bluuebunny/crossref_metadata_2025 huggingface dataset:
Provider reasons for discontinuing long-term opioid therapy following aberrant urine drug tests differ based on the type of substance identified
Authors: Wyse, PhD Jessica J. , Morasco, PhD Benjamin J. , Dobscha, MD Steven K. , Demidenko, BS Michael I. , Meath, MPH Thomas H. A. , Lovejoy, PhD, MPH Travis I.
Published: October 2019

Objective: Urine drug testing (UDT) is increasingly performed as a means of identifying aberrant behavior that may be grounds for discontinuation of long-term opioid therapy (LTOT). Little is known, however, about the ways in which positive UDT results may differentially inform decisions to discontinue LTOT based on the type of substance for which the UDT screened positive. The aim of this study was to examine the likelihood of clinician-initiated discontinuation of LTOT attributed to positive UDT results across three discrete categories of substances: (1) cannabis, (2) alcohol or illicit substances (excluding cannabis), and (3) controlled prescription medications that were not prescribed.Design: This retrospective study utilized the US Department of Veterans Affairs (VA) Health Care System. Corporate Data Warehouse to assemble a sample of 600 patients with substance use disorders and matched controls who were discontinued from LTOT in 2012. Comprehensive manual medical record review identified UDT results in the year prior to discontinuation and reason(s) for discontinuation.Patients, Participants: Patients with one or more UDTs positive for a single substance (N = 185) comprised the study sample.Main Outcome Measure(s): Likelihood of clinician-initiated discontinuation attributed to a positive UDT across the three categories. Results: Patients with one or more UDTs positive for cannabis were more likely to be discontinued from opioid therapy as a result of the positive UDT compared to those with one or more UDTs positive for nonprescribed prescription medication (adjusted odds ratio [OR] = 18.05, 95% CI = 7.29-44.66). Similarly, patients with UDTs positive for alcohol or illicit substances were more likely to be discontinued for the positive UDTs relative to patients who tested positive for nonprescribed prescription medications (adjusted OR = 13.10, 95% CI = 4.81-35.68). No difference in UDT-related discontinuation decisions was evident between patients with UDTs positive for alcohol/illicit substances versus cannabis (adjusted OR = 1.47, 95% CI = 0.57-3.77). Conclusions: High odds of UDT-related discontinuation were found in patients who tested positive for cannabis, alcohol, or illicit substances, relative to nonprescribed prescription medications.
Measuring opioid dependence in chronic pain patients: A comparison between addiction clinic and pain clinic patient populations
Authors: McDonough, MBBS Mike , Johnson, PhD Jacinta L. , White, PhD Jason M. , Buisman-Pijlman, PhD Femke T. A.
Published: October 2019

Objective: To compare dependence characteristics between patients with chronic pain treated within an addiction medicine setting with those attending specialist pain clinics.Setting and patients: Forty patients with chronic non-cancer pain taking opioid analgesics for >1 year were recruited from university-affiliated, tertiary teaching hospital clinics; 20 from an addiction medicine clinic (addiction clinic group) and 20 from specialist pain clinics (pain clinic group).Design and main outcome measures: Data regarding demographics, past and current substance use, pain history and current daily opioid intake were collected. Patients completed three questionnaires: the Severity of Opioid Dependence Questionnaire, Leeds Dependence Questionnaire, and Pain Disability Index. A novel “Opioid Problem Checklist score” assessing drug-related problems was also determined for each patient.Results: The addiction clinic group were younger, more likely to have experienced drug overdose and had a shorter duration of chronic pain. No significant differences in dependence questionnaire scores were found between groups. However, higher Pain Disability Index scores and higher Opioid Problem Checklist scores (indicating more drug-related problems) were found for the addiction clinic group.Conclusions: Some degree of dependence was present across both addiction and pain clinic groups, supporting the notion a state of dependence can be identified among chronic pain patients taking opioids long term. Aberrant behaviors were not common in the pain clinic sample, suggesting these patients are unlikely to meet Diagnostic and Statistical Manual of Mental Disorders-V criteria for Substance Use Disorder. However, opioid dependence carries significant risks for relapse, chronicity, morbidity and mortality, warranting specific medical management. Management of such risks should be considered routine care in chronic pain patients taking opioids long term.
Tapering off long-term methadone: A case study
Authors: Zhou, MD, DPT, LAc Kehua , Frankish, BS Leslie , Wang, MD, PhD Gary G.
Published: October 2019

Opioid tapering may be necessary for patients on long-term opioids. Here, the authors presented a patient who had uncontrolled chronic musculoskeletal pain while on chronic methadone. Upon methadone tapering, the patient had been taking methadone for longer than six years and had severe methadone-related adverse effects. Using multidisciplinary interventions of patient education and counseling, physical interventions, and nonopioid medications, patient’s methadone was discontinued after longer than one year tapering with relatively good pain control. The tapering process highlights the importance of pain management during opioid tapering using multidisciplinary interventions to prevent and treat opioid withdrawal and pain relapses.
Volume 15, Number 4
Authors: Opioid Management Journal of
Published: October 2019

