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         Geoplanning 
Vol 5, No. 1, 2018, 163-174                                                                                                                                                            Journal of Geomatics and Planning 

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

doi: 10.14710/geoplanning.5.1.163-174 

SPATIAL STATISTICS FOR MAPPING SOLID WASTE GENERATION 
MAPPING IN TEMBALANG, SEMARANG CITY 

S. Y. Ardiansyaha, M. Maryonob 

a Master of Urban and Regional Development in Diponegoro University, Indonesia 
b Department of Urban and Regional Planning in Diponegoro University, Indonesia 
 

Abstract: The annually increasing number of urban populations will have impacts on waste 

generation. Tembalang Sub-district as a sub-district located on the outskirts of Semarang City has 

significant developments in the term of population growth in correlation with waste generation. 

Within four years, waste generation in the Tembalang Sub-district increased from the fifth rank to 

the third rank. It is possible that this sub-district will become the first rank in Semarang City in 

waste generation. To be able to identify influential factors and spatial distribution pattern of 

waste generation in Tembalang Sub-district, it is necessary to apply statistical and spatial 

approach. This study uses quantitative methods with a statistical spatial analysis approach by 

using GIS. In addition, this research also intends to model the relationships of Solid Waste 

Generation by applying socio-economic variables. Based on the results of Ordinary Least Square 

analysis, social economy variables that affect the amount of waste generation in Tembalang Sub-

district are the number of population and trading activities. The model of formed socio-economic 

variables has the effect of 25% towards the amount of waste generation. Spatial patterns 

identified from waste generation shows that what needs to be considered is the waste 

management in TPS (Temporary Waste Disposal) in Tembalang and Sendangmulyo.  

 

 
Copyright © 2018 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): Ardiansyah S. Y & Maryono, M. (2018). Spatial statistics for mapping solid waste generation mapping in tembalang, 
semarang city. Geoplanning: Journal of Geomatics and Planning, 5(1), 163-174. doi: 10.14710/geoplanning.5.1.163-174 

1. INTRODUCTION 

Solid Waste Generation problem is a common problem faced by developing countries. The increasing 
number of urban waste is caused by increasing population and urbanization (JICA, 2005). Waste is the 
remaining part of human activities that needs to be managed properly so as not to cause problems to 
human life or disturbance to the environment such as environmental pollution, the spread of disease, 
reduced aesthetics and as a carrier of the disease. The problem of waste management is very serious in 
urban areas due to the complexity of the problems and high population density, so waste management is 
often a prioritized handling in urban areas (Moersid, 2004). Waste management in Indonesian cities has yet 
to achieve optimum results. Population number has been one of the reasons why wastes in cities are piling 
up so high (Damanhuri et al., 2010).  

Previous studies only focused on the flow of solid waste management processes, but they did not specify 
link between waste generation and the spatial aspects (Olukanni et al., 2014). Mapping provides the exact 
location, amount, and type of garbage. Waste mapping had also been done although only limited to 
location mapping and the paths used to dump waste (Lee et al,. 2015).  

To find out how big the influence of urban waste generation, the identification requires a spatial 
approach to make it more easily understandable (Yousif & Scott, 2007). In the context of spatial data, it can 
be seen how the patterns are formed by the spread of the existence of waste generation.  

In this study, the author intends to identify spatially the distribution of waste generation in Tembalang 
Sub-district. This research's objective is to map waste dumping spots and to derive cross-factor 
relationships from related socioeconomic factors. The exact pattern of waste generation will show which 

OPEN ACCESS 

Article Info: 
Received: 7 July 2017 
in revised form: 10 Dec 2017 
Accepted: 30 January 2018 
Available Online:  25 April 2018 
 

Keywords:  
Spatial Statistics, Waste 
Generation, Ordinary Least 
Square 
 

Corresponding Author: 
Septa Yudha Ardiansyah  

Urban and Regional Planning in 

Diponegoro University, Indonesia 

Email: 

septayudha573@gmail.com 

  
 

https://doi.org/10.14710/geoplanning.5.1.163-174
https://doi.org/10.14710/geoplanning.5.1.163-174
mailto:septayudha573@gmail.com


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areas have the highest concentrations from which then a waste handling method can be formulated. The 
analytical method used is quantitative analysis with the spatial approach by using spatial statistics analysis. 
Ordinary Least Square (OLS) models are used to determine the relationship that is formed from the spread 
of waste generation. The author uses the software Arc Map 10.1.   

