Coronavirus 2019 (COVID19) has now become a pandemic. In the early stages of the pandemic, Kota Kinabalu was one of the districts in Sabah with the greatest number of COVID19 positive cases. Despite the government’s announcement of the Movement Control Order (MCO), some residents were unable to follow the rules. As a result, the number of COVID19 positive cases in Sabah has increased, particularly in the Kota Kinabalu district. The increase of COVID 19 cases is indeed influenced by the behavior of the population in a place. The behavior of the population during previous pandemics has already shown how human attitudes can affect the spread of disease in the area. In Sabah, it is also seen to occur, such as violating the movement control order. Due to a shortage of manpower, it is difficult to identify and monitor residents who violate this MCO. Geographical factors in Sabah are also among the reasons why authorities find it difficult to keep control over all areas. In addition, the lack of technology, such as Geographic Information System (GIS), has made it difficult for authorities to monitor all locations. As a result, using Principal Component Analysis (PCA), this study was undertaken to identify the primary determinants of population behaviour that cause the spread of COVID19, which was then mapped using Geographic Information System (GIS). Only zones that registered positive cases of COVID19 from March to August 2020 were included in this study, which included a total of 100 respondents in the Kota Kinabalu area. Population behaviour, factor location, and responder location are among the data sources. To investigate the pattern of population behaviour in Kota Kinabalu, this study used factor analysis using PCA and the classification method using GIS. The study’s findings include a behaviour pattern map for the Kota Kinabalu district, which influences COVID19 distribution in the early phases of the pandemic. This study can assist various parties in identifying a potential area in Kota Kinabalu that has a high risk of COVID19 infection.

The campus radio in Malaysia usually established for educational purpose which is important for the student’s development in soft skills. Unfortunately, some campus radio is regarded as not popular and irrelevant due to the emergence of the Internet. Thus, this leads to low utilization of campus radio among students and the public. Although, there are listeners to the campus radio but it is difficult to identify the distribution pattern especially the location of the listeners. Therefore, this study was conducted using spatial analysis to determine the distribution pattern of radio listeners specifically towards campus radio in Universiti Malaysia Sabah (UMS) KKFM. The study was based on the perceptions of the respondents reside around the area of UMS with distance within 15km. Techniques such as interpolation, hotspot and geographic distribution measurement were applied in this study using ArcGIS software. Based on the result, the spatial pattern shows that the public and those reside far from UMS are among the regular listeners of the campus radio. Finally, the outcome of this study indicates that the campus radio is still relevant specifically for certain programmes. This study is important for service improvement and to achieve sustainability of the campus radio.

ABSTRAK Jenayah dikatakan banyak berlaku di bandar dan lebih cenderung bertumpu di sekitar pusat bandar. Jenayah yang paling kerap berlaku di bandar ialah jenayah harta benda dan kekerasan. Peningkatan perlakuan jenayah tersebut di kawasan bandar telah menyebabkan kawasan sekolah di bandar turut terkena kesannya. Oleh itu, kajian ini mengetengahkan kajian jenayah harta benda dan kekerasan di kawasan tumpuan sekolah-sekolah. Sehubungan dengan itu, kurangnya pengkajian mengenai masalah jenayah dari bidang Geografi antara penyumbang kepada kurang berkesannya pencegahan jenayah. Maka, berhubung dengan masalah ini, kajian ini dijalankan dengan bertujuan untuk memetakan jenayah dalam ruang menggunakan aplikasi GIS. Kajian ini menggunakan data statistik jenayah harta benda dan kekerasan Kota Kinabalu bagi tahun 2018 yang telah diperoleh daripada Ibu Pejabat Polis Daerah (IPD) Kota