ANALISIS HASIL PERTANIAN DENGAN MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS (Studi Kasus : Kota Denpasar)

Agriculture is an activity in utilizing natural resources made by humans so that they can produce food, raw materials for industrial enterprises, energy and natural balance.The limited agricultural land in Denpasar from year to year will affect agricultural output. Based on these problems will require analysis of agricultural products to determine the increase or decrease in agricultural output in Denpasar using Geographic Information Systems (GIS) and ArcView GIS 3.3 as a tool that can be used as an ingredient in decision making by the government. The method used in this research is the collection of spatial data and non-spatial data, data analysis of agricultural products in their respective districts in the city of Denpasar and perform digitization into map digital. Results from this study is a digital map that provides information on the mapping of agricultural products in 2011 -2014 in each district in the city of Denpasar

Segmentation-free inference of cell types from in situ transcriptomics data

Recent advances in the fields of genome editing, whole-genome sequencing, single-cell RNA sequencing, and in situ spatial transcriptomics have enabled the cost-efficient production of high-throughput big data. However, the lack of dedicated bioinformatics algorithms to analyze such data has been a big hurdle. In this thesis, several novel bioinformatics tools applicable to each field are presented. First, a series of web-based tools for genome editing are presented: Cpf1-Database, Cas-Analyzer, web-based Digenome-seq software, BE-Designer/Analyzer. These tools have been developed to guide researchers to easily use genome editing systems, using Cas9 or Cpf1, by providing an easily accessible web-based interface. Second, the development of two bioinformatics pipelines are described: a small variant calling pipeline to process tumor genome sequencing data without a matched control, and a pipeline to pre-process single-cell RNA sequencing data. Third, a novel segmentation-free algorithm to call cell-types from in situ transcriptomics data, namely Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM) is presented. Recent advances of in situ spatial transcriptomics techniques, such as multiplexed fluorescence in situ hybridization or in situ/intact tissue sequencing have enabled the discovery of spatial heterogeneity of cell types at the tissue level. However, cell type calling methods are often limited by cell segmentation algorithms due to various imaging problems. SSAM circumvents these problems by estimating spatial gene expressions as a density estimation of the mRNA in a spatial context and identifying de novo cell-types and their spatial organization without the need to segment cells. Optionally, SSAM can be guided by external sources of cell-type information, integrating them in a spatial context. In this thesis, SSAM is demonstrated with three different mouse brain tissues imaged by different imaging techniques: the somatosensory cortex (SSp) imaged by osmFISH; the hypothalamic preoptic region (POA) by MERFISH; and the visual cortex (VISp) by multiplexed smFISH. SSAM can produce similar results compared to segmentation-based methods and outperforms them when cell segmentation is the limiting factor. In summary, the bioinformatics tools presented in this thesis overcome major obstacles that would normally hinder effective analysis: the web-based tools for genome editing have a wide user base due to their easy-to-use web-based interfaces; omics data analysis pipeline that enables fast analysis of omics data utilizing a compute cluster and facilitate hypothesis generation when lacking control tissue; and SSAM that enables the analysis of in situ spatial transcriptomics data without being limited by cell segmentation. All of the tools and pipelines described in this thesis are open-sourced and freely accessible for non-profit, research-purpose use