 
2. DATA AND METHODS 
2.1.  Study area 

Tembalang Sub-district is one of Semarang City suburbs that has been developed very rapidly. This 
suburb has its own pull factors for the people to settle and live there. The development activities of this 
sub-district are also characterized by the presence of Diponegoro University that turns this area to 
experience function changes of the former rice fields, gardens, and fields into trading activities, housing and 
boarding houses for students. People who live in Tembalang are not only from Semarang City but also from 
out of town or province. 

 

 

With an area of 41.74 km2, Tembalang Sub-district consists of 12 villages which are Rowosari, Meteseh, 
Kramas, Tembalang, Bulusan, Tandang, Mangunharjo, Sendangmulyo, Jangli, Sambiroto, Kedungmundu, 
and Sendangguwo (Figure 1). The population of Tembalang Sub-district in 2015 amounted to 154,697 
inhabitants and is the second highest number of inhabitants in Semarang City after Pedurungan Sub-district. 
Total average production of waste / day in Tembalang Sub-district is the second largest in Semarang which 
amounted to 325.29 m3 / day (CBS, 2015). This means that the average waste generated by Tembalang 
society is a sizeable 2.35 kg / day.  

Each year, the waste generation in Tembalang Sub-district has increased in an average of about 1.73 
tons per year (Figure 2).  Generally, Tembalang sub-district has 5 units of arm roll trucks, 28 units of truck 
containers, and 30 plots of landfills. Based on the data from DKP (Cleaning and Landscaping Agency) of 
Semarang City, the current situation of waste generation in Tembalang Sub-district is already handled as 

Figure 1. Administrative Map of Tembalang Sub-district  
 

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much as 83%. This means that the remaining 17% of waste is still piled up and cannot be addressed by the 
government. 

 

Figure 2. Total Number of Waste Generation in Tembalang Sub-district (DKP, 2015) 

 

Table 1. TPS Name, Container Amount and Estimation of Daily Waste Generation  

Tembalang Sub-district (CBS, 2015) 

Num Village TPS Name Cont. Volume/ 
day (m3) 

Num Village TPS Name Cont. Volume/ 
day (m3) 

1 Rowosari - 0 ±1000 16 Sendangmulyo TPS Ketileng Atas 1 ±5000 

2 Meteseh TPS Dinar Mas 1 ±1500 17 TPS Ketileng Bawah 1 

3 TPS Bukit Kencana 1 18 TPS ASPOL Sd.Mulyo 1 

4 TPS Pasar Meteseh 1 19 TPS PSIS 1 

5 Kramas - 0 ±1000 20 TPS RSUD 1 

6 Tembalang TPS Politeknik 1 ±4000 21 TPS Klipang 1 

7 TPS Tembalang 4 22 TPS Cempaka 1 

8 TPS Bukit Diponegoro 1 23 TPS Menur 1 

9 Bulusan - 
 

±1000 24 TPS Tulus Harapan 1 

10 Mangunharjo TPS Jl. Elang Raya 1 ±1000 25 TPS Keliling 1 

11 TPS Rumpun 1 26 Sambiroto TPS Salak Utama/Intan 1 ±1500 

12 Tandang TPS Tandang 1 ±1000 27 TPS Sambiroto RW VI & VII 1 

13 TPS Rogojembangan 1 28 TPS Wana Mukti 1 

14 Kedungmundu TPS Kini Jaya 1 ±500 29 Jangli TPS TPS Jangli 1 ±500 

15 Sendangguwo TPS Sendangguwo 1 ±1000 
     

*Note:  TPS (Tempat Pembuangan Sampah sementara) : Temporary Waste Disposal Place 
 Cont. : Number of Container 

 
 
 
 
 
 
 
 

 

Volume 
(Ton) 

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*Note: Number 0 indicates Open Dumping TPS 

Figure 3. TPS Location Map (Observation, 2016) 

 

Figure 4. Temporary Waste Disposal Place & Open Dumping Waste (Observation, 2016) 

2.1.1. Socio-economic data 
In this study, the data to be used as an independent variable influence on waste generation in 

Tembalang Sub-district are socio-economic variables (Table 2). Those variables consist of the total 
population, household size, and the number of trading activities. Here is the socio-economic data of 
Tembalang Sub-district in 2015. 

