




































 | 23  
 

         Geoplanning 
Vol 3, No 1, 2016, 23-32                                                                                                                                                           Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

doi: 10.14710/geoplanning.3.1.23-32. 

LAND USE CHANGE IN SUBURBAN AREA: A CASE OF MALANG CITY,  
EAST JAVA PROVINCE 

S. N. Rukmanaa, I. Rudiartob 

a PGRI Adibuana University, Surabaya, Indonesia 
b Diponegoro University, Indonesia 

Abstract: The development of suburban areas of Malang City has developed an 

expansion of built-up areas between urban and suburban areas. There has been a great 

phenomenon that mostly occurs along the suburban areas where industrial activities 

took place. This study aims to determine what factors have influenced the land use 

change in the suburban areas of Malang City by employing “GeoDa” application. It is 

one of the Geographical Information System applications that particularly deals with 

statistical analysis. To achieve this purpose, the objectives are: delineating the study 

area, analyzing the characteristics of land use change, assessing and analyzing the 

variable influencing the land use change. The results have shown that the 

characteristics of land use change, such as population, distance, migration, and 

occupation transformation are directly proportional to the land use change. It has also 

been identified that the high level of density is only located in the surrounding areas of 

industries. From the assessed variables through the statistical model, population (X1), 

density (X2) and migration (X3) are found as the influencing factors of land use change. 

 
 

Copyright © 2016 GJGP-UNDIP  
This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

 
How to cite (APA 6th Style): 
Rukmana, S. N. & Rudiarto, I. (2016). Land use change in suburban area: a case of Malang City, East Java Province. Geoplanning: Journal of 
Geomatics and Planning, 3(1), 23-32. doi:10.14710/geoplanning.3.1.23-32 

 

1. INTRODUCTION 

Land use change in peri-urban area is one of the impacts of urbanization process where urban activities 
expand (Arta & Pigawati, 2015; Ives & Kendal, 2013; Paül & McKenzie, 2013; Shi, Sun, Zhu, Li, & Mei, 2012). 
This issue has become one of the most interesting subjects to be studied particularly in developing 
countries. One of the prominent factors in land use change is population growth. UN-HABITAT (2005) 
reported that in 1950 Asia’s urban population was about 232 million or about 17% of the total population, 
and in 2005 it increased up to 40%.  The distribution of population within and between cities, regions and 
nations is influenced further by certain migration patterns because the city as a generator region provides 
complete facilities, vacancies, and other attracting forces such as industry, education, etc. Therefore, the 
high rate of migration living in urban area can lead to uncontrolled land needs, and it will in turn affect the 
land use change in the future. 

In the context of land use change, many of it were caused by urban development located in suburban 
areas. As  Webster & Muller (2009) mentioned, peri-urban zone begins just beyond the contiguous built up 
area and sometimes extends as far as 150 km from the core city. It has positive and negative impacts. One 
of the positive impacts is the creation of new job for people living in suburban areas, for example as 
industrial labors. On the other hand, the negative impacts can be related to the quality of land use in terms 
of the economic aspect (Phuc, Van Westen, & Zoomers, 2014; Zhang et al., 2015). It means that when the 
quality of agriculture decreases, the tendency of farmers to sell their agriculture land is high (Kangalawe, 
Christiansson, & Östberg, 2008). Through the process of land conversion, agricultural land is converted into 

Article Info: 
Received: 21 March 2016 
In revised form: 1 April 2016 
Accepted: 25 April 2016 
Available Online: 30 April 2016 
 

Keywords:  
Landuse change, suburban 
area,spatial regression, Malang 
 

Corresponding Author: 
Siti Nuurlaily Rukmana 

PGRI Adibuana University, 
Surabaya, Indonesia 
Email: 
nuurlaily_rukmana@unipasby.ac.id  

OPEN ACCESS 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32
mailto:nuurlaily_rukmana@unipasby.ac.id


 
Rukmana and Rudiarto / Geoplanning: Journal of Geomatics and Planning, Vol 3, No 1, 2016, 23-32         
doi: 10.14710/geoplanning.3.1.23-32   

 

24 | 
 

more productive land such as settlements, industry, etc., and creates transformation of the occupation 
from farmers to labors  (McGee, 1991). Malang is one of the regions that has such experience from 1990 to 
2011. Malang has promoted expansion urban areas that caused land use change. In addition, the presence 
of industrial activities also creates a high in-migration flow. Thus, the impact of this phenomenon is 
occupation transformation.  