July/August 2019
AN APPROACH OF VICARIOUS CALIBRATION OF SENTINEL-2 SATELLITE MULTISPECTRAL IMAGE BASED ON SPECTRAL LIBRARY FOR MAPPING OIL SPILLS
Authors: Althawadi J. J. A. , Hashim M.
Published: October 2019

Abstract. Sentinel-2 satellite Multispectral Image (MSI) is one of the recent advancement of satellite optical imaging for detecting and tracking oil spills. MSI equipped with enhanced radiometric and spatial resolutions, apart from relatively high temporal resolution of every 5 days revisit capability. Both systematic errors of the geometric and radiometric of level 1 and 2 data were successfully treated before any data download for users’ levels applications. As such, leaving the random errors, crucially to be minimized to enable oil spill detection and tracking due to non-discernible absolute signatures of spills against the scene background and the look-alikes. The magnitude of these random errors’ minimization and the efficacy of the MSI absolute signatures within visible bands for oil spills is very crucial. However, it is rarely reported; in fact, it is a new issue to be addressed accordingly. The calibrating tool was created with oil spill spots revealed by the official authorities. Whereas, the spill pixels are identified in the corresponding pre-processed Sentinel MSI image using region growing segmentation algorithm. These spill pixels grown were analyzed against the RGB bands, logistically regressed against the oil spill via a spectral library of the crude oil type. Originated from Arabian Gulf region with an average film thickness of 0.5 to 4 mm; reporting a calibrating function in a form gain and bias corrections for RGB bands, respectively. The results indicated that calibrated MSI spill pixels have higher correlation (r2 > 0.85, p < 0.001). As the signature variations were used to formulate calibration matrices for spills identified from satellite images which can be used for processing of spill monitoring system.
HOTINE OBLIQUE MERCATOR MAP PROJECTION IN PROJ4JS LIBRARY
Authors: Anshar K. , Suryana N. , Othman M. F. I.
Published: October 2019

Abstract. The implementation of GIS application using hybrid approach on smartphone should have a coordinate transformation capability from one coordinate system into another. One of the available libraries to handle a coordinate transformation is Proj4JS. Unfortunately, this library is not able to perform coordinate transformation from Hotine Oblique Mercator into other projection. One example of this projection is Kertau (RSO) / RSO Malaya (m). We proposed new approach that introduce new formula, algorithm, Proj4JS definition and involve datum transformation to enable the coordinate transformation from Hotine Oblique Mercator into another coordinate system or vice versa. Using the proposed approach, all the features are rendered on top the feature that rendered using Spherical Mercator projection. We compare the result also with the Google Map. It shows that all features are properly rendered on top of Google Map features.
MAPPING THE EFFECTS OF AGRICULTURAL DEVELOPMENT TO WATER YIELD USING REMOTE SENSING
Authors: Ba’iya B. , Hashim M.
Published: October 2019

Abstract. Variation of land cover status, like clearance of forest for agricultural (oil palm) plantation activities, may lead to rapid landscape development. Also, it may cause climate variation effects like excessive rainfall and flooding, which may result in loss of properties and lives. Agricultural (oil palm) plantation‟s dispersed canopy and the open surface of the ground compared to primary forest cause much more runoff and streamflow or discharge. Hence, this paper analyses the land-use changes and evaluate its influence on water yield using the Soil Water Assessment Tools model. Quantitative and qualitative assessment to examine the successive variation from forest to agriculture has been carried out between 2000 and 2010. The results indicate that the conversion of forest to agriculture has a significant influence on hydrological properties of the study area, which cause average water yield increase by 1.93% in the area between 2000 and 2010. Which, in turn, can lead to dryer during the dry season and wetter during rainy seasons. Furthermore, forest and water body are directly proportional. Similarly, forest and agriculture are inversely proportional with R2 > 0.85, p = 0.001. The study shows the ability of Landsat in monitoring the assessment of forest to agriculture, which causes changes in hydrology. This study offers a useful input of planning and sustainable management of surface and water resources.
PREDICTION AND MAPPING OF COCHLODINIUM POLYKRIKOIDES RED TIDE USING MACHINE LEARNING UNDER IMBALANCED DATA
Authors: Bak S. H. , Hwang D. H. , Enkhjargal U. , Yoon H. J.
Published: October 2019

