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"This book provides a cross-section of cutting-edge research areas being pursued by researchers in spatial data handling and geographic information science (GIS). It presents selected papers on the advancement of spatial data handling and GIS in digital cartography, geospatial data integration, geospatial database and data infrastructures, geospatial data modeling, GIS for sustainable development, the interoperability of heterogeneous spatial data systems, location-based services, spatial knowledge discovery and data mining, spatial decision support systems, spatial data structures and algorithms, spatial statistics, spatial data quality and uncertainty, the visualization of spatial data, and web and wireless applications in GIS."

"Pemilihan lokasi dan desain sistem energi surya merupakan tahapan yang sangat penting dalam memastikan kelayakan, keberlanjutan, dan operasional yang optimal suatu Pembangkit Listrik Tenaga Surya (PLTS). Banyak hal yang perlu dipertimbangkan dalam evaluasinya, seperti posisi (spasial) dan kondisi suatu wilayah (geospasial). Oleh karena itu, penelitian ini bertujuan untuk mengembangkan skema atau rancangan pengambilan keputusan untuk pemilihan lokasi PLTS yang sesuai, khususnya di desa tertinggal, terdepan, dan terluar (3T) Indonesia dengan metode yang digunakan adalah kombinasi Geographical Information System (GIS) dan pendekatan pengambilan keputusan multi-kriteria (MCDM), serta optimisasi desain sistem PLTS dengan menggunakan perangkat lunak HOMER untuk daerah paling sesuai berdasarkan analisis geospasial GIS-Analytic Hierarchy Process (AHP). Hasil yang didapatkan adalah kriteria yang paling berpengaruh adalah kriteria iklim (GHI 32%, temperatur 21%, dan kelembaban 5%). Daerah yang paling sesuai untuk PLTS adalah Desa Kalibagor, Desa Tulamben, Desa Tolada, Desa Kotaraja, Desa Talaga Tomoagu, Desa Maronge, dan Desa Nelelamawangi, sedangkan desa 3T yang sesuai untuk PLTS adalah Desa Kafelulang, Desa Pendulangan, dan Desa Sampuro. Kapasitas PLTS di daerah paling sesuai berada pada rentang 45,7 s/d 2.973 kW dengan produksi listrik yang dihasilkan sebanyak 69.718 s/d 4.457.825 kWh/tahun, sedangkan di desa 3T 79,4 s/d 146 kW dengan produksi listrik yang dihasilkan sebanyak 97.685 s/d 247.234 kWh/tahun. Rentang nilai Levelized Cost of Electricity (LCOE) untuk daerah paling sesuai adalah Rp4.119/kWh s/d Rp4.639/kWh, sedangkan untuk desa 3T adalah Rp4.201/kWh s/d Rp4.808/kWh.

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Site selection and solar energy system design are very important stages in ensuring the feasibility, sustainability, and optimal operation of a Solar PV Power Plant. Many things need to be considered in the evaluation, such as the position (spatial) and condition of an area (geospatial). Therefore, this study aims to develop a decision-making scheme or design for the selection of suitable solar PV locations, especially in disadvantaged, frontier, and outermost (3T) villages in Indonesia. The method used is a combination of Geographical Information System (GIS) and a multi-criteria-decision-making approach (MCDM), as well as optimization solar energy system design using HOMER software for the highly suitable area based on GIS-Analytic Hierarchy Process (AHP). The results obtained are the most influential criteria are climate (GHI 32%, temperature 21%, and humidity 5%). The highly suitable areas for solar PV power plant are Kalibagor Village, Tulamben Village, Tolada Village, Kotaraja Village, Talaga Tomoagu Village, Maronge Village, and Nelelamawangi Village, while the 3T villages that are suitable for PLTS are Kafelulang Village, Pendulangan Village, and Sampuro Village. Solar PV capacity in the highly suitable area varies between 45.7 and 2,973 kW with electricity production of 69,718 to 4,457,825 kWh/year, while in 3T villages varies 79.4 to 146 kW with electricity production of 97,685 to 247,234 kWh/year. Levelized Cost of Electricity (LCOE) in the highly suitable area varies between 4,119 IDR/kWh to 4,639 IDR/kWh, while in 3T villages varies 4,201 IDR/kWh to 4,808 IDR/kWh."

"Quantifying the social and economic value that geospatial information contributes to modern society is a complex task. To construct reliable and consistent valuation measures requires an understanding of the sequence of processes that starts with data acquisition, and leads to decision-makers' choices that impact society. GEOValue explores each step in this complex value chain from the viewpoint of domain experts spanning disciplines that range from the technical side of data acquisition and management to the social sciences that provide the framework to assess the benefit to society. The book is intended to provide foundational understanding of the techniques and complexities of each step in the process. As such it is intended to be assessable to a reader without prior training in data acquisition systems, information systems, or valuation methods.In addition, a number of case studies are provided that demonstrate the use of geospatial information as a critical input for evaluation of policy pertaining to a wide range of application areas, such as agricultural and environmental policy, natural catastrophes, e-government and transportation systems."