This article elaborates some influencing factors that contributed to land use change and its impacts on 
occupation transformation. Some studies have been conducted to assess land use change from different 
perspectives and approaches such as physical and socio-economic aspects.  The urbanization process also 
has an impact on the reduction of agricultural land use through the enlargement of residential and 
industrial areas (Guan et al., 2011; Su, Jiang, Zhang, & Zhang, 2011). They used physical aspects to measure 
the relationships between land use change and urbanization process. These aspects are total area, patch 
density, parameter area ratio distribution, Euclidian nearest neighbor distance, and aggregation index. In 
addition, patch  numbers  and  patch  areas of  a built-up  land are still  increasing  and  showing  the  
diffused  distribution  patterns  from an urban  center  to suburban region (Guan et al., 2011). Both studies 
measured transformation of agricultural landscapes under rapid urbanization by applying Global Moran’s I 
statistics and Local Indicators of Spatial Association (LISA) analysis. 

 

2. DATA AND METHODS  

2.1 Data  

This research used data collected from different sources. The data is related to the physical and social 
aspects and divided into two purposes as shown in Table 1. 

 
 

Table 1. The Data (Authors, 2015) 

Data and information Sources 

Delineation of study area: initial urban and rural status, density, 
accessibility and built up area.  

Basic Village Data (Podes), CBS (2010-2012), 
Spatial Planning of Malang City (2010-2012) 

Assessing the influence of land use change;  

 Population 

 Density 

 Migration 

 Distance 

 Occupation Transformation 

CBS (2010-2012), Development Planning Agency 
of Malang City 

 
 

2.2 Research Methods 

This research used quantitative approach in analyzing the factors influencing land use change as well as 
scoring method to delineate the study area. Scoring method was applied in order to delineate study area 
into a more realistic condition. The analysis itself was conducted into two types, i.e., descriptive and spatial 
statistical analyses. 

a) Descriptive analysis was applied for: 

(1) Delineating the study area based on scoring in each criteria (i.e. the percentage of built up area and 
road level) 

(2) Analyzing the characteristics of land use change in suburban area of Malang city by using an 
Exploratory Data Analysis (EDA). The first step to analyze spatial regression is EDA (L. Anselin & 
Getis, 1993; Anselin et al., 2006). The function of EDA is to determine the outlier or extreme value 
through the tools of boxplot and box map. Moreover, to assess the relationship between 
dependent variable and independent variable can be conducted by scatter plot tool. 
 
 
 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32


 
Rukmana and Rudiarto / Geoplanning: Journal of Geomatics and Planning, Vol 3, No 1, 2016, 23-32         

doi: 10.14710/geoplanning.3.1.23-32   

 | 25  
 

n

Range

b) Spatial Statistical Analysis 

Spatial multiple regressions analysis was employed through Open GeoDa and ArcGIS 9.3 software. The 
regression analysis was divided into two processes, i.e., simple linear regression and spatial regression. The 
spatial regression analysis can be continued if the value of Lagrange Multiplier (LM) Lag and LM error in 
simple linear regression is less than 0.05. Spatial regression in this analysis was based on the following 
rules. Spatial weight describes the model of spatial interaction between a polygon and another polygon. It 
can be used to analyze total villages of the study area that have been influenced and to be included in the 
model formula. The output of this model can be visualized as follows: 

 

Y = A.W+B+ a.X1+ b.X2+ c.X3+d.X3+ e.X4+ f.X5+… 
where:  
Y  = Land use changes 
A  = Lambda 
W  = spatial weight (Queen Contiguity) 
B  = constants 
a-z = variable coefficients 
X1 = Population 
X2 = Density 
X3 = Total migration 
X4 = Accessibility 
X5 = Occupations transformation 

 

3. RESULTS AND DISCUSSION 

3.1 Delineation of Study Area 

The process of delineating suburban area was done by collecting initial data. There are two criteria to 
assess suburban area, i.e., total built up area and road level or accessibility. These criteria were applied to 
each village in the study area by using scoring and then summarizing them into the final weights (see Table 
2). 

