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Sensors and Materials, Vol. 35, No. 2 (2023) 965–974
MYU Tokyo
S & M 3221

Data Mining of National Geographical Census
for Decision-making in Urban Planning:
A Geo-simulation of Urban Size in Beijing, China
Miao Wang,1,2,3 Meizi Yang,4 Xu-dong Yang,1,2 Juan Chen,1,2 and Bogang Yang1,2*
1Beijing Institute of Surveying and Mapping, Beijing 100038, China

2Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China

3State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Scienceand Geospatial Information Technology of MNR, CASM, Beijing 100036, China

4Beijing University of Civil Engineering and Architecture, Beijing 100044, China(Received November 7, 2022; accepted January 24, 2023)

Keywords: boundary calculation, construction land, cellular automaton, machine learning, integrated
learning

With the development of the census and monitoring of national geographical conditions in
China, the availability of information has sharply increased. Progress in data mining methods
and social application tools has provided a way for solving the problems of low resource
allocation and high uncertainty in decision-making regarding planning. To relieve non-capitalfunctions and serve the healthy development of the Beijing Metropolitan Area, we propose a newmodel of self-adaptive cellular automaton based on ensemble learning (EL-CA). The method isbased on the data collected by monitoring geographical conditions and is guided by complex
geocomputing that simulates city-scale evolution in Beijing. A comparison of predicted and real
data for Beijing in 2015 demonstrated that the predictions made by the EL-CA model proposed
significantly outperformed those by traditional cellular automaton (CA) models based on
empirical statistics. Data on the geographical conditions in Beijing in 2007 and 2015 were
employed in model simulation and training to predict the scale of the city in 2023. The urban
agglomeration points in Beijing tended to be dense, the overall construction land tended to besaturated, and the growth rate of land use areas slowed. Results from the model also establishedthat the construction land in Beijing is close to saturation from a quantitative perspective, andthe potential urban expansion hotspots in the future are mainly concentrated in the Tongzhou
District, the Daxing District, the Fangshan District, the south side of the fourth and fifth ringroads, and the southwest side of Pinggu District. These results can provide decision-makers inurban planning with supporting data and support Beijing to relieve Beijing of functions
nonessential to its role as China’s capital.

*Corresponding author: e-mail: bogangy@126.com

https://doi.org/10.18494/SAM4241

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Sensors and Materials, Vol. 35, No. 2 (2023)

Introduction

National geographical conditions comprehensively express the nature, economy, and humangeography at a country’s macro level, which is an important part of basic national conditions. A
survey of geographical conditions based on remote sensing technology is one of the main aspectsof work in monitoring land space in China in recent years. Remote sensing images are an
important data source for the survey of geographical conditions. Surveys of geographicalconditions use interpretation technology on remote sensing images to achieve rapid extraction
and accurate interpretation of geographical information. Monitoring national geographicalconditions involves the investigation and observation of status quo and evolution of national
geographical information. National conditions and national strength can be accurately andeffectively captured by research and statistics based on national geographical information in acertain space and over time. As a subset of national geographical conditions, urban geographicalconditions include natural, economic, and human geographic elements, such as urban area andlayout, urban topography and land use, population distribution and density, road traffic, andurban expansion. Monitoring of the geographical condition of cities includes two primary
aspects: important geographical information and evolutionary geographical information. The
former is visualized by carriers such as maps and images, whereas the latter is distributedorderly over time. The direction of future development can be analyzed and predicted byresearch on how regularly information changes with time.(1–2)
With rapid economic growth, urban population continues to grow, urban scale continues toexpand, and urban construction areas continue to increase. This has caused a series of social andecological problems such as traffic congestion, uneven distribution of social resources, anddeterioration of the ecological environment. The evolution of urban scale is affected by manythings, including geographical conditions, social economy, infrastructure, population, andnational policies and regulations. Therefore, the evolution of urban scale is highly complex. To
identify the rules of urban scale evolution, we must first explore the inner driving factors thatdeeply affect urban scale. After that, we can accurately predict future trends in urban scale
development and take effective measures to provide support for the planning and construction ofrational and sustainable cities.
The data acquired by the completion of the first census of national geographical conditions
provide an opportunity to study the evolution of urban scale over a large time span. We
investigated the national geographical information on urban construction land in Beijing. Bysummarizing the strengths and weaknesses of the former methods of research in urban-scale
simulation, urban cellular automaton (CA), a self-adaptive method of CA geographicalsimulation based on ensemble learning (EL), is proposed. We simulated the dynamic evolutionof urban agglomeration in Beijing under the influence of various policies and other unchanging
factors and forecast its future development. Our prediction highlights the key areas that needattention to alleviate Beijing urban agglomeration in the coming years. It also provides strong
support for Beijing to relieve Beijing of functions nonessential to its role as China’s capital and topromote the coordinated development of the Beijing Metropolitan Area.(3)Sensors and Materials, Vol. 35, No. 2 (2023)