 

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Table 2. Socio-economic data in Tembalang Sub-district (CBS, 2015) 

Num. Village Wide Area (km2) Population 
(person) 

Household Size Number of Trading Activities 
Density 

(people/km2) 

1 Rowosari 8.7 11019 3214 84 1316 

2 Meteseh 4.99 15621 4888 383 2669 

3 Kramas 0.93 3384 1024 15 1311 

4 Tembalang 2.68 5519 1294 75 1480 

5 Bulusan 3.04 5125 1588 147 1772 

6 Mangunharjo 3.03 8468 2532 68 3552 

7 Sendangmulyo 4.61 33697 10440 622 5855 

8 Sambiroto 3.18 12357 3584 84 6548 

9 Jangli 2.07 6402 1676 13 2208 

10 Tandang 3.75 20382 6115 104 10744 

11 Kedungmundu 1.49 11127 2883 156 5680 

12 Sendangguwo 3.27 21596 5997 87 17396 

 TOTAL 41.74 154697 45235 1838  

 

2.2.  Analytical Procedure 

The research sample location is the whole Temporary Waste Disposal (TPS) in each village which then 

the results of observations and obtained data are to be used as input for the analysis of total community 

waste production. From the distribution of point locations in the study area, model in the term of spatial 

aspect spreading can be observed. The author also finds the relationship between the community waste 

generation by factors that affect the amount of waste. In this case, the author only uses socio-economic 

variables as independent variable. The relationship is modeled into an equation by using Ordinary Least 

Square (OLS). The software used for Ordinary Least Square Analysis is ArcMap 10.1. 

2.3.  Spatial Statistics using GIS for mapping 

Spatial Statistics is a tool of analysis in a Geographic Information System that serves to analyze the 
spatial distribution, spatial patterns, processes, and spatial relationships. While there may be similarities 
between the spatial and non-spatial (traditional) statistics in terms of concepts and objectives, spatial 
statistical uniquely developed specifically used for data concerning spatial geographic. Spatial Statistics 
combines the space of distance, area, connectivity and spatial relationships directly by using math. 
Statistical analysis is also used to identify and confirm the form of spatial patterns, such as the 
centralization of the group, finding out the trend direction, or whether to form a cluster. Statistical 
functions analyze the underlying data and provide some measures that can be used to determine the 
existence and strength of the pattern. Spatial statistics contains cluster analysis which takes formed cluster 
pattern into account. 
 
2.3.1. Average Nearest Neighbor & Hot-Spot Analysis 

Average Nearest Neighbor is one of analysis used to explain the distribution pattern of location points by 
using a calculation that considers distance, the number of location points, and total area. This analysis 
requires data regarding the distance between the settlements with the closest settlement which is 
settlement nearest neighbor. Average Neighbor Analysis calculates the distance between each feature and 
the nearest neighbor, then computes the data for the entire distance of the nearby neighborhood. The 
output of the Average Nearest Neighbor analysis is in a form of a Z-Score and P-value index that indicate 
whether the case is clustered, random or dispersed. Before analyzing, the requirement to obtain a Z-Score 
is to know the extent of the territory within the limits of the study, in this case is Tembalang Sub-district. 
Based on calculations by using Arc Map application, the total area of Tembalang Sub-district is 41,452,310 

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m2. The input data to be processed is the data of point results of waste disposal sites scattered around 
Tembalang Sub-district. 

Nearest Neighbor Index describes the ratio of the observed distance median value to the expected 
distance median value. The expected distance is the mean distance between neighbors in random 
distribution hypothesis. If the index is less than 1, the pattern shows a grouping (Cluster); if the index is 
greater than 1, the trend is spreading. In Average Nearest Neighbor analysis, there is also significance level 
(p-value) and the critical value (z-score). Both these values are worth 0.01 -> 2.58. P-value and Z-score 
indicate whether H0 is rejected or not. In Average Nearest Neighbor, H0 indicates that features are 
randomly distributed. If the Z-score has a value <-1.65, then the cluster will more likely to shape. Whereas if 
it is> 1.65, the pattern will be more random. 