Table 2.Total of Delineation Area (Analysis, 2015) 

Parameter Weight Indicator Score Classification Sub district 

A B C D E F G H I 

Built Up area 50 The 

percentage 

of built up 

area 

1 Rural  

(0 %- 25%) 

50 

(1) 

50 

(1) 

50 

(1) 

150 

(3) 

50 

(1) 

150 

(3) 

150 

(3) 

150 

(3) 

50 

(1) 

3 Sub urban (25% - 75%) 

Accessibility 50 Road level 1 Local 100 

(2) 

50 

(1) 

150 

(3) 

50 

(1) 

50 

(1) 

100 

(2) 

100 

(2) 

100 

(2) 

150 

(3) 2 Collector 

3 Main Road 

TOTAL         150 100 200 200 100 250 250 250 200 

 
Information:  
A  = Bululawang sub district F = Pakisaji sub district 
B = Tajinan sub district G = Pakis sub district 
C = Singosari sub district H = Dau sub district 
D = Wagir sub district I = Karangploso sub district 
E = Tumpang sub district 
 

The range of the class can be calculated as follows: 
    

p =                             Range = the highest data – the lowest data 
 

Note : R = Range  
 p = interval class 
 n = Total class 

 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32


 
Rukmana and Rudiarto / Geoplanning: Journal of Geomatics and Planning, Vol 3, No 1, 2016, 23-32         
doi: 10.14710/geoplanning.3.1.23-32   

 

26 | 
 

p  =   250 - 100  = 75 
       2 
 

Based on the formula, the class intervals in terms of classification zone were (1) Rural = 100-175; (2) 
Suburban = 176 – 250. 

 

Based on the overall analysis above, the delineation area is located in the northern part of Malang City 
(Singosari, Karangploso, and Pakis) as shown in Figure 1. It is reasonable since those three sub districts are 
industrial area for different products such as tobacco and furniture. In addition, it is also recognized from 
the physical appearance that these three sub districts have strategic location and accessibility (Malang-
Surabaya) and hence, the tendency of land conversion is quite high. 

 
 

Figure 1. Delineation Map (Analysis, 2015) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3.2 Characteristics of Land Use Change 

As mentioned before, in order to analyze the land use change, five variables were selected, i.e., 
population, density, migration, distance, and occupation transformation. Those variables are then divided 
into dependent and independent variables. Total built up area was determined as the dependent variable 
and the five variables mentioned above as the independent variables. 

 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32


 
Rukmana and Rudiarto / Geoplanning: Journal of Geomatics and Planning, Vol 3, No 1, 2016, 23-32         

doi: 10.14710/geoplanning.3.1.23-32   

 | 27  
 

a) Population 

Lynch (2005) mentioned that the rapid population of third world cities raises concerns on the changing 
nature of the relationships between urban and rural area. Malang is one of the cities that have such 
experience of population growth. Based on the analysis, the highest population percentage was found in 
Pangentan Village-Singosari Sub-districts. High population concentration in these areas is merely because of 
two factors, i.e., location of furniture industries and major access from Malang to Surabaya. Box map 
analysis was applied to show the relation between population and land use change. As shown in Figure 2, 
the population variable is directly correlated to land use change, which means high population level will 
affect land use change in the suburban of Malang. 

 

Figure 2. Scatter Plot and Box Map Analysis of Population (Analysis, 2015) 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

b) Density 

Density is one of the drivers of urbanization process where it may indicate that the built up area is 
growing (Knox & McCarthy, 1994). It is also relevant to Malang City. High density areas are found in some 
parts of the city particularly in the industrial area. Population and density are two aspects directly 
correlated to the urbanization process. High population concentration will influence the level of density. 
Based on the analysis, it is indicated that the highest density is located in Pangentan Village-Singosari Sub-
districts as well as Sekarpuro village of Pakis Sub-district. In those sub-districts, agriculture and furniture 
industries are dominant.  

Figure 3 shows the box map and scatter plot analysis that indicates the relationship between land use 
change and density. The density variable is in contrast with the land use change in the suburban area of 
Malang City. This is because the settlements and facilities are just located in surrounding workplaces and 
shows that not all villages have high density as population density is always followed by the location of 
jobs. This is similar with what has been mentioned by (Bergstrom et al., 2013) that people and jobs often 
move together. 

 

 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32


 
Rukmana and Rudiarto / Geoplanning: Journal of Geomatics and Planning, Vol 3, No 1, 2016, 23-32         
doi: 10.14710/geoplanning.3.1.23-32   

 

28 | 
 

Figure 3. Scatter Plot and Box Map Analysis of Density (Analysis, 2015) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

c) Migration 

In this research, the migration is only about the number of entrants in each village - people who work in 
surrounding their workplace and people who do not have the ability to live in urban area. The highest 
migration number was found in Purwoasri Village-Singosari Sub-district. This village is located just on the 
side of the major road of Malang-Surabaya. It is found that the migration variable is in line with land use 
change and therefore high migration number will also affect land use change in Malang’s suburban (see 
Figure 4). Migration in Malang’s suburban is very much related to the pull factor of the region, i.e., 
industrial area. 