Kinabalu. Data tersebut dimasukkan dalam perisian GIS dan seterusnya dianalisis menggunakan analisis corak iaitu teknik analisis Densiti Kernel. Hasil kajian yang diperoleh merupakan peta corak densiti jenayah harta benda dan kekerasan. Berdasarkan peta corak densiti tersebut, didapati corak densiti tinggi jenayah harta benda terdapat di zon Kota Kinabalu manakala bagi jenayah kekerasan pula terdapat di dua zon iaitu Kota Kinabalu dan Signal Hill. Didapati tiada sekolah yang berada dalam kawasan corak densiti tinggi kedua-dua jenayah tersebut. Kajian ini menyumbang kepada bidang pemetaan jenayah dengan menggunakan analisis GIS. Selain itu, hasil kajian ini dapat membantu pihak-pihak yang terlibat seperti pihak polis, pihak sekolah dan pihak perancang bandar untuk mengatasi perlakuan jenayah yang semakin meningkat di bandar dan semestinya di kawasan tumpuan sekolah-sekolah yang turut terkesan dengan hal tersebut. Di samping itu, kajian ini juga memberi peringatan kepada orang ramai supaya sentiasa menjaga keselamatan terutamanya berada di kawasan yang menjadi tumpuan jenayah.   ABSTRACT Crime is said to be more prevalent in the city and more likely to be concentrated around the city center. The most common crime in the city are property crime and violence. The increase of crime in urban areas has affected the school in the city. Therefore, this study highlights the study of property crime and violence in schools' focus areas. In this regard, the lack of studies on crime issues from the Geography has been one of the contributors to less effective prevention of crime. So, in relation to this problem, this study was conducted with the aim of mapping crime in space using GIS applications. This study uses Kota Kinabalu property crime and violence statistics data for 2018 obtained from the Kota Kinabalu District Police Headquarters (IPD). The data were entered into GIS software and then analyzed using pattern analysis, Kernel density analysis technique. The result of this study is a map of the pattern of property crime and violence. Based on the map of the density pattern, high density patterns of property crime is found in the Kota Kinabalu zone while violent crime found in two zones, Kota Kinabalu and Signal Hill. It was found that no school was in the high-density pattern of the two crimes. This study contributes to the field of crime mapping using GIS analysis. In addition, the findings of this study can help stakeholders such as the police, schools and city planners to overcome the increasing crime rate in the city and of course in the focus area of schools that are also affected by this. In addition, this study also can be used to warn people to always be safe especially in crime-prone areas.

Perintah Kawalan Pergerakan (PKP) telah diisytiharkan di Malaysia pada 17 Mac 2020 untuk memutuskan rantai wabak COVID-19. Sejak saat itu, tidak ada vaksin yang dibuat untuk menyembuhkan penyakit. Oleh itu, PKP adalah kaedah terbaik yang dilaksanakan oleh banyak negara untuk meminimumkan atau membasmi penyakit ini. COVID-19 adalah penyakit berjangkit yang mudah dijangkiti oleh orang lain melalui sentuhan, mulut, hidung dan mata. Oleh itu, penjarakan fizikal antara satu sama lain mesti diamalkan dan tempat yang sesak mesti dielakkan. Walau bagaimanapun, orang ramai cenderung melanggar peraturan PKP dan jarak fizikal. Hal ini terbukti berdasarkan catatan dari fasa 1 hingga fasa 5 PKP di Malaysia. Bilangan kes positif COVID-19 menurun semasa fasa awal PKP tetapi mendapat daya tarikan pada fasa 4 dan 5. Pada masa yang sama, jumlah tenaga kerja di pihak berkuasa terhad dan sukar bagi mereka untuk memantau di semua tempat. Faktor geografi dan jaraknya juga merupakan beberapa cabaran yang harus dihadapi untuk memastikan rakyat mengikuti peraturan PKP. Tujuan kajian ini adalah untuk menganalisis taburan spatial faktor lokasi yang sering dikunjungi orang ramai dengan bantuan analisis spatial melalui Sistem Maklumat Geografi (GIS). Dengan menggunakan teknik pertindanan dan Kernel density dari kaedah analisis spatial, kajian ini kemudian dapat menghasilkan peta kepadatan risiko COVID-19 yang berpotensi. Selepas itu, kajian ini dapat mengenal pasti kawasan potensi risiko COVID-19 dan mengesahkannya dengan lokasi terkini kes positif di daerah Kota Kinabalu, Sabah. Melalui hasil kajian, walaupun tidak mencapai ketepatan yang dikehendaki tetapi ia masih boleh dijadikan sebagai salah satu panduan kepada pihak berkuasa untuk mengawal kawasan yang terlibat. Akhir sekali, penemuan kajian ini sesuai untuk pihak berkuasa bertindak dan memfokuskan kawasan berisiko tinggi penyebaran COVID-19.   