PEMETAAN FASILITAS PENDIDIKAN SEKOLAH MENENGAH ATAS DI KOTA DUMAI DENGAN MEMANFAATKAN SISTEM INFORMASI GEOGRAFIS

Pelayanan pendidikah harus merata dan menjangkau setiap warga negara. Hak mendapat pelayanan pendidikan tanpa diskriminasi bagi setiap warga negara Indonesia telah dijamin oleh peraturan yang berlaku di Indonesia. Artinya setiap warga negara Indonesia, dimana saja, harus memiliki kesempatan yang sama dalam mengakses pendidikan. Berdasarkan statistik pendidikan Kota Dumai, jumlah fasilitas pendidikan SMA di Kota Dumai secara keseluruhan telah dapat melayani seluruh kebutuhan di Kota Dumai hanya saja persebarannya di setiap Kecamatan belum merata. Dengan demikian perlu dilakukan tinjauan evaluatif terhadap persebaran fasilitas pendidikan SMA di Kota Dumai. Untuk mengevaluasi persebaran fasilitas pendidikan SMA ini dibutuhkan suatu sistem informasi yang mampu mengintegrasikan dan mengolah data spasial dan non spasial. Kemampuan dalam mengintegrasikan data spasial dan non spasial ini dapat ditemukan dalam sistem informasi geografis. Oleh karena itu pada studi ini dilakukan pemetaan sekolah (school mapping) dengan memanfaatkan sistem informasi geografis. Penelitian ini bertujuan menganalisis tentang: 1) sebaran sekolah menengah atas di Kota Dumai dengan memanfaatkan sistem informasi geografis, 2) ketersediaan fasilitas sekolah menengah atas di Kota Dumai dengan memanfaatkan sistem informasi geografis dan 3) kesesuaian fasilitas sekolah menengah atas dengan peserta didik di Kota Dumai dengan memanfaatkan sistem informasi geografis. Metode yang digunakan dalam penelitian ini adalah Sistem Informasi Geografis dengan teknik survey dan pemetaan yang didukung dengan analisis data sekunder. Hasil analisis menunjukkan bahwa: 1) persebaran SMA memusat di bagian tengah Kota Dumai; 2) ketersediaan fasilitas pendidikan SMA di Kota Dumai jumlah tertinggi bernilai 27 dimiliki oleh SMAN 3, sedangkan yang terendah bernilai 18 dimiliki oleh SMAN 4; 3) Sebanyak 88.92% penduduk usia sekolah di Kota Dumai dapat tertampung di fasilitas yang sudah tersedia. ;--- Educational services should be equitable and reach every citizen. The right to receive non-discriminatory educational services for every Indonesian citizen has been guaranteed by the prevailing laws of Indonesia. This means that every Indonesian citizen, everywhere, should have equal opportunity in accessing education. Based on the educational statistics of Dumai City, the number of high school education facilities in Dumai City as a whole has been able to serve all needs in Dumai City, but its distribution in every district is not evenly distributed. Thus an evaluative review of the high school education facility in Dumai City is required. To evaluate the distribution of high school education facilities required an information system that is able to integrate and process spatial and non spatial data. The ability to integrate spatial and non-spatial data can be found in geographic information systems. Therefore, in this study mapping school (school mapping) by utilizing geographic information systems. This study aims to analyze: 1) distribution of high school in Dumai City by utilizing geographic information systems, 2) availability of high school facilities in Dumai City by utilizing geographic information systems and 3) suitability of high school facilities with learners in Dumai City by utilizing geographic information systems. The method used in this study is geographic information system with survey and mapping techniques supported by secondary data analysis. The results of the analysis show; 1) the senior high school distribution centered in the central part og the city; 2) the availability of high school education facilities in Dumai the highest number of 27 is owned by senior high school 3, while the lowest value of 18 is owned by senior high school 4; 3) 88.92% of school-age residents in the city of dumai can be accommodated at an existing facility

Sparse representations and harmonic wavelets for stochastic modeling and analysis of diverse structural systems and related excitations