Figure 4. Scatter Plot and Box Map Analysis of Migration (Analysis, 2015) 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32


 
Rukmana and Rudiarto / Geoplanning: Journal of Geomatics and Planning, Vol 3, No 1, 2016, 23-32         

doi: 10.14710/geoplanning.3.1.23-32   

 | 29  
 

d) Distance 

Distance variable was selected to compare village’s location to the city center where distances among 
villages were assessed. The results show that there were no villages became outliers in the model of box 
map analysis. It means that the average of distance between each village to the urban area is not so far. 
Moreover, the distance variable is directly correlated to the land use change; it means that land use change 
occurs on a far distance from the city center. From the current condition, land use change was not found in 
the whole location but only in several places where industrial and commercial activities were located (see 
Figure 5). 

Figure 5. Scatter Plot and Box Map Analysis of Distance (Analysis, 2015) 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 

e) Occupation Transformation 

The occupation transformation variable was focused on the total labors in each village. It was found 
that the highest occupation transformation is located in Banjararum Village-Singosari Sub-district. The 
occupation transformation in this village tends to follow the development of the area where a large 
tobacco industry is located.  As the conclusion from the statistical test, the occupation transformation 
variable is in line with the land use change in the suburban area of Malang (see Figure 6). 

Figure 6. Scatter Plot and Box Map Analysis of Occupation Transformation (Analysis, 2015) 
 
 
 
 

 
 
 
 
 
 
 
 

 
 

 

 

3.3 Model of Land Use Change 

 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32


 
Rukmana and Rudiarto / Geoplanning: Journal of Geomatics and Planning, Vol 3, No 1, 2016, 23-32         
doi: 10.14710/geoplanning.3.1.23-32   

 

30 | 
 

Previous analysis by using box map and scatter plot only shows the preliminary relation of each variable 
in correspondent with land use change. The challenge is how to assess all the variables into a single model 
and based on the model the factors that influence land use change can then be identified. Two approaches 
have been done in order to assess the best land use change model, as follows: 

a) Correlation analysis; from this analysis, it was found that variables of density and occupation 
transformation have a strong relationship to land use change. Therefore, simple regression analysis 
was necessary to compare the model resulted from each approach.  

b) Regression analysis was employed to see the correlation between variables by reducing the outliers 
through one by one outlier of independent variables. If the value of Lagrange Multiplier (LM) Error is 
appropriate (less than 0.05) then spatial regression analysis is possible to be applied. 

 

To determine the best model of land use change, these criteria need to be fullfilled:  

a) The model should has a high coefficient of determination value (R2), where this value range from 0 to 1 
(closer to 1 considered as the best), 

b) Total of independent variables; the independent variables which are entered in the formula can be 
identified as the best model. 

Based on the whole model process done in linear regression analysis, two models were identified that 
can further be processed into spatial regression analysis, as shown in Table 3. 

 
 

Table 3. The Models (Analysis, 2015) 

Dependent variable Model of land use change  R2 value Total of 
independent 

variables 

Spatial 
Probability 

 
 
 
Land Use Change 

Using Correlation Independent Variable: 
 
Y = - 172,678.9 – 0.2139184 .W + 164.8632 X1 – 
336.3252 X2 

0.35 2   

Reducing one by one outlier in term of 
population variable: 
 
Y = 3,733.646 – 0.140907 .W + 98.51888 X1 – 
341.7208 X2 + 3,579.966 X3 

0.40 3   

 

The best model of land use change in Malang suburban area must have two criteria as mentioned in the 
previous explanation. Therefore, the best model in this study is chosen by eliminating village used as 
population variable and reducing one by one of the outliers with the equation as follows; 

 

Y = 3,733.646 – 0.140907 .W + 98.51888 X1 – 341.7208 X2 + 3,579.966 X3 
 
 
 
where,  
Y = Land use change (Km2) 
X1= Population (inhabitants) 
X2 = Density (inhabitants/Km2) 
X3 = Migration (inhabitants) 
 

 

From the model and statistical results, it can be inferred that the urbanization process that implies to 
land use change in suburban area of Malang City is influenced by migration aspect followed by population 
growth. The higher the level of population growth may create more demand on the land for different 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32


 
Rukmana and Rudiarto / Geoplanning: Journal of Geomatics and Planning, Vol 3, No 1, 2016, 23-32         

doi: 10.14710/geoplanning.3.1.23-32   

 | 31  
 

purposes such as settlement and facilities. The result also shows that most of the land use change in 
suburban area occurred because of the development of industrial area. This has been the pull factor for the 
surrounding areas to be developed. The existence of industrial area as the pull factor then determines the 
density level of its area where more people would like to stay around. It is also found out that the distance 
from city center is not one of the influence factors in land use change. In Malang City, the development of 
suburban areas was mostly caused by the growth of the industrial areas instead of the city center 
expansion. 