Hot Spot is the concentration of the incident with the geographic area boundaries that appear from time 
to time. Hot Spot can also be used to assess concentrations of specific land use, or between activity and 
land use  (Block & Block, 1995). Hot Spot may not exist in real life, but it represents where there is a 
concentration of activities or specific cases so that the area can be labeled as areas of high concentration. 
There are dozens of statistical analysis techniques to identify the Hot Spot (Everiit, 1974). Most of the 
statistical analysis technique used is commonly called cluster analysis. This is a technique of grouping 
together cases in a relatively coherent group. Because the Hot Spot is a perception construction, the 
techniques must use an approach on how one understands the study area. Here are a few types of methods 
Hot Spot / Cluster analysis (Everiit & Megbolugbe, 1996) : 
1. Point Location 
2. Hierarchical Techniques 
3. Partitioning Techniques 
4. Density Techniques  
5. Clumping Techniques 
6. Risk-based Techniques 
7. Miscellaneous Techniques 

 
2.3.2 Spatial Pattern 

According to Tobler in his book "The First Law of Geography", he revealed that all things are always 
related to everything else, but something closer is to have more influence than something far (Tobler, 1970) 
a spatial autocorrelation. Spatial cluster is a positive spatial autocorrelation when there are similar values 
forming cluster, while the opposite is if there are separate values called as negative spatial autocorrelation  
(Boots & Getis, 1988). The spatial clusters can help the understanding of geographic processes underlying 
the relationship with the phenomenon under study. Based on existing spatial clusters, there will be formed 
different spatial patterns. Spatial Pattern is something that shows the placement or arrangement of objects 
on the Earth's surface (Lee & Wong, 2001) . The spatial pattern will explain how geographic phenomena is 
distributed and how it compares with other phenomena. The spatial pattern can be either a point or area 
(Polygon), and they can form a pattern of clustered, dispersed, and random. 

 
2.3.3 Modeling using Ordinary Least Square Regression 

As with the simple linear equation regression analysis, the basic idea behind OLS is to explore the 
relationship between a dependent variable (Y) and one or more independent variables (the X’s). The simple 
model of OLS equation can be expressed as (Ryan, 1996), which is defined as follows: 

 ++=
k

iikki xy 0

 
where  

• { ikx } are observations for i = 1,..,n cases and k = 1,..,m explanatory variables,  

• {yi} are the dependent variables,  

•  ’s are the estimates of the coefficients,  

• and ’s are normally distributed error terms.  
 

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3. RESULTS AND DISCUSSION 

3.1. Average Nearest Neighbor & Hot Spot of Waste Generation 

Under the table shows that the expected mean distance between one point to another point is 578 
meters, however, the real condition is that the observed mean distance between one point to another 
point is 560 meters (Table 3). The value of z-score value is -0.326302 because the value is > - 1.65, so the 
case of observation points forms a random pattern. More details are shown in the following figure. 
 

Table 3. Calculation Results of Average Nearest Neighbor Analysis (Analysis, 2016) 

Explanation Result 

Observed Mean Distance 560.468465 Meters 

Expected Mean Distance 578.180614 Meters 

Nearest Neighbor Ratio 0.969366 

z-score -0.326302 

p-value 0.744196 

 

 

Figure 5. Result Diagram of Average Nearest Neighbor Analysis (Analysis, 2016) 

Hot Spot Analysis on waste generation uses attributes of the average amount of waste disposal per 
capita / day. Hot Spot Analysis is performed by using analysis tools in Arc Map named "Hot Spot Analysis 
(Getis-Ord-Gi’)" (Figure 5). In addition to the above attributes, the maximum distance of each household in 
one sub-district is required based on the previous analysis result. So, the author uses observed mean 

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distance as a threshold which is 560 meters. Based on the input data in the Arc Map application, the Hot 
Spot result is obtained as follows. 

 
Figure 6. Result Map of Hot Spot Analysis (Analysis, 2016) 

 
Based on the picture (Figure 6), the hotspots show the occurring concentration of solid waste 

generation. There are two main colors namely red and blue. The red color indicates the higher 
concentration of waste generation while blue color conversely indicates the lower concentration. The 
yellow color indicates the medium concentration. TPS Tembalang has the hot spot, so it means in this place 
amount of solid waste generation agglomerated here. 