Abstract. Cochlodinium polykrikoides (C. polykrikoides) is a phytoplankton that causes red tides every year in the middle of the South Sea of Korea. C. polykrikoides is a harmful Algae that has migratory ability and causes the fisheries damage over a long period of wide sea area if it causes red tide once. To minimize red tide damage, it is important to anticipate and prepare the red tide occurrence timing and location in advance. In this study, we predicted the occurrence of red tide of C. polykrikoides using machine learning techniques and compared the results of each algorithm. Logistic regression model, decision tree model, and multilayer neural network model were used for prediction of red tide occurrence. To produce the data set for model learning, we used the red tide occurrence map provided by the National Institute of Fisheries Science, the Local Data Assimilation and Prediction System (LDAPS) provided by the Korea Meteorological Agency, and the G1SST provided by the National Oceanic and Atmospheric Administration (NOAA). The feature vectors used for modeling consisted of 59 elements, which were made by using temperature, water temperature, precipitation, solar radiation, wind direction and wind speed. Only a very small number of red tide cases can be collected compared to the case of no red tide cases. Thus, an imbalance data problem arises in the data set. To overcome this imbalanced data problem, we used adding noise after oversampling to data of red tide occurrence to solve the difference of data between two classes.The data set is divided into 8 : 2 to prevent over-fitting and 80% is used as the learning data. The remaining 20% was used to evaluate the performance of each model. As a result of evaluating the prediction performance of each model, the multilayer neural network model showed the highest prediction accuracy.
SPATIAL ANALYSIS OF POVERTY AND HAPPINESS PROFILES IN SPECIAL REGION OF YOGYAKARTA USING SMALL AREA ESTIMATION METHOD
Authors: El-Yasha S. R. , Rizky M. , Wibowo T. W. ,   Sudaryatno
Published: October 2019

Abstract. In March 2017, the Province of Special Region of Yogyakarta (DIY Province) has poverty line of IDR 374,009, percentage of poor people (13.03%) and Gini index (0.432) above the national average (IDR 374,478; 10.64%; 0.393). The result of happiness index in 2017 shows the position of DIY Province (72.93%) is above average of national happiness index (70.69%). Scatterplot between happiness index and percentage of poor people in Indonesia in 2017 shows that DIY Province is on first quadrant. This marks the high level of happiness along with high percentage of poor people. Small area estimation method developed by Elbers et al (known as ELL method) is used to determine spatial characteristics of poverty and happiness profiles in DIY Province. This study used village census data (Podes) 2018; Susenas March 2017 and SPTK 2017 as survey data. There are twenty three household variables and another five variables that are significant to poverty and happiness models at urban and rural provincial level. Rural regency areas dominates high poverty profile (FGT0 0.0491 – 0.1076), low happiness profile (FTG0 0.0087 – 0.0124), and inequality of happiness profile (Gini index 0.0847 – 0.0923). Urban regency areas dominates low poverty profile (FTG0 0.0082 – 0.0491), high happiness profile (FTG0 0 – 0.0087), and perfect equality of both income (Gini index 0.3048 – 0.3604) and happiness profiles (Gini index 0.0624 – 0.0847). Yogyakarta City has happiest and wealthies profiles, whereas Gunung Kidul regency urban area has perfect equality of both income and happiness profiles.
MOBILE INDOOR LASER SCANNING FOR 3D STRATA REGISTRATION PURPOSES BASED ON INDOORGML
Authors: Hashim M. N. , Hassan M. I. , Abdul Rahman A.
Published: October 2019

Abstract. In Malaysia, the current 2D cadastre system is regularly updated by the National Mapping Agency (NMA) and Land Offices (LO). However, this 2D information may not be able to serve complex situations. The 3D strata acquisition and 3D modelling are important for strata title to manage the Right, Restriction and Responsibility (RRRs). This means there is a need for the system to be extended into 3D cadastre environment. One of the data acquisition techniques such as LiDAR from Mobile Laser Scanning (MLS) could be utilised to solve the problem. This research also discusses the 3D geospatial objects generated from the captured point-clouds, modelled in SketchUp and transformed into IndoorGML. In this study, Web application is developed as a platform for generating an integrated XML-IndoorGML schema. Thus, this research contributes on 3D strata modelling especially for the development of 3D strata registration in Malaysia.
MODELING URBAN CRIME PATTERNS USING SPATIAL SPACE TIME AND REGRESSION ANALYSIS
Authors: Hashim H. , Wan Mohd W. M. N. , Sadek E. S. S. M. , Dimyati K. M.
Published: October 2019

Abstract. The population size, population density and rate of urbanization are often crediting to contributing increasing a crime pattern specially in city. Urbanism model stating that the rise in urban crime and social problems is based on three population indicators namely; size, density and heterogeneity. The objective of this paper is to identify crime patterns of the hot spot urban crime location and the factors influencing the crime pattern relationship with population size, population density and rate of urbanization population. This study employed the ArcGIS Pro 2.4 tool such as Emerging Hot Spot Analysis (Space Time) to determine a crime pattern and Ordinary Least Squares (OLS) Regression to determine the factors influencing the crime patterns. By using these analyses tools, this study found that 54 (53%) out of 102 total neighbourhood locations (2011–2017 years) had a 99 percent significance confidence level where z-score exceeded +2.58 with a small p-value (p < 0.01) as the hot spot crime location. The result of data analysis using OLS regression explains that combination of exploratory variable model rate of urbanization and population size contributes 56 percent (R2 = 0.559) variance in crime index rate incident [F (3,39) = 18.779, p < 0.01). While the population density (β = 0.045, t = 0.700, p > 0.10) is not a significance contributes to the change in crime index rate in Petaling and Klang district. The importance of the study is useful information for encouraging government and law enforcement agencies to promote safety and reduce risk of urban population crime areas.
CADASTRAL POSITIONING ACCURACY IMPROVEMENT (PAI): A CASE STUDY OF PRE-REQUISITE DATA QUALITY ASSURANCE
Authors: Hashim N. M. , Omar A. H. , Omar K. M. , Abbas M. A. , Mustafar M. A. , Sulaiman S. A.
Published: October 2019