"Microsoft SQL server implements extensive support for location-based data. Pro Spatial with SQL server 2012 introduces SQL server’s spatial feature set, and covers everything you'll need to know to store, manipulate, and analyze information about the physical location of objects in space. You’ll learn about the geography and geometry datatypes, and how to apply them in practical situations involving the spatial relationships of people, places, and things on earth.Author Alastair Aitchison first introduces you to SQL server’s spatial feature set and the fundamental concepts involved in working with spatial data, including spatial references and co-ordinate systems. You’ll learn to query, analyze, and interpret spatial data using tools such as Bing Maps and SQL server reporting services. Throughout, you'll find helpful code examples that you can adopt and extend as a basis for your own projects. Fitur : explains spatial concepts from the ground up—no prior knowledge is necessary, provides comprehensive guidance for every stage of working with spatial data, from importing through cleansing and storing, to querying, and finally for retrieval and display of spatial data in an application layer, and brilliantly illustrated with code examples that run in SQL server 2012, that you can adapt and use as the basis for your own projects."

"Kawasan pedesaan merupakan wilayah yang kerap kali terlupakan dalam pembahasan pembangunan. Saat ini, pemanfaatan teknologi geospasial telah digunakan untuk berbagai sektor. Salah satu metode yang paling umum adalah pengamatan melalui penginderaan jauh. Penginderaan jauh kerap kali digunakan dalam pengamatan pembangunan suatu wilayah. Penelitian ini memanfaatkan penginderaan jauh untuk mengamati pembangunan di Kabupaten Sumedang. Tujuan dari penelitian ini adalah untuk menganalisis pola spasial dari suhu permukaan di Kabupaten Sumedang pada tahun 2007, 2013, 2017, dan 2022 serta korelasi antara suhu permukaan darat tersebut dengan tutupan lahan berdasarkan indeks spektral (NDVI, NDWI, dan EBBI). Data yang digunakan dalam penelitian ini adalah citra dari Landsat-5 TM, Landsat-8 OLI/TIRS, dan Landsat-9 OLI-2/TIRS-2. Citra didapatkan dan diolah menggunakan Google Earth Engine dan divalidasi dengan menggunakan data lapangan. Hasil dari penelitian ini adalah rata-rata suhu di daerah penelitian secara konstan mengalami peningkatan sejak tahun 2007 hingga 2022. Peningkatan terbesar terjadi pada tahun 2017 ke 2022, suhu meningkat hingga 4,41℃. terdapat tiga jenis tutupan lahan yang mengalami kenaikan LST yang signifikan yaitu lahan terbuka, lahan pertanian, dan lahan terbangun. Berdasarkan analisis yang dilakukan pola spasial LST di daerah penelitian terdapat 3 jenis sebaran yaitu terpusat, menyebar dan memanjang. LST menunjukan korelasi yang sedang dengan EBBI dan NDWI, sedangkan terdapat korelasi negatif dengan NDVI. NDVI memiliki korelasi negatif yang kuat dengan EBBI dan NDWI. Selanjutnya, NDWI dan EBBI menunjukan angka korelasi sebesar 0,816. Hal ini memiliki arti adanya korelasi positif yang kuat antara NDWI dan EBBI.

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Rural areas are areas that are often forgotten in development discussions. Currently, the utilization of geospatial technology has been used for various sectors. One of the most common methods is observation through remote sensing. Remote sensing is often used in observing the development of an area. This research utilizes remote sensing to observe development in Sumedang Regency. The purpose of this study was to analyze the spatial pattern of surface temperature in Sumedang Regency in 2007, 2013, 2017, and 2022, as well as the correlation between land surface temperature and land cover based on spectral indices (NDVI, NDWI, and EBBI). The data used in this study are images from Landsat-5 TM, Landsat-8 OLI/TIRS, and Landsat-9 OLI-2/TIRS-2. The image is processed using the Google Earth Engine and validated using field data. The results of this study are that the average temperature in the study area has constantly increased from 2007 to 2022. The most significant increase occurred from 2017 to 2022. The temperature increased to 4.41℃. Three types of land cover experienced a significant increase in LST, namely bare land, agricultural, and built-up. Based on the analysis carried out by the LST spatial patterns in the study area, there are 3 types of distribution, namely centralized, disperse and linear. LST shows a moderate correlation with EBBI and NDWI, while there is a negative correlation with NDVI. NDVI has a strong negative correlation with EBBI and NDWI. Furthermore, NDWI and EBBI show a correlation number of 0.816. This means that there is a strong positive correlation between NDWI and EBBI.

 

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"The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. "

"Aimed at readers with a knowledge of Geographic Information Systems (GIS) but no formal training in computer science, this book provides a clear and accessible introduction to how GIS store and process spatial data. This updated edition includes two new chapters on databases and future developments, substantial additional material on raster imagery, and revisions throughout that incorporate up-to-date applications such as GPS on mobile devices and Internet-based services. The chapter on future technologies includes discussions of 3D GIS, handling time in GIS, spatial SQL, and handling imprecise geographies"-"