2.

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Research Area and Data Sources

Beijing, the capital of China, is the center of politics, economy, culture, education, andinternational exchange. It is located between 115.7°–117.4° E and 39.4°–41.6° N, with its center at
39°54′20″ N and 116°25′29″ E, covering a total area of 16,410 square kilometers. It is adjacent to
Tianjin and surrounded by Hebei Province and Tianjin. For 30 years, both the urbanization of
Beijing and the influx of migrants have been accelerating. Before the mode of developmentchanged from “population influx” to “population outflux,” the land area in Beijing devoted toconstruction had been increasing year by year. To simulate Beijing’s size, we use the data fromthe first geographic national conditions and special monitoring of Beijing in 2015. The specificdata include:(1) Raster data: Grade Map Data of Beijing in 2015.
(2)	Vector data: Data on construction land in Beijing in 2007, 2011, and 2015; data on road
networks in Beijing (including urban roads, expressways, and railway networks); data on thewater system (including threadlike rivers, flat-faced lakes, and reservoirs); all levels of thedata on the urban center; and data on protected areas with restricted development.

3.

Methods

3.1

Geographical simulation

To compensate for deficiencies in the current modeling of geographical information systems,
geographical simulation systems have emerged. Their core lies in the establishment ofgeographical models, and simulation and prediction of complex geographical phenomena arecarried out through simulations and other research methods.(4,5) Key technologies of ageographical simulation system include CA and multi-agents. Among these, the cellular
automata method is widely used in the modeling of land use and coverage changes due to its highefficiency and simple assumptions. CA is based on the interaction among the cells in local
neighborhoods on the grid system, which simulates complex spatial phenomena. Newly
generated cell properties are derived from the cells of the previous moment through certain rulesof evolution. This bottom-up feature is consistent with the characteristics of urban scaleevolution. Therefore, cellular automata are widely used in simulations of urban scale. For
example, Li and Ye (2002) studied the expansion of urban land in Dongguan City with goodresults by combining artificial neural networks and other technical methods in the modeling.(6–12)
A significant disadvantage of the CA approach is that it does not introduce spatial elements
and driving forces in the simulation of land use and changes in coverage. To compensate for thisshortcoming, some researchers have improved the scientific nature of logical decision-making
by combining CA with empirical statistical models, including an ordinary least-squares
regression (OLs) model, a spatial autoregressive model, an analytic hierarchy model, a logistic
regression model, and a hierarchical multiple regression model. However, as parametric models,these empirical methods also have some drawbacks:

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Sensors and Materials, Vol. 35, No. 2 (2023)(1)	They rely too much on prior knowledge, but there is no consistent and comprehensive
knowledge about the processes of land use and changes in coverage.
(2)	They cannot process multi-channel data or identify nonlinear correlations. On the other hand,
land use and changes in coverage are affected by a series of complex and nonlinear factors.
(3)	They cannot process high-dimensional data, which may lead to over-fitting. The existence of
these drawbacks greatly reduces the versatility of the models when using the empiricalstatistical models for out-of-sample prediction. Therefore, it is necessary to find a morereliable and accurate method to replace CA based on the empirical statistical models.(13,14)
Machine learning techniques (such as neural networks, decision trees, random forests, andsupport vector machines) offer new possibilities for modeling land use and coverage on different
geographical scales. Machine learning techniques have the following advantages over empiricalstatistics:(1)	Stronger processing power for quantitative and qualitative high-dimensional data;(2) Computational efficiency to avoid overfitting;(3)	No need for strict parameter assumptions to make the description of the data more accurate
and reliable;(4) No strong assumptions are required, and nonlinear features can be processed.
Because of these advantages, some groundbreaking researchers have tried to improve cellular
automata by combining different machine learning methods,(15,16) but it is often difficult tochoose the “optimal” machine learning algorithm. In addition, there has not been any research
up to now on the systematic simulation of urban scale by combining cellular automata andmachine learning techniques.
3.2