In this thesis, novel analytical and computational approaches are proposed for addressing several topics in the field of random vibration. The first topic pertains to the stochastic response determination of systems with singular parameter matrices. Such systems appear, indicatively, when a redundant coordinate modeling scheme is adopted. This is often associated with computational cost-efficient solution frameworks and modeling flexibility for treating complex systems. Further, structures are subject to environmental excitations, such as ground motions, that typically exhibit non-stationary characteristics. In this regard, aiming at a joint time-frequency analysis of the system response a recently developed generalized harmonic wavelet (GHW)-based solution framework is employed in conjunction with tools originated form the generalized matrix inverse theory. This leads to a generalization of earlier excitation-response relationships of random vibration theory to account for systems with singular matrices. Harmonic wavelet-based statistical linearization techniques are also extended to nonlinear multi-degree-of-freedom (MDOF) systems with singular matrices. The accuracy of the herein proposed framework is further improved by circumventing previous “local stationarity” assumptions about the response. Furthermore, the applicability of the method is extended beyond redundant coordinate modeling applications. This is achieved by a formulation which accounts for generally constrained equations of motion pertaining to diverse engineering applications. These include, indicatively, energy harvesters with coupled electromechanical equations and oscillators subject to non-white excitations modeled via auxiliary filter equations. The second topic relates to the probabilistic modeling of excitation processes in the presence of missing data. In this regard, a compressive sampling methodology is developed for incomplete wind time-histories reconstruction and extrapolation in a single spatial dimension, as well as for related stochastic field statistics estimation. An alternative methodology based on low rank matrices and nuclear norm minimization is also developed for wind field extrapolation in two spatial dimensions. The proposed framework can be employed for monitoring of wind turbine systems utilizing information from a few measured locations as well as in the context of performance-based design optimization of structural systems. Lastly, the problem of with data-driven sparse identification methods of nonlinear dynamics is considered. In particular, utilizing measured responses a Bayesian compressive sampling technique is developed for determining the governing equations of stochastically excited structural systems exhibiting diverse nonlinear behaviors and also endowed with fractional derivative elements. Compared to alternative state-of-the-art schemes that yield deterministic estimates for the identified model, the herein developed methodology exhibits additional sparsity promoting features and is capable of quantifying the uncertainty associated with the model estimates. This provides a quantifiable degree of confidence when employing the proposed framework as a predictive tool

The Repast Simulation/Modelling System for Geospatial Simulation

The use of simulation/modelling systems can simplify the implementation of agent-based models. Repast is one of the few simulation/modelling software systems that supports the integration of geospatial data especially that of vector-based geometries. This paper provides details about Repast specifically an overview, including its different development languages available to develop agent-based models. Before describing Repast’s core functionality and how models can be developed within it, specific emphasis will be placed on its ability to represent dynamics and incorporate geographical information. Once these elements of the system have been covered, a diverse list of Agent-Based Modelling (ABM) applications using Repast will be presented with particular emphasis on spatial applications utilizing Repast, in particular, those that utilize geospatial data

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Designing Improved Sediment Transport Visualizations

Monitoring, or more commonly, modeling of sediment transport in the coastal environment is a critical task with relevance to coastline stability, beach erosion, tracking environmental contaminants, and safety of navigation. Increased intensity and regularity of storms such as Superstorm Sandy heighten the importance of our understanding of sediment transport processes. A weakness of current modeling capabilities is the ability to easily visualize the result in an intuitive manner. Many of the available visualization software packages display only a single variable at once, usually as a two-dimensional, plan-view cross-section. With such limited display capabilities, sophisticated 3D models are undermined in both the interpretation of results and dissemination of information to the public. Here we explore a subset of existing modeling capabilities (specifically, modeling scour around man-made structures) and visualization solutions, examine their shortcomings and present a design for a 4D visualization for sediment transport studies that is based on perceptually-focused data visualization research and recent and ongoing developments in multivariate displays. Vector and scalar fields are co-displayed, yet kept independently identifiable utilizing human perception\u27s separation of color, texture, and motion. Bathymetry, sediment grain-size distribution, and forcing hydrodynamics are a subset of the variables investigated for simultaneous representation. Direct interaction with field data is tested to support rapid validation of sediment transport model results. Our goal is a tight integration of both simulated data and real world observations to support analysis and simulation of the impact of major sediment transport events such as hurricanes. We unite modeled results and field observations within a geodatabase designed as an application schema of the Arc Marine Data Model. Our real-world focus is on the Redbird Artificial Reef Site, roughly 18 nautical miles offshor- Delaware Bay, Delaware, where repeated surveys have identified active scour and bedform migration in 27 m water depth amongst the more than 900 deliberately sunken subway cars and vessels. Coincidently collected high-resolution multibeam bathymetry, backscatter, and side-scan sonar data from surface and autonomous underwater vehicle (AUV) systems along with complementary sub-bottom, grab sample, bottom imagery, and wave and current (via ADCP) datasets provide the basis for analysis. This site is particularly attractive due to overlap with the Delaware Bay Operational Forecast System (DBOFS), a model that provides historical and forecast oceanographic data that can be tested in hindcast against significant changes observed at the site during Superstorm Sandy and in predicting future changes through small-scale modeling around the individual reef objects

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone