p–ISSN: 2723 - 6609 e-ISSN: 2745-5254
Vol. 4, No. 9, September 2023 http://jist.publikasiindonesia.id/
Doi : 10.59141/jist.v4i9.686 1285
MORAN I AUTOCORRELATION STUDY FOR LEVEL SPATIAL PATTERN
ANALYSIS
Nirma Lila Anggani
1*
, Hammam Muhammad Amrullah
2
, Diaz Syifa Akbar
Gemilang
3
University of Muhammadiyah Surakarta, Indonesia
Email: [email protected]
*Correspondence
ARTICLE INFO
ABSTRACT
Accepted
: 13-08-2023
Revised
: 13-09-2023
Approved
: 20-09-2023
Unemployment is a serious challenge faced by developing countries
such as Indonesia. These challenges involve complex factors
interacting with each other and can have a negative impact on social
and economic stability. This study focused on East Java Province as a
case in point, with the aim of analyzing the geographical distribution of
open unemployment (TPT) and the relationship between regions in that
context. Using a spatial analysis approach, specifically the Moran's I
autocorrelation method, this study seeks to uncover spatial patterns and
spatial interactions related to TPT levels. Quantitative data were used to
identify TPT distribution patterns in this region. The results of spatial
autocorrelation analysis indicate that the distribution of TPT in East
Java Province tends to be random. Although there are spatial patterns
that can be identified based on the Moran index, the z-score results
show that they are not significantly different from random patterns.
From the results of the Moran's I quadrant, it can be seen that there are
several areas with high TPT rates around other regions that also have
high TPT rates. Thus, this research contributes to formulating policies
and actions aimed at reducing unemployment, improving people's
welfare, and preventing potential social insecurity and poverty.
Keywords: Spatial Pattern
Analysis; Autocorrelation
Studies; Causes of poverty.
Attribution-ShareAlike 4.0 International
Introduction
Indonesia, as one of the developing countries, faces the challenge of
unemployment as one of the common problems faced by similar countries.
Unemployment is a complicated problem because it is influenced by various factors
that interact with each other with complex and elusive patterns (Muslim, 2014). If not
addressed immediately, unemployment can lead to social insecurity and potentially
lead to poverty. Open unemployment has a significant influence on economic
development, especially to increase per capita income in a country which ultimately
results in improving people's welfare (Arifin & Fadllan, 2021).
East Java Province is one of the provinces with a fairly developed economic
growth rate in several regencies and cities, especially in big cities such as Surabaya
City, Gresik Regency and Malang City, these cities are relatively large and have very
many resources to support regional economic development. East Java Province as one
of the provinces with a growing pace of economic development certainly has similar
problems related to open unemployment as other provinces in Indonesia (Giovanni,
2018). Primary poverty is included in the category of poor in asset ownership, low
Nirma Lila Anggani, Hammam Muhammad Amrullah, Diaz Syifa Akbar Gemilang
Jurnal Indonesia Sosial Teknologi, Vol. 4, No. 9, September 2023 1286
participation in social and political organizations, and limited knowledge and skills.
Meanwhile, in the aspect of secondary poverty, it involves poor conditions in terms of
social networks, limited financial resources, and limited access to information. The
influence of open unemployment here plays a considerable role in the impact on this
aspect.
This study aims to analyze regional distribution and inter-regional relations
related to the open unemployment rate of East Java province, with studies using
regional analysis methods becoming increasingly important. In this study, this study
applies a spatial analysis approach centered on Moran's I autocorrelation study to
identify spatial patterns of regional open unemployment rates and spatial interaction
rates. Moran's I autocorrelation study is a powerful statistical method for finding spatial
patterns in geospatial data (Ningrum, 2017). Using this method, the study can
determine whether there are groups of regions that have the same open unemployment
rate (positive autocorrelation) or random distribution (negative autocorrelation). The
results of this study are expected to provide a deeper understanding of the
characteristics of open unemployment areas in East Java Province and open
opportunities to identify areas that require more attention to overcome unemployment
problems.
According to the National Central Statistics Agency (BPS), the open
unemployment rate is a percentage of the number of unemployed in the labor force
(Ningrum, 2017). The open unemployment rate refers to four aspects, namely residents
who are actively looking for work, residents who are preparing new businesses or jobs,
residents who are not looking for work because they find it difficult to find work, and
groups of residents who are not actively looking for work because they already have a
job but have not started it. The open unemployment rate arises because the problem of
unemployment is a complex and multi-dimensional problem (Ningrum, 2017).
In this study, testing was carried out using the Moran Index method. The Moran
index is a commonly used method for calculating global-scale autocorrelation. This
usage refers to the indication of spatial patterns in TPT in East Java Province
(Wuryandari et al., 2014). The Moran Index calculates the difference in the average
value of all attributes and the difference in attribute values in each neighbor with
reference to the average value. The calculation is carried out by the following formula:
𝝁 =
𝑵
∑𝒊∑𝒋𝒘𝒊𝒋
∑𝒊∑𝒋((𝑿𝒊 − 𝑿)(𝑿𝒊 − 𝑿)
∑𝒊∑𝒋𝒘𝒊𝒋
Information:
I = Moran's I assessment
N = number of locations
Xi = assessment at location i
Xj = assessment at location j
X = average of variable calculations
Wij = element on weighting between regions i and j
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Assessment on Moran I can be assessed to determine indications of spatial
patterns in the attributes tested. The shape of the pattern is classified into three parts
can be in Table 1.
Table 1. Classification of Formed Patterns
Moran’s I
Information
I>0
Cluster
I<0
Random
I=0
Spread
After the assessment using the Moran Index is carried out, the next step is to test
the autocorrelation to see the presence of positive or negative values. Pattern testing can
be done using Moran's Scatterplot to see grouping and distribution patterns between
locations with standard provisions to test with an average of assessments taken based on
locations neighboring the location concerned. Figure 1 will present the quadrant on
Mora's Scatterplot. According to Moran's Scatterplot is divided into four quadrants.
Table 2
KuadranMoran’s Scatterplot
WZstd
Quadrant II (Low-High)
Quadrant I (High-High)
Quadrant III (Low-Low)
Quadrant IV (High-Low)
Zstd
The four quadrants indicate an assessment of the high and low grouping of
observation areas. Quadrant I (High-High) indicates a high value of observations
surrounded by a high observation area, Quadrant II (Low-High) indicates a low value of
observations surrounded by a high observation area, Quadrant III (Low-Low) indicates
a low value of observations surrounded by a low observation area and Quadrant IV
(High-Low) indicates a high value of observations surrounded by a high observation
area.
Method
Research Location
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The location in this study is located in East Java Province. East Java Province is
one of the easternmost provinces of Java Island. East Java Province has an area of
48,033 km2, with absolute locations at 111°0' – 114°4' East Longitude and 7°12' – 8°48'
South Latitude.
Data Sources
This research was conducted using quantitative data with the aim of determining
the number of TPT in East Java Province and its spatial pattern.
Stages of Data Processing
The research phase began with the collection of TPT data in East Java Province in
2022 and RBI data for East Java Province. The second is the incorporation of TPT Data
into RBI Data attributes, at this stage the data merger is assisted by ArcGIS software to
further adjust TPT data according to districts and cities in East Java Province, as well as
to calculate the spatial autocorrelation of the Moran index using the spatial statistics
tools feature. The third calculates the moran index, at this stage data processing is
assisted using GeoDa software, the processing results at this stage are moran scatter plot
and moran scatterplot map. The last stage in this study is drawing conclusions based on
the results of data processing.
Results and Discussion
a. Results of spatial autocorrelation calculation
The results obtained from the calculation of spatial autocorrelation found
information about spatial patterns based on the Moran index, This pattern is formed
based on the results of autocorrelation and relates the results to three classification
patterns formed according to the Moran Index (Table 1). Assessment on the spatial
patterns formed can later be used to determine the distribution pattern of TPT in East
Java Province by district.
The results of the spatial autocorrelation calculation of the Moran TPT Index in
East Java Province can be seen in Table 1.
Table 1
Calculation ResultsAutocorrelationSpatialSpatial
P-Value
0,122161
Variance
Z-Score
0,053637
0,002723
1,545767
Based on the results in Table 1, there is an assessment used to determine the shape
of spatial patterns of TPT in East Java Province. Based on the results obtained, it is
known that the value of Moran's Index is 0.053637 or indicates a random distribution
pattern. Based on the results of the z-score found to be 1.54576675529, the pattern does
not seem to differ significantly from random. The Z-score is used to measure the degree
to which data differs from the mean in units of standard deviation. If the z-score is close
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to 0, then the data shows a pattern that is not significantly different from the expected
random data. In Figure 1 will be visualized a map of the Moran's Index I test results.
b. Hasil Moran’s Scatterplot
Based on the results of Moran's Scatterplot used to see the distribution of regions
between quadrants. Moran's Scatterplot processing is done using GeoDa software. The
results of the TPT distribution pattern in East Java Province will be visualized in Figure
1 as follows.
Figure 1. Results of Moran's Scatterplot
Based on the results obtained, it can be seen the spatial distribution of regencies
and cities in East Java Province which are classified in each quadrant in the scatterplot
moran. In Figure 2, the spatial pattern of TPT will be visualized based on the quadrant
that has been tested.
Figure 2. Moran Scatterplot Map
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Referring to the results of Moran's quadrant I, the Quadrant I group is very high
(High-High) there are 11 regions, namely, Lumajang, Magetan, Nganjuk, Pacitan,
Pamekasan, Ponorogo, Probolinggo, Sampang, Situbondo, Trenggalek and
Tulungagung. These districts and cities have high TPT values and are also surrounded
by areas that have high TPT values as well so that they enter quadrant I.
Followed by Quadrant II, namely Low-High, there are 7 regions, including Blitar,
Gresik, Lamongan and Batu Regency, Madiun City, Probolinggo City, Surabaya City.
The regency has a TPT value that is not so high but is surrounded by other districts that
have a high TPT value, one of which is Surabaya City.
In Quadrant III which means low-low, there are 7 regions, namely Banyuwangi,
Bojonegoro, Bondowoso, Jember, Jombang, Kediri, and Kediri City. Quadrant III
indicates that the area has a low TPT value and is surrounded by low regencies or cities
as well.
Quadrant IV or High-Low. There are 6 regions, namely, Madiun, Mojokerto,
Tuban, Pasuruan, Malang and Sidoarjo regencies. The district has a low TPT value but
is surrounded by areas that have a high TPT disease value, one of which is the Sidoarjo
Regency area.
Conclusion
This study aims to analyze the distribution of regions and relationships between
regions related to the open unemployment rate in East Java Province. Unemployment is
a complex problem that affects people's welfare and can lead to social insecurity and
poverty. In this study, spatial analysis methods were used with Moran's I autocorrelation
approach to identify spatial patterns and levels of spatial interaction. The results of
spatial autocorrelation calculations show that the distribution of open unemployment
rates in East Java Province tends to have a random pattern. This study provides a deeper
understanding of the characteristics of the distribution of open unemployment rate in
East Java Province. The results can be used as a basis for identifying areas that require
more attention in addressing the unemployment problem. With a better understanding of
the spatial pattern of unemployment, it is hoped that more effective policies and
programs can be developed to reduce unemployment and improve people's welfare in
East Java Province.
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Bibliography
Arifin, S. R. & Fadllan. (2021). Pengaruh Indeks Pembangunan Manusia (IPM) dan
Tingkat Pengangguran Terhadap Pertumbuhan Ekonomi di Provinsi Jawa Timur
Tahun 2016-2018. IQTISHADIA Jurnal Ekonomi & Perbankan Syariah, 8(1), 38–
59. https://doi.org/10.19105/iqtishadia.v8i1.4555
Giovanni, R. (2018). Analisis Pengaruh PDRB, Pengangguran dan Pendidikan Terhadap
Tingkat Kemiskinan di Pulau Jawa Tahun 2009-2016. Economics Development
Analysis Journa, 7(1), 23–31. https://doi.org/10.15294/edaj.v7i1.21922
Muslim, M. R. (2014). Pengangguran Terbuka dan Determinannya. Jurnal Ekonomi dan
Studi Pembangunan, 15(2), 171–181.
Ningrum, S. S. (2017). Analisis Pengaruh Tingkat Pengangguran Terbuka, Indeks
Pembangunan Manusia, dan Upah Minimum terhadap Jumlah Penduduk Miskin
di Indonesia Tahun 2011-2015. Jurnal Ekonomi Pembangunan, 15(2), 184.
https://doi.org/10.22219/jep.v15i2.5364
Wuryandari, T., Hoyyi, A., Kusumawardani, D. S., & Rahmawati, D. (2014).
Identifikasi Autokorelasi Spasial Pada Jumlah Pengangguran di Jawa Tengah
Menggunakan Indeks Moran. MEDIA STATISTIKA, 7(1), 1–10.
https://doi.org/10.14710/medstat.7.1.1-10