EL

As an integrated approach, EL methods train multiple machine learning algorithms andintegrate their estimates to improve overall numerical prediction or classification accuracy. In
general, EL algorithms are superior to single machine learning algorithms because errors changewith different machine learning algorithms, but the combination of different machine learningalgorithms makes the results more accurate. Typical methods of EL are divided into two types:
bagging algorithms and stacking algorithms.(17–23)
The bagging algorithm is a technique to reduce generalization error by combining several
models. The main idea is to train several different models, and then let all models vote on the
output of test samples. This is an example of conventional strategies in machine learning, also
known as model averaging. The bagging algorithm is characterized by training each algorithm
in the machine learning algorithm with different subsets of training data, which can effectively
reduce the sensitivity of the machine learning algorithm, avoid the occurrence of over-fitting,and improve the overall generalization ability. Specifically, n times of repetitive sampling ofvariables in the original training samples are first carried out by random sampling to generate n
new sample sets. With these new sample sets, m machine learning algorithms are trained
separately. Then, the same variables are used as input to these algorithms to obtain m prediction
values, and the bagging algorithm finally obtains the integrated output results of the m prediction
values by simple voting or averaging methods.

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The stacking algorithm is also called the superposition method. First, m machine learningalgorithms are trained with the existing training sample sets. Second, the output values of the malgorithms are used as training samples, and the actual value of the variable is taken as the true
value. Finally, the original machine learning algorithm is superimposed onto a new machinelearning algorithm through a new training dataset. In the superposition method, the outputs ofthe previous machine learning algorithm are applied to the induction process of the subsequentalgorithm, so that the new machine learning algorithm can identify and correct the errors of theoriginal machine learning algorithm, find the best ensemble method of the machine learningalgorithms, and improve the accuracy of learning.
3.3

CA based on self-adaptive EL

We selected five machine learning algorithms commonly used in combination with cellular
automata to simulate land use and changes in coverage: support vector machine (SVM), radialbasis function neural network (RBF-NN), random forests (RFs), boosted tree regression (BTR),
and rough sets (RSs).
SVM is a generalized linear classifier that carries out binary classification of data according
to supervised learning. Its decision boundary is the maximum distance hyperplane for learning
samples. Finding a hyperplane (segmentation line) to segment the sample segmentation
maximizes the interval.
RBF-NN is an artificial neural network that uses a radial basis function as the activation
function. It is a three-layer forward network including an input layer, a hidden layer, and an
output layer. The transformation from input space to hidden space is nonlinear, while thetransformation from hidden space to output space is linear.
RFs refers to a classifier that uses multiple trees to train and predict samples. In fact, it is a
special bagging method, which uses the decision tree as the model in bagging.
BTR is a lifting method based on classification trees and regression trees. The decision tree isa binary classification tree for classification problems and a binary regression tree for regression
problems.
RSs is a mathematical tool for the quantitative analysis and treatment of imprecise,inconsistent, and incomplete information and knowledge. It is a two-dimensional table that uses
information tables to describe objects in the universe. Each row represents an object, and eachcolumn represents an attribute of the object. The degree of imprecision is described by theconcepts of lower approximation and upper approximation.
On the basis of the data from monitoring Beijing’s national geographical conditions in 2007,2011, and 2015, we extracted the predictive factors of urban construction land and urban scaleevolution in Beijing. The predictive factors can be divided into auxiliary factors and control
factors, among which the auxiliary factor data includes distance factors (distance from road –subdivided into distance from railway, highway, and expressway), distance from city (town)
center, distance to satellite center, distance to rivers, lakes or water systems, and topographic
factors (slope data). In view of the many cultural relics and historical protected areas in Beijing,
we selected protective areas with restricted development as the limiting factors. The predictive
factors and their sources are shown in Table 1.