Abstract. Nowadays, there is an increasing need for comprehensive spatial data management especially digital cadastral database (DCDB). Previously, the cadastral database is in hard copy map, then converted into digital format and subsequently updated. Theoretically, these legacy datasets have relatively low positional accuracy caused by limitation of traditional measurement, adjustment technique and technology changes over time. With the growth of spatial based technology especially Geographical Information System (GIS) and Global Navigation Satellite System (GNSS) the Positional Accuracy Improvement (PAI) to the legacy cadastral database is inevitable. PAI is the refining process of the geometry feature in a geospatial dataset through integration between legacy and higher accuracy dataset to improve its actual position. However, by merely integrating both datasets will lead to a distortion of the relative geometry. Thus, an organized method is required to minimize inherent errors in fitting to the new accurate dataset. The focus of this study is to design a comprehensive data preparation for legacy cadastral datasets improvement. The elements of datum traceability, cadastral error propagation and weightage setting in adjustment will be focused to achieve the targeted objective. The proposed result can be applied as a foundation for PAI approach in cadastral database modernization.
BIO OPTICAL MODELLING OF LAGUNA LAKE USING BOMBER TOOL AND WASI-DERIVED INVERTED PARAMETERS
Authors: Jalbuena R. L. , Blanco A. C. , Manuel A. , Sta. Ana R. R. , Santos J. A.
Published: October 2019

Abstract. Laguna Lake is significant to its surrounding cities and municipalities as it serves multiple purposes: flood basin, aquaculture, water source for irrigation and domestic use, among others. Monitoring the lake’s water quality is an integral part ensuring that the lake would continue to serve its purposes. Bio-optical modelling is a type of empirical model that relates the inherent optical properties of water to different biological properties like chlorophyll-a. The BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) tool makes use of the different IOPs apparent optical properties (AOPs) of satellite images to be able to produce water quality maps. To localize the parameters used by the BOMBER tool, the use of WASI (The Water Color Simulator) tool was introduced. Inverting in situ spectral measurements of the lake, WASI tool was able to produce parameters localized for the lake. This research used 2018 Landsat 8 Images to produce images and used a water profiler to validate results. Results show the bio-optical model provided a R-squared value of 0.6912 and an RMSE of 2.43 μg/l which shows good correlation between the in-situ and the bio-optical model results.
SENTINEL-1 IMAGE CLASSIFICATION FOR CITY EXTRACTION BASED ON THE SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS
Authors: Jamali A. , Abdul Rahman A.
Published: October 2019

Abstract. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct impact on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. Considering eight different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 90.81 and 92.09 percent respectively.
MULTI-SCALE AND SCALE DIMENSION PROPERTIES IN SPATIAL RASTER MODELLING – CONCEPT AND CURRENT IMPLEMENTATION
Authors: Karim H. , Abdul Rahman A. , Mohd Salleh M. R.
Published: October 2019

Abstract. Various users and applications required different abstraction details of spatial model either in vector or/and raster data types/models. Generating different model abstraction details (e.g. Level of Detail/LOD) produces various drawbacks especially for data model sharing among stakeholders or publics. Different abstraction detail or LOD means different details in geometry, semantic information, attributes as well as different accuracy provided within the vector model (e.g. a certain LOD). On the other hand, raster dataset with different resolutions on certain information or layer (e.g. elevation, land cover, spatial imagery, soil type, thematic raster map and others) could also be considered as multi-scale raster modelling which produces similar drawbacks with additional storage redundancy/consumption and updating works. There are some solutions for vector scale modelling such as CityGML (3D) and multi-scale or vario-scale (2D) modelling induce good solutions for vector; however, there are no solution for raster data type (or model) yet. Thus, a concept description in categorizing and defining multi-scale for multi-resolution raster dataset should be introduced. This paper basically highlights the similarity of spatial 2D vector and raster type GIS dataset, some introduction and properties of raster dataset which able to be defined it as the same level of vector LoD in scale modelling. This paper basically tries to kick off a new multi-scale domain in supporting spatial raster dataset (new idea), which will be then be extend/expand by related researchers near the future. Discussion on successful implementation of vector multi-scale model will be in the paper as well as existing multi-scale approach in storing raster dataset as the main content of the paper. Some potential analysis on related multi-scale raster and validation are also discussed to give brief idea on what is spatial raster capable of; especially to those who are new/not yet engage with this multi-scale spatial raster dataset.
BIG DATA PROCESSING AND ANALYSIS USING MULTI-TEMPORAL SENTINEL 1 DATA TO MANAGE FLOODS IN NORTH OF MOROCCO
Authors: Lahsaini M. , Tabyaoui H. , El Hammichi F.
Published: October 2019