 
3.2. Spatial Pattern of Solid Waste Generation 

Spatial Pattern Analysis of Solid Waste Generation is carried out by using the input feature from the Hot 
Spot result of waste generation. Hot-Spot points are interpolated according to the attributes similarity so it 
will make up the classification class to be determined. Here is a picture interpolated hot spot. 
 

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Figure 7. Spatial Patterns Map of Interpolation (Analysis, 2016) 

Based on the following image (Figure 7) , the color gradation of blue-yellow-red indicates the 
concentration of waste in landfills. The bluer the color indicates the less and more insufficient amount of 
waste generation. While the redder the color indicates that the amount of waste has exceeded its capacity. 
From the map, it appears that a pretty solid waste generation are around the area of Sendangmulyo and 
Meteseh. While the area that has a solid waste, generation is around the area of Tembalang Village. 
 
3.3. Modelling Solid Waste Generation 

In making the equation model of waste generation in Tembalang Sub-district, OLS analysis is used as 
supporting equipment. The dependent variable in the model is the data of waste generation, while the 
independent variable is a variable of socio-economic characteristics including the number of populations, 
number of households, population density, and the amount of trading activity. Here are the results of the 
processed data by using Spatial Statistics analysis in ArcMap 10.1: 
 

 
Figure 8. Number of Household & Number of Population Map (Bappeda1, 2011) 

 
1 Bappeda (Badan Perencanaan Pembangunan Daerah): Regional Development Planning Agency 

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Figure 9. Total Number of Trading Activities & Number of Density Map (Bappeda, 2011) 

 

 
Y = 398.776650 + 0.720890 X1 - 1.972677X2 + 2.317599X3 - 0.203706 X4 

 

Where: 
Y  : Solid Waste Generation  X3 : Number of Trading Activities 
X1 : Number of Population  X4 : Number of Density 
X2 : Number of Household 
 

The results of Adjusted R-Squared (R2) analysis indicates the value of 0.253366. This shows that the 
percentage of independent variable influence contribution towards the dependent variable is 25%. This 
means that there are other variables that affect the amount of society waste generation. Based on the 
above linear equation, the coefficients which have positive value towards waste generation are population 
and trading activities, while the coefficients which have negative value are the number of household and 
density. This equation explains that the increase of population and trading activities will intensify the 
production of waste generation. While the number of household and density don’t necessarily affect the 
amount of waste generation in Tembalang Sub-district. 

 
3.4. Discussion  

According to the spatial pattern map identified by the amount of solid waste generation, it can be seen 
that the dominant red color is in TPS Tembalang (TPS number 7). This is because TPS Tembalang 
accommodates waste from three villages namely Rowosari, Kramas, and Bulusan. Although TPS Tembalang 
has a considerable number of containers, but it is still not able to accommodate all waste from those three 
villages. Another factor that led to the amount of waste in TPS Tembalang is due to the presence of 
Diponegoro University. The existence of Diponegoro University causes the growth of residential areas, 
trading activities, and the increasing number of students who live in the area around Tembalang. The 
second largest waste generation is in TPS Sendangmulyo mainly caused by the fact that Sendangmulyo has 
the largest population in Tembalang Sub-district. The presence of market and trading activities also cause 
this sub-district is very noteworthy. This is in line with  Yousif & Scott (2007)  argument on how the amount 
of waste's growth in cities is driven by rapid population increase and economic expansion. 
Recommendations that can be applied in the Tembalang Sub-district is the need to create new TPS in the 
Kramas and Rowosari. Ideally, every village must have at least one TPS. Because the socio-economic 

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variable that contains the model of waste generation models influence only 25%, this means that there are 
other variables that affect the increase in the amount of waste generation (Lee et al,. 2015). Therefore, the 
author recommends further research to add variables such as culture (lifestyle), the income of people 
(more detailed), and land use (spatial aspects) to be able to make a more complete model. 
 

4. CONCLUSION 

Based on the performed OLS result in the study area of Tembalang Sub-district, socio-economic variables 
that influence waste generation are variables of population and the number of trading activities. The 
growing number of residents and trading activities will affect the amount of waste generation. The results 
of the relationship model between the dependent variable (waste generation) with independent variables 
(population, the number of households, trading activities, and density) have an effect of 25%. It means 
there needs to be a search for other variables that can affect the amount of waste generation in Tembalang 
Sub-district. Spatial patterns identified from the waste generation in Tembalang Sub-district shows that TPS 
Tembalang in Bulusan Village is red. It indicates the need for special handling in that location. TPS 
Tembalang accommodates garbage from three villages namely Rowosari, Bulusan, and Kramas. However, 
the number of containers to accommodate the amount of community waste is still lacking. Therefore, it is 
necessary to establish new TPS in the Village of Rowosari, Kramas, and Bulusan. By using spatial statistics, 
we can map, search for relationships between variables and model a case around us. Observing previous 
studies of a case in terms of the spatial view would make it more easily understood and solved. Spatial 
Statistics by using GIS is not only to be used for waste disposal or waste generation but can also be used in 
the case of health, crime, transport, etc. The author also suggests forming models using other variables 
related to waste piles such as economic, demographic, physical and other factors. 

 

5. ACKNOWLEDGMENTS 

The author would like to give an appreciation to the anonymous reviewer who has provided a lot of 
input during the writing of this paper. This study is supported by Indonesia Endowment Fund for Education 
(LPDP) Ministry of Economic in Indonesia. The author thanks the lecturers in Master Program of Urban and 
Regional Planning Development for providing supports to this research.  

 

6. REFERENCES 

Bappeda. (2011). Spatial Planning Development Document. Semarang. 
Block, R. L., & Block, C. R. (1995). Space, place and crime: Hot spot areas and hot places of liquor-related 

crime. Crime and Place, 4(2), 145–184. 
Boots, B. N., & Getis, A. (1988). Point pattern analysis (Vol. 8). SAGE Publications, Incorporated. 
CBS. (2015). Tembalang Sub-district in Figures. Semarang: Central Bureau of Statistics. 
Damanhuri, E., Wahyu, I. M., & Padmi, T. (2010). Evaluation of waste recycling potential in Bandung 

Municipal Solid Waste. World Review of Science, Technology and Sustainable Development, 7(3), 
282. [Crossref] 

DKP. (2015). Number of Waste Generation in Tembalang Sub-district. Semarang. 
Everiit, B. (1974). Cluster Analysis, London: Heinemann Education Books. 
Everiit, B., & Megbolugbe, I. (1996). The Geography of Underserved Mortgage Markets. American Real 

Estate and Urban Economics Association Meeting. 
JICA. (2005). Supporting Capacity Development in Solid Waste Management in Developing Countries; 

Towards Improving Solid Waste Management Capacity of Entire Society. Tokyo.  
Lee, D., Kung, K., & Ratti, C. (2015). Mapping the Waste Handling Dynamics in Mombasa Using Mobile 

Phone GPS. In Proceedings of The 14th International Conference on Computers in Urban Planning 
and Urban Management. 

Lee, J., & Wong, D. W. . (2001). Statistical Analysis with ArcView GIS. New York: John Wiley & Sons Inc. 

https://doi.org/10.14710/geoplanning.5.1.163-174
https://doi.org/10.1504/wrstsd.2010.032530


Ardiansyah & Maryono / Geoplanning: Journal of Geomatics and Planning, Vol 5, No 1, 2018, 163-174 
doi: 10.14710/geoplanning.5.1.163-174 

  

174 | 
 

Moersid, M. M. (2004). Konsep National Action Plan Pengelolaan Sampah dalam Rangka Millenium 
Development Goals. Semarang: Dalam acara Kajian Pengelolaan Sampah Secara Terintegrasi. 

Olukanni, D., and Oladipupo Akinyinka, Ede, A., Akinwumi, I., Ajanaku, K., & and. (2014). Appraisal of 
Municipal Solid Waste Management, Its Effect and Resource Potential in a Semi-Urban City: a Case 
Study. Journal of South African Business Research, 1–13. [Crossref] 

Ryan, T. P. (1996). Modern Regression Methods. Chichester: John Wiley and Sons. 
Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic 

Geography, 46, 234. [Crossref] 
Yousif, D. F., & Scott, S. (2007). Governing solid waste management in Mazatenango, Guatemala: Problems 

and prospects. International Development Planning Review, 29(4), 433–450 [Crossref] 

 

 

https://doi.org/10.14710/geoplanning.5.1.163-174
https://doi.org/10.5171/2014.705695
https://doi.org/10.2307/143141
https://doi.org/10.3828/idpr.29.4.2