Movement Control Order (MCO) has been declared in Malaysia on 17th Mac 2020 to break the chain of the COVID-19 pandemic. Since at that time, no vaccine was made to cure the disease, therefore, the MCO was the best method implemented by many countries to minimize or eradicate the disease. COVID-19 is a contagious disease that can be easily contracted to others based on touch, mouth, nose, and eye. Thus, physical distance from each other must be applied and crowded places must be avoided. However, people tend to violate the MCO ruling and the physical distance. This was evident based on the record from phase 1 to phase 5 of MCO in Malaysia. The number of COVID-19 positive cases were decreased during the early phase of MCO but gain traction in phase 4 and 5. At the same time, the number of manpower in the authority is limited and it was difficult for them to monitor in all places. The geographical factors and the distance were also some of the challenges that they must face to make sure the people follow the MCO ruling. The aim of this study is to analyze the spatial distribution of the location factors that the people frequently visited with the help of spatial analysis through Geographic Information System (GIS). By using the Kernel density and overlay technique from the spatial analysis method, this study could then produce a density map of potential COVID-19 risk. Subsequently, this study manages to identify the area of potential risk of COVID-19 that can be contracted and validate it with the current location of the positive cases in Kota Kinabalu district of Sabah. Although some places unable to show the desired result but it still good enough as one of the guidance for the relevant authorities to take action. Lastly, the findings of this study are suitable for the authorities to act and mainly focused the high-risk area of COVID-19 spreading.

Geographic Information System (GIS) merupakan antara teknologi yang boleh digunakan dalam sektor kesihatan awam untuk mengkaji penyakit dan membantu mengenal pasti sebaran penyakit semasa pandemik. Walau bagaimanapun, penggunaan analisis ruang dalam kajian COVID-19 masih kurang dilakukan terutamanya di Malaysia setakat ini. Oleh itu, objektif kajian ini adalah untuk melakukan tinjauan literatur terhadap penggunaan analisis ruang dalam kajian berkaitan COVID-19. Kertas kerja ini akan mengkaji semula kesusasteraan tentang penggunaan analisis ruang yang digunakan untuk membuat analisis dalam kajian COVID-19 daripada beberapa kajian lepas. Sebanyak empat model regresi ruang yang didapati paling kerap digunakan dalam analisis kajian COVID-19 seperti Geographically weighted regression (GWR), Ordinary Least Square (OLS), Spatial Error Model (SEM), dan Spatial Lag Model (SLM). Akhir sekali, kertas kerja ini akan menyarankan penggunaan analisis ruang dalam kajian berkaitan COVID-19 pada masa akan datang terutamanya di Malaysia.   Geographic Information System (GIS) is one of the technologies that can be used in the public health sector to study diseases and help to identify the spread of diseases during pandemic. However, so far, the use of spatial analysis in COVID-19 study is still lacking, especially in Malaysia. Therefore, the objective of this study is to conduct a literature review on the use of spatial analysis in COVID-19 related studies. This paper will review the literature of spatial analysis that used to make the analysis in the COVID-19 study from previous studies. A total of four spatial regression models were found to be most frequently used in the COVID-19 study analysis such as Geographically weighted regression (GWR), Ordinary Least Square (OLS), Spatial Error Model (SEM), and Spatial Lag Model (SLM). Finally, this paper will suggest the use of spatial analysis in a COVID-19 related studyespecially in Malaysia in future.