Abstract. Floods are the natural hazards that produce the highest number of casualties and material damage in the Western Mediterranean, especially in Morocco. An improvement in flood risk assessment and study of a possible increase in flooding occurrence are therefore needed. Earth Observation big data such as the ones acquired by the Copernicus programme are providing unprecedented opportunities to detect changes and assess economic impacts in case of disasters. This article present the different results obtained by the multi-temporal methods using the Synthetic Aperture Radar images. The spaceborne Synthetic Aperture Radar (SAR) systems are suitable tools for flood mapping thanks to their daytime and nighttime and almost all-weather imaging capability, in addition to their sensitivity to surface roughness and to Flood monitoring. The method has been developed to exploit Sentinel-1 data. It has been tested for the 2018 flood of Tetouan (Morocco).
CONSIDERATION AND EXPLORATION ON THE DEMAND CHANGES OF SURVEYING AND MAPPING GEOGRAPHIC INFORMATION
Authors: Liu X. L. , Li C. M.
Published: October 2019

Abstract. This article primarily describes the nature of geoinformatics, including the role of each of its components as understood today, and illustrates how the change in demand and the progress in science and technology are the two driving forces for the development of this subject. The changes in popular needs are next analyzed in detail, focusing on four aspects: the user group, intended usage, medium, and service. Lastly, it points out that geoinformatics work should be conducted following the development of geographic information.
INTEGRATION BETWEEN UNMANNED AERIAL VEHICLE AND TERRESTRIAL LASER SCANNER IN PRODUCING 3D MODEL
Authors: Mat Adnan A. , Darwin N. , Ariff M. F. M. , Majid Z. , Idris K. M.
Published: October 2019

Abstract. Unmanned Aerial Vehicles (UAV) frequently used for obtaining 2D or 3D data acquisition. Meanwhile, Terrestrial Laser Scanners (TLS) are used for obtaining only 3D data acquisition. However if both are integrated, they were able to produce a more accurate data. The purpose of this study is to investigate the possible integration of point clouds obtained by TLS with UAV images at T06 FBES building through the aerial survey where the roof is scanned and ground survey which scans the facades‟ building. Topcon GLS 2000 and DJI Inspire 1 UAV were used to acquire the data at the field. The aerial data and ground data were processed using Pix4D and Scanmaster respectively. The data integration process is done by converting both point clouds into the same coordinate system and then by aligning the same points of both points clouds in Cloud Compare. For verification purposes, dimensional survey was done and there are several distances were taken from the study area to validate the accuracy assessment. The result of residuals between the dimension survey and integration is 0.183 m which is below 1 meter. The result of this study is a 3D model of UTM T06 FBES building based on the point cloud accuracy in cm level. To conclude, the integration between these two methods can be implemented to produce an accurate 3D model.
SEISMIC STRAIN MAP IN MALAYSIA DERIVED FROM LONG-TERM GLOBAL POSITIONING SYSTEM DATA
Authors: Mohamad M. S. , Aris W. A. W. , Jaffar N. J. , Othman R.
Published: October 2019

Abstract. Series of major earthquakes struck the Sundaland plate as a result of convergence with neighboring plates such as Indian, Australian and Philippine plates. Since then, the Sundaland is experiencing significant crustal deformation that implicate reactivation of local fault and embark question on the status of geohazard and seismic risk. In Malaysia, crustal deformation study by using Global Positioning System (GPS) has been conducted for many years. However, the information of crustal deformation was reported separately and difficult to be archived. In addition, continuous estimation of crustal deformation derived from GPS has to be carried out in order to provide present day seismic status. This study aims at generating a seismic catalog map in Malaysia derived from approximately nine (9) years of GPS data. In this study, derived long-term crustal deformation in the form of coordinate time series (CTS) were converted into yearly strain map. The changes of strain with respect to location of old and active fault line in Malaysia were properly analysed. From the result, the highest changes of strain rate for Peninsular Malaysia happened in 2004 until 2005 and 2012 until 2013 prior to 2004 Acheh earthquake event with the moment magnitude (Mw) and 2012 two strike-slip events in Northern Sumatera with the magnitude of 8.2Mw and 8.6Mw. In North Borneo region, the most significant changes of strain rate happened from 2007 to 2009 and 2011 to 2013. It can be expected that the results will be beneficial in augmenting geohazard mitigation in Malaysia.
DEVELOPMENT OF GEODATABASE FOR ARCHAEOLOGICAL SITE IN BUJANG VALLEY KEDAH
Authors: Mohd Aminuddin N. U. , Majid Z. , Ahmad Fuad N. , Aspuri A. , Mohd Salleh M. F. , Ariff M. F. M. , Idris K. M. , Darwin N.
Published: October 2019

