type A measure of the degree of landscape fragmentation A simple measure of class aggregation or clumpiness LPI equals the area of the largest patch of the corresponding patch type divided by total area covered by land type Indicating the complexity of the shape, the complexity varies with the value.

N/A

1 ≤ FRAC≤2

CONTIG_MN CONTAG SHDI

Mean contiguity Index Contagion index Shannon's diversity index

A measure of spatial connectedness, or contiguity Indicating the distribution of patches A measure of diversity

N/A Percent Information

1 ≤ CONTIG≤100 0 < CONTAG ≤100 SHDI ≥0

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Table 4 Land use transition matrix of land use classes in Zhanggong District. 2005

2015

FA F G W UC RC OC U Total (%)

FA

F

G

W

UC

RC

OC

U

Total (%)

– 7.63 0.00 0.00 46.14 4.05 18.10 0.00 75.91

0.24 – 0.00 0.00 14.60 0.73 8.33 0.00 23.91

0.00 0.00 – 0.00 0.00 0.00 0.00 0.00 0.00

0.02 0.00 0.00 – 0.00 0.00 0.00 0.00 0.02

0.00 0.00 0.00 0.00 – 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.02 – 0.06 0.00 0.08

0.00 0.00 0.00 0.00 0.02 0.06 – 0.00 0.08

0.00 0.00 0.00 0.00 0.00 0.00 0.00 – 0.00

0.26 7.63 0.00 0.00 60.78 4.83 26.50 0.00 100.00

Note: Farmland-FA; Forest-F; Grass-G; Water-W; Urban construction land-UC; Rural construction land-RC; Other construction land-OC; Unused areas-U.

The diagonal values of the confusion matrix of Table 5A contains a summary of the correctly predicted percent number of cells in the map whereas Table 5B summarizes the total number of cells in the confusion matrix. The model performed relatively well to simulate most of the land use classes such as farmland, forest, grass, urban construction land and unused areas in 2015 (the diagonal values in Table 5B are all around 95%: 96.61%, 95.51%, 98.76%, 94.55% and 100%). In general, the observed map is very close to the predicted map (Tables 5A and 5B). However, the diagonal value of the rural construction land and other construction land are significantly smaller than these other classes at 72.84% and 60.31% (Table 5A) which indicates these were more difficult to predict based on our model drivers. In general, the model overpredicted farmland and converted these to rural construction land or other constructed urban in about equal amounts (25.67% and 25.31%). Farmland to urban construction land use was also a common error of the model. Other construction land was predicted, in error, across three very diverse land uses: farmland, forest, and urban construction land. The most abundant error in our model (Fig. 9B) is the observed farmland that predicted urban construction land class. Nearly 1.3% of our error map was in this error transition category. This means that farmland was observed in 2015 but the model predicted urban

increase of LPI of the urban construction land suggests that the urban expansion was the dominant characteristic of the LUCs in Zhanggong District. 4.2. Validation of the model 4.2.1. Quantify location errors We created an observed and simulated 2015 map for the study area (Fig. 8) and used these maps to quantify location and quantity errors and to produce a map of transition error types. Note that the simulated map of the urban classes shows that the model predicts more city growth in the northwest than what was observed. More city growth also occurred in the central portion of the large urban patch and the northwest portion of that core urban area as well. Fig. 9A provides a clearer illustration of location errors. Locations in yellow contain areas where the observed maps were farmland but one of the classes of urban was predicted to be there. Cells in red or orange indicate urban false negative errors; these are location where urban was present in 2015 but the model failed to predict their transitions. Several transition errors for forest are also shown and appear to be scattered throughout the southeastern central and northeastern portion of the study area.

Fig. 6. Landscape shape and fragmentation indices over time (Note: Ob represents observed map, Si represents simulated map). 8

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Fig. 7. Landscape metrics for land use classes over time.

and urban construction land use classes, predicted number of patches varied greatly than any of the observed land use maps (2005, 2010 and 2015). Farmland, forest and other construction land were, however, similar between observed 2015 and simulated 2015. Values for LSI, LPI, ED, FRAC-MN and CONTIG_MN all suggest that the shape of grass and urban construction land patches were difficult to predict well compared to the other classes. These two classes are also a rare transition type as well.

construction land. The results of the multi-class areas under the curve (AUC) were 61.6% and 92.5% using the set of changed cells and the entire dataset (with change and non-change cells), respectively (this is done using the multiclass.roc function in R - pROC library). 4.2.2. Landscape metrics of observed and simulated LUCs The landscape metrics of the simulated map (2015) possesses some interesting differences and similarities in several ways with the 2015 observed map (Fig. 6). First, for number of patches, landscape shape indicator, contagion and Shannon's diversity index, simulated 2015 was more similar to observed 2015 than observed 2005 or 2010 land use maps. However, when these landscape shape metrics are examined by land use class, large differences are seen (Fig. 7). For example, for grass

4.3. Prediction of LUCs Although uncertainty is an inherent feature of any land use change forecast (Mustafa, Saadi, Cools, & Teller, 2018c), some studies did not 9

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Fig. 8. Observed and simulated maps for 2015.

land use resources frugally. For the sustainable land use in Zhanggong District, the land control must be enforced strictly. On one hand, we argue for a strengthened regulation of the land market by government, especially on the farmland protection. On the other hand, we believe that planning officials should guide the spatial distribution of land supply actively. We also predict that the urban expansion may slow down in the near future, especially after 2025 (Fig. 10). The potential reason may be the limited land supply for urban expansion. The terrain of Zhanggong District may constrain the growth as there is limited space for urban expansion in the southeast and northwest of the Zhanggong District. Raising the land use efficiency of existing urban areas and optimizing the structure and functions of urban land use are effective ways to reduce the pressure on urban land in Zhanggong District fundamentally. It will also be the focus of future land management and urban development in Zhanggong District. Intensive and effective land use mode was the new style urban expansion mode presented by the China government. Promoting the utilization potentiality of existing urban land use, arrange all types of land scientifically and rationally will be an effective way to reduce land supply pressure of the Zhanggong District. Furthermore, we believe that to raise the quality of life for urban residents, such as more outdoor living space (access to park or green space) with improved servicing infrastructure (access to the medical establishment and insurance service) should also be considered as well. In a word, improving the production, living and ecological functions of land resources are the key points to optimize urban land use functions and the allocation of land resources.

consider uncertainty in the future simulations according to a scientific point of view holds that uncertainty is irrelevant to scenario-based analysis because storylines are not predictive (Pontius & Neeti, 2010). Accordingly, we did not consider uncertainty in our simulated maps of Zhangong District for 2020, 2025 and 2030 (see Fig. 10). These maps show that the future expansion of this District is west and south, and the urban expansion will slow down after 2025, which are consistent with the city's strategy “expand in the south and west; extend in the east; limited spread in the north”. 5. Implications for land use management and urban planning In reference to the simulated maps of Zhanggong district, the transitions from farmland and forest to urban are still the main pathway now as well as the near future. Researches of land use transitions and urban planning management will still focus on coordinating economicsocial development (urbanization) and farmland resource protection (non-agricultural construction land conversion) in Zhanggong District in the future (Long & Qu, 2018; Wu, Hui, Zhao, & Long, 2018). Zhanggong District is the central city of Ganzhou City and also one of the three metropolitan areas supported with special measures by Jiangxi Province. It is likely that the increased demand for industrial restructure and development due to the rapid economic development and urbanization is the primary cause of the of urban land expansion. According to the transition matrix between 2005 and 2015, other construction land increased greatly during the 10 years (Table 4). The quantity and the land area of industrial parks and quarry also increased substantially and the position and areas manifest intensely randomly (Jiangxi Morris Magnetic Energy Technology Co., Ltd founded in 2010; the fourth division of China Railway 21 Bureau Chang founded in 2012). The farmland and forest are the most common candidates for urban expansion. For protecting the farmland and forest land effectively, there needs to be a strict control of the excessive expansion of urban land to utilize

6. Discussion The simulation results show that ANNs can produce the expected results since the simulated map in 2015 possesses many similarities to the observed map in 2015 with several measures for goodness of fit10

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Table 5B Number of correctly (diagonal) and incorrectly (non-diagonal) predicted cells for each transition organized as a confusion matrix between observed and simulated land use for 2015. Simulated land uses in 2015

Observed land uses in 2015

FA F G W UC RC OC U

FA

F

G

W

UC

RC

OC

U

Total

17646 325 6 0 361 86 410 0

0 12557 0 0 21 1 56 0

0 0 479 0 0 0 0 0

3 0 0 2976 0 0 0 0

567 219 0 0 6882 4 177 0

4 2 0 0 0 244 0 0

45 44 0 0 15 0 977 0

0 0 0 0 0 0 0 9

18265 13147 485 2976 7279 335 1620 9

Note: Farmland-FA; Forest-F; Grass-G; Water-W; Urban construction land-UC; Rural construction land-RC; Other construction land-OC; Unused areas-U.

Fig. 9B. Quantity of errors, in percent, between observed and simulated map in 2015.

Fig. 9A. Error map of land use in 2015 (Note: FA-UC means observed classes is farmland but model predicts it to be urban construction land).

on per capita land use requirements. The purpose of “Combining multiple laws and regulations” in China is to establish a sound and unified space planning system and improve the capacity and efficiency of national land and space governance. It refers to the integration of multiple plans into one region to realize a city-county plan and a blueprint. The goals of urban planning should be aligned with land use planning (Chen, Chen, Xu, & Tian, 2016). There is a mutual feedback mechanism between land use transitions and land use management (Long & Qu, 2018). Land use transitions are restricted by policies and measures. At the same time, the policy-makers need to adjust the land management policy and urban planning timely according to the land use transitions (Lu & Ke, 2017; Tu, Long, Zhang, Ge, & Qu, 2018). Taking into account the current and future land use transitions will enhance the accuracy and timeliness of land use policy making and urban land planning. In summary, the quantity and spatial pattern of land use changes simulated by the model will benefit the land