 

4. CONCLUSION 

The development of industrial areas has contributed greatly to the total migration of suburban area of 
Malang City with the average rate of 7.69% in 2010-2012. This phenomenon creates occupation 
transformation from agricultural activities to non-agricultural ones such as residential, commercial, service, 
labor, etc. As this study focused on the labor activities with the average level of 3.04%, therefore, the 
people particularly those working as labors prefer to choose their dwelling around the workplace for two 
reasons, i.e., the location close to their workplace to minimize their transportation cost and cheaper land 
price for housing and ownership purposes. The two reasons have made Malang urban region grows to the 
areas where industries are located, and this phenomenon creates unbalance development. Consequently, 
the growth of Malang urban region has only been in particular location, which may cause inefficiency in 
managing the city.  

 

5. REFERENCES 

Anselin, L., & Getis, A. (1993). Spatial Statistical Analysis and Geographic Information System. In M. M. 
Fischer & P. Nijkamp (Eds.), Geographic Information Systems, Spatial Modeling, and Policy 
Evaluations. Berlin Heidelberg: Springer-Verlag. 

Anselin, L., et. al. (2006). GeoDa: an introduction to spatial data analysis. Geographical Analysis, 38(1), 5–
22. 

Arta, F., & Pigawati, B. (2015). the Patterns and Characteristics of Peri-Urban Settlement in East Ungaran 
District, Semarang Regency. Geoplanning: Journal of Geomatics and Planning, 2(2), 103–115. 
http://doi.org/10.14710/geoplanning.2.2.103-115 

Bergstrom, J. C., et. al. (2013). Land use problems and conflicts: Causes, consequences and solutions. 
Routledge. 

Guan, D., et. al. (2011). Modeling urban land use change by the integration of cellular automaton and 
Markov model. Ecological Modelling, 222(20-22), 3761–3772. 
http://doi.org/10.1016/j.ecolmodel.2011.09.009 

Ives, C. D., & Kendal, D. (2013). Values and attitudes of the urban public towards peri-urban agricultural 
land. Land Use Policy, 34, 80–90. 

Kangalawe, R. Y. M., et. al. (2008). Changing land-use patterns and farming strategies in the degraded 
environment of the Irangi Hills, central Tanzania. Agriculture, Ecosystems and Environment, 125(1-4), 
33–47. http://doi.org/10.1016/j.agee.2007.10.008 

Knox, P. L., & McCarthy, L. (1994). Urbanization. Englewood Cliffs (MJ): Prentice-Hall. 
Lynch, K. (2005). Rural–Urban Interaction in the Developing World. New York: Routledge. 
McGee, T. G. (1991). The Emergence of Desakota Regions in Asia: Expanding a Hypothesis. The Extended 

Metropolis Settlement Transition in Asia, Honolulu: University of Hawai, 3–26. 
Paül, V., & McKenzie, F. H. (2013). Peri-urban farmland conservation and development of alternative food 

networks: Insights from a case-study area in metropolitan Barcelona (Catalonia, Spain). Land Use 
Policy, 30(1), 94–105. 

Phuc, N. Q., et. al. (2014). Agricultural land for urban development: The process of land conversion in 
Central Vietnam. Habitat International, 41, 1–7. 

Shi, Y., et. al. (2012). Landscape and Urban Planning Characterizing growth types and analyzing growth 
density distribution in response to urban growth patterns in peri-urban areas of Lianyungang City. 
Landscape and Urban Planning, 105(4), 425–433. http://doi.org/10.1016/j.landurbplan.2012.01.017 

Su, S., et. al. (2011). Transformation of agricultural landscapes under rapid urbanization: a threat to 
sustainability in Hang-Jia-Hu region, China. Applied Geography, 31(2), 439–449. 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32


 
Rukmana and Rudiarto / Geoplanning: Journal of Geomatics and Planning, Vol 3, No 1, 2016, 23-32         
doi: 10.14710/geoplanning.3.1.23-32   

 

32 | 
 

UN-HABITAT. (2005). Housing the Poor City in Asia. Project Report. Nairobi. 
Webster, D., & Muller, L. (2009). Peri-urbanization: Zones of rural-urban transition. Human Settlement 

Development-Volume I, 280. 
Zhang, Y., et. al. (2015). Responses of soil respiration to land use conversions in degraded ecosystem of the 

semi-arid Loess Plateau. Ecological Engineering, 74, 196–205.  

 
 

 

http://dx.doi.org/10.14710/geoplanning.3.1.23-32