Abstract. In recent years, there has been little adoption of geospatial technology applications towards the archaeological excavation project in Malaysia which yields increasing amount of data on historical assets. Those data however been processed and managed via conventional method of paper form- based and less associated spatial data which is actually can be necessary to improve the finding method of potential archaeological sites. This paper presents the application of Geographical Information System (GIS) towards the archaeology data management by adopting geodatabase for storing archaeology information and visualizing the archaeological monuments via method of 3D Terrestrial Laser Scanning (TLS). The research area is located within the Bujang Valley Archaeological Museum, Merbok, Kedah which known to locate most of the ancient relics that being preserved over time. The TLS method is used to scan the interior and exterior structures of the monuments due to its capability in representing 3D visualization digitally from point cloud data and close to the accuracy of the actual structure. In addition, the geodatabase can provide the organization a better medium to create large-scale databases for organizing, analyzing and sharing the products of the field research with other users. The GIS capability to capture, integrate, store, edit, analyze and display geospatial data can really help the effort in preserving the archaeological information from lost over time.
ORIENTATION-BASED PAIRWISE COARSE REGISTRATION WITH MARKERLESS TERRESTRIAL LASER SCANS
Authors: Mohd Isa S. N. , Abdul Shukor S. A. , Rahim N. A. , Maarof I. , Yahya Z. R. , Zakaria A. , Abdullah A. H. , Wong R.
Published: October 2019

Abstract. In this paper, pairwise coarse registration is presented using real world point cloud data obtained by terrestrial laser scanner and without information on reference marker on the scene. The challenge in the data is because of multi-scanning which caused large data size in millions of points due to limited range about the scene generated from side view. Furthermore, the data have a low percentage of overlapping between two scans, and the point cloud data were acquired from structures with geometrical symmetry which leads to minimal transformation during registration process. To process the data, 3D Harris keypoint is used and coarse registration is done by Iterative Closest Point (ICP). Different sampling methods were applied in order to evaluate processing time for further analysis on different voxel grid size. Then, Root Means Squared Error (RMSE) is used to determine the accuracy of the approach and to study its relation to relative orientation of scan by pairwise registration. The results show that the grid average downsampling method gives shorter processing time with reasonable RMSE in finding the exact scan pair. It can also be seen that grid step size is having an inverse relationship with downsampling points. This setting is used to test on smaller overlapping data set of other heritage building. Evaluation on relative orientation is studied from transformation parameter for both data set, where Data set I, which higher overlapping data gives better accuracy which may be due to the small distance between the two point clouds compared to Data set II.
LAND USE/LAND COVER ASSESSMENT IN A SEISMICALLY ACTIVE REGION IN KUNDASANG, SABAH
Authors: Mohd Kamal N. A. , Razak K. A. , Rambat S.
Published: October 2019

Abstract. Land use development in the mountainous environment must be risk-informed especially in the area highly vulnerable to disaster and extreme climate. Kundasang, Sabah, Malaysia is one of the tourist-demanding areas characterized by mountainous landscape and agriculture activity. The increasing number of tourists and agriculture activity affects the land use exploration. This area is vulnerable to geohazards such as earthquakes and landslides due to its location under seismically active region and complex geological environment. In this study, geospatial technique was used to assess the land use activity in Kundasang, Sabah pertaining to geological risk in this area. The assessment started with the identification of geohazard activities and its associated tectonic features using field investigation and mapping for coherent visualization. Subsequently, multiple high-resolution satellite imageries were used to detect land use changes before and after the disaster. In order to detect the land use change, object-based change detection was applied based on segmentation and object-based classification compared to the classical pixel-based method. The output of this study shows a number of field evidences associated with geohazard features that affecting the land use activities especially build-up area and agriculture land. In conclusion, the combined results provide an important benefit for better understanding the interaction between geohazard activity and landscape patterns in order to support the planning and decision making through spatial analysis and appropriate object-based processing method.
SPATIAL PREDICTION MODELS FOR LANDSLIDE ACTIVITY MAPPING USING VEGETATION ANOMALIES
Authors: Mohd Salleh M. R. , Ismail Z. , Mohd Ariff S. A. , Abd Rahman M. Z. , Abdul Khanan M. F. , Asmadi M. A. , Razak K. A.
Published: October 2019

Abstract. An area that located in Kundasang which in Ranau district in Sabah, Malaysia that lies along the bank of Kundasang valley was chosen for comparing the reliability of frequency ratio (FR) and weight of evidence (WoE) methods for landslide activity probability mapping by using related vegetation anomalies indicator. The locations of 47 and 189 of active and dormant landslides respectively were identified using 4 raster layers (topographic openness, hillshade, colour composite and high resolution orthophoto). Each landslide activites were randomly divided into two groups as training (70%) and testing (30%) datasets. Tree height irregularities, DVI, NDVI, SAVI, and OSAVI were considered as landslide bio-indicator. The landslide activity probability maps were prepared using the FR and WoE method. The generated maps were validated by calculating the success and prediction rates from area under receiver operating characteristics (ROC) curve. The results of WoE method were relatively reliable (AUC > 0.8) for dormant landslide while only about 40% of active landslide have been predicted accurately. Similar trend yielded for FR method where least accuracy for active landslide prediction.
THE CAPABILITIES OF UNMANNED AERIAL VEHICLE FOR SLOPE CLASSIFICATION
Authors: Mokhtar N. M. , Darwin N. , Ariff M. F. M. , Majid Z. , Idris K. M.
Published: October 2019

Abstract. Slope classification mapping is an important component of land suitability analysis for preventing landslides. This study aim to investigate the capabilities and application of Unmanned Aerial Vehicle (UAV) platform for slope classification. The objectives of this study such as investigating the capabilities of UAV for slope classification, generating Digital Elevation Model (DEM) and orthophoto from the image acquired and assessing the accuracy of DEM and orthophoto produced for slope classification. In this study, the aerial image was acquired using UAV at 60 m and 40 m altitude will then generates the DEM and orthophoto used to produce the slope map and classify the slope. The UAV data was validated with the check points observed from ground survey using GPS to obtain the Root Mean Square Error (RMSE) values. The RMSE value for UAV derived DEM at 60 m altitude is ±0.234 m and ±0.604 m for X and Y respectively. The average RMSE is ±0.279 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.616 m. The RMSE value for UAV derived DEM at 40 m altitude is ±0.596 m and ±0.405 for X and Y respectively. The average RMSE is ±0.334 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.450 m. In conclusion, it shows that the RMSE value obtained from UAV derived DEM is smaller than the RMSE value obtained from LiDAR derived DEM. Hence, UAV is capable for the generation of slope map and slope classification.
SEAGRASS HABITAT SUITABILITY MAP AT MERAMBONG SHOAL, JOHOR: A PRELIMINARY STUDY USING MULTIBEAM ECHOSOUNDER AND MAXENT MODELLING
Authors: Muhamad M. A. H. , Che Hasan R.
Published: October 2019

Abstract. In recent years, there has been an increasing interest to use high-resolution multibeam dataset and Species Distribution Modelling (SDM) for seagrass habitat suitability model. This requires a specific variable derived from multibeam data and in-situ seagrass occurrence samples. The purpose of this study was (1) to derive variables from multibeam bathymetry data to be used in seagrass habitat suitability model, (2) to produce seagrass habitat suitability model using Maximum Entropy (MaxEnt), and (3) to quantify the contribution of each variable for predicting seagrass habitat suitability map. The study area was located at Merambong Shoal, covering an area of 0.04 km2, situated along Johor Strait. First, twelve (12) variables were derived from bathymetry data collected from multibeam echosounder using Benthic Terrain Modeller (BTM) tool. Secondly, all variables and seagrass occurrence samples were integrated in MaxEnt to produce seagrass habitat suitability map. The results showed that the Area Under Curve (AUC) values based on training and test data were 0.88 and 0.65, respectively. The northwest region of survey area indicated higher habitat suitability of seagrass, while the southeast region of survey area indicated lower suitability. Bathymetry mean found to be the most contributed variables among others. The spatial distribution of seagrass from modelling technique agreed with the previous studies and they are found to be distributed at depths ranging from 2.2 to 3.4 meters whilst less suitable with increasing of water depth. This study concludes that seagrass habitat suitability map with high-resolution pixel size (0.5 meter) can be produced at Merambong Shoal using acoustic data from multibeam echosounder coupled with MaxEnt and underwater video observations.
A REVIEW ON MEASURING ENTREPRENEURS' SOCIAL BEHAVIOUR VIA SPATIAL ANALYSIS TECHNIQUES
Authors: Nawawi N. M. , Ujang U. , Azri S. , Choon T. L.
Published: October 2019

Abstract. This article reviews the subject of entrepreneur with a focus on the components of the entrepreneurial personality traits that are reflected in entrepreneur behaviour. Human behaviour receives a warm welcome in social psychology for examination and measurement of individual social behaviour. There are a number of tools to measure human behaviour in social science such as Likert scale, Indices, Social Network Analysis, E-scan and lastly most popular Big Five Factor Model. In entrepreneurship, entrepreneur’s personality can be reflected in their action, activity, and behaviour in certain space such as traveling to find raw resources in certain distance or their decision on size of the shop. Using in-depth observation and examination of this behaviour, there are several spatial elements that could lead to the possibility in implementing spatial measurement in entrepreneur behaviour. However, there is a scarcity of research and implementation of the spatial measurement on entrepreneur behaviour, even though it is already acknowledged that space influences human behaviour and vice versa human behaviour has an impact on space. Thus, this article aims to highlight the possibility of implementing spatial measurement in entrepreneur behaviour and current exploration of spatial measurement for human behaviours.
THE POTENTIAL OF BUILDING DETECTION FROM SAR AND LIDAR USING DEEP LEARNING
Authors: Nordin Z. , Shafri H. Z. M. , Abdullah A. F. , Hashim S. J.
Published: October 2019

Abstract. The introduction of airborne Synthetic Aperture Radar (SAR) approach has successfully addressed several challenges for mapping and surveying applications Unlike other conventional sensors, airborne SAR mapping approach offers practicality and significant cost savings for the nation minimizing the need for ground control points on the ground in addition to providing high-resolution, day-and-night, cloud coverage and weather independent images, which in turn provides faster turnaround times for creation of large area geospatial data. Up-to-date building map is necessary to guide the decision making in many fields to understand the urban dynamics such as in disaster management, population estimation, planning and many other applications. Whilst mapping and surveying work using airborne SAR have started to capture many interest among surveyors, professionals and practitioners abroad, Malaysia however is still lacking behind in term of the knowledge and the usage of this technology together with Deep Learning, Machine Learning approach especially in building extraction for topographic mapping and urban planning and development. Deep learning is a subset of the machine learning algorithm. Recently, Deep Learning has been proposed to solve traditional artificial intelligent problems. In order to develop a sustainable national geospatial infrastructure for years to come, the integration between airborne SAR and other sensors as such LIDAR is therefore essential in Malaysia and in high demand for urban planning and management. Thus, this paper reviews current techniques and future trends of multi-sources Remote Sensing for building extraction.
ACCURACY OF UNSUPERVISED CLASSIFICATION TO DETERMINE CORAL HEALTH USING SPOT-6 AND SENTINEL-2A
Authors: Nurdin N. ,   Supriadi, Lanuru M. , Akbar AS M. , Kartika I. , Komatsu T.
Published: October 2019

Abstract. Characteristics of corals spectral from different species are expected to have optically different characters. The aims of this research are to compare unsupervised classification between IsoData and K-Means methods with Lyzenga application, and to analyze the precision of SPOT-6 and Sentinel-2A satellite imagery in classsifying shallow water habitat. The image processing are atmosferic correction, cropping, masking, Depth Invariant Index, Unsupervised classification, ground truthing, reclassify, accuracy assessment, and shallow water habitat spectral reflectance analysis. Rubble and dead coral with algae were indicating as coral death due to either damaging human activity or natural death such as bleaching. The accuracy of unsupervised classification IsoData and K-Means method have the same accuracy 62.50%. The IsoData method is better detected live coral and algae. Rubble were dominant detected in K-Means method.
FOREST DEGRADATION ANALYSIS USING GEOSPATIAL TECHNIQUES APPROACH IN TROPICAL REGION OF PAHANG, MALAYSIA
Authors: Othman M. A. , Ash’aari Z. H. , Aris A. Z. , Ramli M. F.
Published: October 2019

Abstract. Tropical forest degradation is a global concern since most deforestation study supports that the region seems to suffer a great loss in the recent decades. The main objective of this paper is to evaluate the forest dynamic in Rompin and Pekan district of Pahang state using satellite image and land use model. Pekan district has the biggest peat swamp forest (PSF) in Peninsular Malaysia and also covers with inland dipterocarp forest. The land use of study area was categorized using Maximum Likelihood Classification (MLC) and further analysed using Land Change Modeler (LCM). From the results, forest land use has a negative growth (15% net loss) in the last 25 years, mostly converted to other vegetation class with overall 45% loss and 20% gain. The PSF itself was continuously degraded during the 1990 to 2017 periods, with a total loss of more than 700 km2. This study demonstrates the forest land use exploitation and the needs to consider a details forest monitoring analysis to extract the actual contributors for this phenomenon. The role of Permanent Reserved Forest (PRF) seem likely to be taking effect since the deforestation only massively happen outside of the PRF and part of the disturbed area are being reforested.
ASSESSMENT OF SUSTAINABILITY OF AFFORDABLE HOUSING IN MALAYSIA
Authors: Rizal N. , Tarmidi Z. , Razali N. , Pisol S.
Published: October 2019

Abstract. Housing is a basic need that can enhance the quality of life. The government is committed towards ensuring access to quality and affordable housing to meet the needs of a growing population by matching demand and supply, promoting an efficient and sustainable housing industry, as well as providing efficient public utilities and services and a clean environment. Thus, it is imperative for the government and private sector, to work together in order to create a competitive and sustainable housing industry. One of the aspect need improve is the sustainability level of this affordable housing. The aim of this study is to enhance the model to measure the sustainability level of the affordable housing in Malaysia using spatial analysis. The spatial analysis used in this study are both GIS analysis and non-GIS analysis. The result of this study will be list of indicators to measure the sustainability level of affordable housing, and then, from the analysis, map of the sustainability indicators nearby the affordable housing can be produced.
EXPLORING SPATIO-TEMPORAL WAVE PATTERN USING UNSUPERVISED TECHNIQUE
Authors: Rohana N. A. , Yusof N. , Uti M. N. , Din A. H. M.
Published: October 2019

Abstract. The sea waves are the up and down movements of water in the sea. The various heights of sea waves are known as significant wave heights. Each type of wave has their own characteristics based on their significant wave heights. The aim of this research is to explore spatio-temporal wave patterns and their effects on Tok Jembal coastal areas. For this study, the monthly wave data were obtained from the satellite altimeters that have been processed using Radar Altimeter Database System (RADS). The Self Organizing Map (SOM) method was used to extract the spatio-temporal wave height patterns from the monthly wave height data. From the clustering results, six number of clusters were extracted and then each of these clusters was categorized into specific type of wave heights. In addition, time series of Landsat satellite images were used to observe the coastal changes at Tok Jembal areas. Finally, we analyzed the effects of spatio-temporal wave patterns towards the occurrences of coastal erosion along the coastal areas. This study has discovered that the wave heights along the coastal areas fall in slight category and showed less effects on the erosion. From the visual interpretation of time- series images (10 years gap) also proved that the erosion can be considered as moderate. Overall, this study could benefit the coastal management especially for shoreline monitoring where early action can be taken when there are signs of erosion along the coast.