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Table 1
Predictive factors and their sources.
Predictive Factors
Category
Distance Factors
Auxiliary Factors

Topographic Factors
Other Factors

Limiting Factors

Protective Areas with
Restricted Development

SourceDistance raster data calculated from vector data such as Beijingroad network, water system, and town center.
Slope raster data calculated from Beijing DEM data.
Other influencing factors based on the results of the census of
the geographical conditions of Beijing.
Converting vector data of the restricted development zones toraster data.

After converting the vector data for Beijing’s urban construction land in 2007, 2011, and 2015
into raster data, the CA model based on the five machine learning algorithms described wastrained using the self-adaptive approach, and the city scale of Beijing in 2023 was predicted. The
data for the first two years were used for model training, the data for the third year were used for
adjustment and calibration, and randomness was reduced by repeating 10-fold cross-validations
ten times. Specifically, the training dataset was divided into ten subsets, nine of which were used
for training, and the last one was used for testing. Then the bagging algorithm and the stacking
algorithm were used to generate a new CA model based on EL. These five machine learningalgorithms can generate many new superposition algorithms, which can use the Wilcoxon
signed-rank test to compare the saliency of the differences shown by different superposition
combinations and can adopt the superposition combination with the least number of algorithms
and the highest prediction accuracy as the final simulation method. The cellular automata model
algorithm was trained with real data from multiple years and was verified and calibrated with
one of the years. Randomness was reduced by repeated training and cross validation. Finally, thecombination with the highest predictive accuracy was found by the superposition of different
algorithms.
In this study, 240 sets of randomly selected samples were used to calculate the predictive
accuracy, and the best CA model based on EL was selected. To judge if the model were relatively
better than the traditional CA method based on the empirical statistical model, we further
compared the accuracy of the two predictions.

4.

Results

4.1

Comparison of EL methods and empirical statistical methods

We used various model methods to predict construction land in Beijing in 2015 and compare
it with the actual situation in 2015, as shown in Table 2. From the predictions, it can be seen thatthe accuracy of the machine learning algorithm was higher than that of the empirical statisticalmodel algorithm. At the same time, we also show the accuracy of predictions from the bagging
algorithm and stacking algorithm. The accuracy of each machine learning algorithm wassignificantly improved after being combined with the bagging algorithm, but it was still not as
good as that of the stacking algorithm. Among the methods listed, the highest predictive

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Table 2
Prediction by each model (%).
Prediction
Original Model
Accuracy
SVM
69.1

Bagging
Algorithm
SVM

Prediction
Accuracy
77.5

RBF-NN

65.5

RBF-NN

72.9

RFs

63.2

RFs

70.5

BTR

60.1

BTR

68.4

RSs
OLs
SAR

68.0
48.4
56.7

RSs

75.9

Stacking
Algorithm
SVM + RSs
SVM + RSs +RBF-NN
SVM + RSs +RBF-NN + RFs
SVM + RSs +RBF-NN + RFs +BTRPrediction
Accuracy
81.1
84.7
74.3
73.9

SAR: spatial auto regression

accuracy was achieved when the stacking algorithm superimposed the SVM, rough set, andRBF-NN, for which the prediction accuracy reached 84.7%.
4.2

Predicted Results

The land use for buildings in Beijing in 2007 and 2015 is shown in Fig. 1. Combining thiswith the results from the predictive models, we selected three algorithms to be superposed upon
the stacking algorithms, namely, SVM, RSs, and RBF-NN. On the basis of the data for Beijing’s
construction land in 2007 and 2015, the urban construction land was predicted for 2023. The
comparison of the construction land between the predicted results for 2015 and 2023 is shown in
Fig. 2.
From the perspective of spatial distribution, the overall extent of construction land in Beijing
has not increased significantly. The internal distribution tends to be dense, while the overall
contour is unchanged. The newly added construction land is mainly distributed on the edge ofexisting construction land. After excluding the influence of random points, there is no obvious
new point within the fourth ring road of Beijing, which shows that construction land in the
downtown area of Beijing has become saturated. New sites are primarily concentrated outside
the fourth and fifth ring roads, such as on the south side of the Tongzhou District, the Daxing
District, and the Fangshan District, and the southwest side of the Pinggu District, all of which
are close to Tianjin City and Hebei Province, as the key areas for relieving Beijing of functions
nonessential to its role as the capital. The construction land of the Huairou District, theChangping District, the Mentougou District, and the Fangshan District remains mainly
unchanged, which may be related to steep slopes of these areas and a large number of areas inwhich development was prohibited.
Urban construction land predicted in 2007, 2015, and 2023 was converted into raster datawith a cell size of 30 × 30. From the absolute value of the change in the area, there were 267539
newly added pixels in 2015 as compared to 2007, and 259854 predicted newly added pixels in972

Sensors and Materials, Vol. 35, No. 2 (2023)(a)Fig. 1.

Fig. 2.

(b)(Color) Urban construction land in Beijing in (a) 2007 and (b) 2015.

(Color) Comparison of predicted results with actual urban construction land in 2015.

2023 compared to 2015. The results show that, if policy and other factors remain unchanged, thegrowth rate of construction land in Beijing decreased with the increase in area, which also
indicates that construction land in Beijing is close to saturation due to slower growth in theovercrowded city. In the future of urban construction and planning, Beijing should strictly
control the quantity of new construction land, improve road traffic conditions in the city,
alleviate the uneven distribution of population, and protect the environment.
On the basis of the results from data on urban construction land in 2007, 2011, and 2015 in thegeneral survey of geographical conditions, we verify the use of remote sensing technology in thegeneral survey of geographical conditions and that it provides support for decision makers on
urban planning. Remote sensing technology not only greatly improves the speed of the census ofgeographical conditions but also improves the accuracy of the census.

Sensors and Materials, Vol. 35, No. 2 (2023)

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Conclusions

To study the evolution of Beijing’s urban scale and provide decision support for Beijing’s
policy of alleviating non-capital functions and promoting the coordinated development of
Beijing, Tianjin, and the Hebei Region, we summarized the experiences of predecessors using
CA to simulate the urban scale, analyzed the advantages and disadvantages of various methods,
and emphasized a CA model based on a machine learning algorithm and EL. Through theexamples in this paper, we verified that the method proposed is much better than the traditionalmodeling method based on empirical statistics in terms of the predictive accuracy of urban scale
simulation.
On the basis of the survey data of Beijing’s geographical conditions in 2007 and 2011 as the
input data, the 2015 predicted value was obtained. Comparing it with the actual value in 2015,
we selected the CA model with the highest predictive accuracy; it consisted of a stacking
algorithm superimposing SVM, RSs, and RBF-NN. Finally, the distance factor, slope factor,
limiting factor, and other factors were provided as input. According to the data of Beijing’s
construction land in 2007 and 2015, the scale of construction land in Beijing in 2023 waspredicted under the condition of unchangeable external factors, and the city scale of Beijing wasanalyzed.
The results show that the overall contour of Beijing’s construction land is basicallyunchanged, the internal agglomeration point tends to be dense, the overall construction land
tends to be saturated, and the growth rate of land area has slowed. To achieve orderly andsustainable urban development, the government needs to relieve Beijing of functions nonessential
to its role as the capital. In addition to the central urban area, key control areas for future
construction land include the south side of the Tongzhou District, the Daxing District, and theFangshan District, and the southwest side of the Pinggu District, all of which are close to Tianjin
and Hebei Province.
The experimental results in this paper fully demonstrate the flexibility and predictive
accuracy of the CA model based on the EL method in urban scale simulation, but there are also
deficiencies. In addition to the impact of the extracted impact factors from the monitoring of
national geographical conditions, the city scale is also affected to a certain extent by policy
formulation and other aspects. To further improve the accuracy of urban prediction, further
information needs to be obtained to provide more accurate references for decision makers.

Acknowledgments
The authors thank the State Key Laboratory of Geo-Information Engineering and the Key
Laboratory of Surveying and Mapping Science and Geospatial Information Technology of
MNR, CASM (No. 2022-04-05) and the Beijing Key Laboratory of Urban Spatial Information
Engineering (No. 20210102) for financial support for this study.

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