location, quantity and spatial configuration-close between observed and simulated land change. Error maps also illustrate interesting differences between the two maps (Fig. 9A and B) with the sizes of transition patches and spatial distribution evidently. This present model differs in several respects from the previous ANNs models of Pijanowski and Tayyebi (Moore et al., 2007; Tayyebi & Pijanowski, 2014). First, the ANNs was configured here to allow for a host of possible transitions, previous version often include one output node. Second, transition threshold rules different from that presented by Pijanowski; here, the transition with the largest output value is selected as the candidate land use at that time step. Previously, Pijanowski (Pijanowski Shellito et al., 2002; Pijanowski Brown et al, 2002) used a principal index driver (PID) to determine the number of cells transitioning and a maximum likelihood rule was used to select only those cells with the largest output values in the correct quantity based Table 5A Confusion matrix: true predicted percent for each of the simulated transitions. Simulated land uses in 2015

Observed land uses in 2015

FA F G W UC RC OC U

FA

F

G

W

UC

RC

OC

U

Total

96.61 2.47 1.24 0.00 4.96 25.67 25.31 0.00

0.00 95.51 0.00 0.00 0.29 0.30 3.46 0.00

0.00 0.00 98.76 0.00 0.00 0.00 0.00 0.00

0.02 0.00 0.00 100.00 0.00 0.00 0.00 0.00

3.10 1.67 0.00 0.00 94.55 1.19 10.93 0.00

0.02 0.02 0.00 0.00 0.00 72.84 0.00 0.00

0.25 0.34 0.00 0.00 0.21 0.00 60.31 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00

100% 100% 100% 100% 100% 100% 100% 100%

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Fig. 10. Simulated maps in 2020, 2025 and 2030.

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use management and urban planning definitely.

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7. Conclusions Land use changes (LUCs) driven by urban expansion occur everywhere. A wide variety of modeling approaches have been applied to predict LUCs and urban expansion. This paper presents a model combining GIS with ANNs to predict multiple land use transitions and subcategories of urban-rural construction land in Zhanggong District of Ganzhou city in China, which has undergone profound rates of urban expansion in recent decades due to administrative region adjustment. This present model differs in several aspects from the previous ANN models and the simulation results show that the model can produce the expected results since the simulated map in 2015 possesses many similarities to the observed map in 2015 with several measures for goodness of fit-location, quantity and spatial configuration-close between observed and simulated land change. The model results show that farmland and forest are the most common candidates for urban expansion, and the urban expansion will slow down after 2025. The goals of urban planning should be aligned with land use planning. There is a mutual feedback mechanism between land use transitions and land use management. Taking into account the current and future land use transitions will enhance the accuracy and timeliness of land use policy making and urban land planning. For the sustainable land use in Zhanggong District, we argue for a strengthened regulation of the land market by government and believe that planning officials should guide the spatial distribution of land supply actively. Furthermore, improving the production, living and ecological functions of land resources are the key points to optimize urban land use functions and the allocation of land resources. We discuss how our improved model with advantages of simultaneously simulating multiple land use classes and specific land use subclasses can be adapted to other study areas to benefit land use management and urban planning in China. Acknowledgements This work was supported by Jilin Province Science and Technology Development Plan Project (Grant No. 20180418111FG); Jilin Provincial Department of Education "13th Five-Year" Science and Technology Project (Grant No. JJKH20180163KJ); Key Program of National Natural Science Foundation of China (Grant No. 41731286);The last author was supported by the Major Science and Technology Program for Water Pollution Control and Treatment (Grant No.2010ZX07320-003004);Jilin Science Foundation for Excellent Young Scholars (Grant No.20180520169JH); The land use data and soil fertility data were provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn). References Ameen, R. F. M., & Mourshed, M. (2017). Urban environmental challenges in developing countries—a stakeholder perspective. Habitat International, 64, 1–10. Andre Botequilha Leitao, J. M., Jack, Ahern, & McGarigal, Kevin (2012). Measuring landscapes: A planner's handbook. Angel, S., Parent, J., Civco, D. L., Blei, A., & Potere, D. (2011). The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Progress in Planning, 75(2), 53–107. Arribas-Bel, D., Nijkamp, P., & Scholten, H. (2011). Multidimensional urban sprawl in Europe: A self-organizing map approach. Computers, Environment and Urban Systems, 35(4), 263–275. Batty, M., Xie, Y., & Sun, Z. (1999). Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3), 205–233. Chang, C.-C. (2011). LIBSVM : A library for support vector machines. 1–27:27 ACM Transactions on Intelligent Systems and Technology, 2(27) http://www.csie.ntu.edu.tw/ 〜cjlin/libsvm, 2. Charif, O., Omrani, H., Abdallah, F., & Pijanowski, B. (2017). A multi-label cellular automata model for land change simulation. Transactions in GIS, 21(6), 1298–1320. Chen, S., & Chen, B. (2015). Urban energy consumption: Different insights from energy flow analysis, input–output analysis and ecological network analysis. Applied Energy, 138(C), 99–107.

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