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"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."

"Menganalisis hubungan antara jumlah tindak kejahatan dan faktor-faktor yang mempengaruhinya menjadi topik penelitian yang menarik karena jumlah tindak kejahatan di Indonesia dalam sepuluh tahun terakhir cenderung meningkat. Untuk meningkatkan kualitas keamanan masyarakat maka pemerintah perlu memahami faktor-faktor apa saja yang dapat memicu tindakan kejahatan. Dibandingkan dengan metode analisis regresi klasik, metode Geographically Weighted Regression GWR lebih diunggulkan karena dapat menangani masalah ketidak stasioneran spasial yang biasanya terjadi pada data fenomena-fenomena sosial. Ketidakstasioneran spasial adalah situasi dimana hubungan antar peubah berbeda-beda secara signifikan disetiap lokasi observasi. Hal tersebut mengakibatkan hasil analisis regresi klasik menjadi tidak akurat di beberapa lokasi. GWR menangani masalah tersebut dengan membangun model regresi di setiap titik observasi sehingga memungkinkan parameter regresi menjadi berbeda di setiap lokasi observasi. Penelitian ini menggunakan jumlah tindak kejahatan y sebagai peubah terikat dan peubah bebasnya adalah jumlah penduduk buta huruf x1, jumlah pengangguran x2, jumlah penduduk miskin x3, kepadatan penduduk x4, dan jumlah korban NAPZA x5. Penelitian ini menggunakan data sekunder yang dihimpun oleh POLRI, BPS, dan Dinsos di Jawa Tengah pada tahun 2015. Terdapat dua fungsi pembobot spasial GWR yang akan dibandingkan yaitu Kernel Gaussian dan Kernel Bisquare. Hasil penelitian menunjukkan fungsi Kernel Gaussian lebih baik dibanding Kernel Bisquare berdasarkan skor AIC dan R2. Hasil analisis menggunakan GWR menghasilkan model untuk 35 kabupaten/kota di Jawa Tengah.

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Analyzing the relationship between number of crime cases and factors defined became an interesting research topic over the last ten years. The total number of crime in Indonesia didn rsquo t show a consistent decrease. In order to upgrade people safeness quality, the government need to know the factors influence people committing crime acts. Rather than using classical regression analysis, Geographically Weighted Regression GWR was preferable since it gave a better representative model by effectively resolve spatial non stationary problem which is generally exist in spatial data of social phenomenon. Spatial non stationary is a situation when the relationship between variables are significantly different in each location of observation point, so that classic regression analysis will result a misleading interpretation in some location. GWR handled the spatial non stationary problem by generating a single model in each observation point which allow different relationship to exist at different point in space. This study used number of crime cases y as the dependent variable and the factors which affect the number of crime cases as independent variables that consist of the number of illiterates x1 , the number of unemployed x2, the number of poor population x3, population density x4, the number of victims of drug x5. This study used secondary data collected by POLRI, BPS, and Social ministry of Indonesia in Central Java during 2015. Two spatial weighting functions were compared i.e. Kernel Gaussian and Kernel Bisquare and the study result indicated that Kernel Gaussian was batter according to score of R2 and AIC. GWR generated model for 35 city regency in Central Java. "

"This timely and fascinating book illustrates how applied geography can contribute in a multitude of ways to assist policy processes, evaluate public programs, enhance business decisions, and contribute to formulating solutions for community-level problems"

"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."

"Analisis pola spasial harga tanah diperlukan untuk identifikasi bentuk pola harga tanah secara spasial dan zona nilai tanah imajiner. Penelitian ini menggunakan metode indeks Global Moran?s I, regresi maximum likelihood spasial lag dan spasial error serta Moran?s Scatterplot untuk mengidentifikasi pola dan faktor spasial yang memengaruhi harga tanah pasar dan NJOP. Pola spasial harga tanah pasar dan NJOP teridentifikasi memiliki pola sistematik atau mengelompok. Model spasial lag lebih menjelaskan variasi harga tanah pasar sedangkan model spasial error lebih menjelaskan variasi harga tanah NJOP, koefisien lag ρ (rho) 19,62% dan λ (lambda) 20,31% belum cukup kuat dalam menunjukkan pengaruh spatial dependence.

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Spatial pattern analysis of land prices is needed to identify the form of the spatial pattern of land prices and imaginary land values zone. This study uses index Global Moran's I, regression maximum likelihood spatial lag and spatial error, Moran's Scatterplot to identify the spatial pattern and factors of land prices in the market and tax. The spatial pattern of land prices in the market and tax has identified systematic pattern or clustered, spatial lag models better explain the variation of land price in the market while the spatial error models better explain the variation of tax value, lag coefficient ρ(rho) 19.62% and λ(lambda) 20.31% has not been strong enough to show the effect of spatial dependence."

"PT PLN (Persero) UPT Cikupa memiliki 3 Unit Layanan Transmisi dan Gardu Induk (ULTG) yang terdiri dari ULTG Serang, ULTG Cikupa, dan ULTG Balaraja disuplai dari 21 Gardu Induk yang terdiri dari 69 trafo dengan kapasitas 5.943,4 MVA.[3] PT PLN (Persero) UPT Cikupa memiliki Gardu Induk (GI) yang menyuplai Konsumen Tegangan Tinggi (KTT). GIS Suvarna Sutera 150/22 kV mulai bertegangan pada tahun 2019 terdiri dari 2 trafo dengan kapasitas total 2x60 MVA (120 MVA). Total beban pada GIS Suvarna Sutera 390 A (23%) untuk Trafo I dan 142 A (8%) untuk Trafo II.[3] Oleh Karena itu keandalan pasokan listrik di wilayah tersebut perlu mendapat perhatian terutama saat terjadi gangguan. Pada tanggal 02 Agustus 2024 terjadi gangguan di GIS Suvarna Sutera dan jalur SUTT 150 kV Suvarna – Sindang Jaya #1 dan 03 Agustus 2024 terjadi dan gangguan di GIS Suvarna Sutera dan jalur SUTT 150 kV Suvarna-Sindang Jaya #2. Untuk menindaklanjuti gangguan pada sistem tersebut diperlukan langkah-langkah verifikasi, analisis, dan evaluasi penanganan gangguan tersebut. Selanjutnya dianalisis terhadap Kode Etik Insinyur, Asas Profesionalisme, dan Keselamatan, Kesehatan Kerja, dan Lingkungan (K3L). Rangkaian pekerjaan yang sudah dilakukan di PT PLN (Persero) UPT Cikupa sudah memenuhi standar penilaian yang menjadi acuan analisis di dalam laporan praktik keinsinyuran ini. Dari aspek K3L masih perlu menjadi perhatian dalam pelaksanaan pekerjaannya. Dalam kegiatan untuk proyek kedepannya, aspek yang menjadi koreksi pada laporan praktik keinsinyuran ini dapat menjadi bahan pertimbangan.

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PT PLN (Persero) UPT Cikupa has 3 Transmission Service Units and Main Substations (ULTG) consisting of ULTG Serang, ULTG Cikupa, and ULTG Balaraja that supplied from 22 Main Substations consisting of 69 transformers with a capacity of 5,943.4 MVA.[3] PT PLN (Persero) UPT Cikupa has a Main Substation (GI) which supplies High Voltage Consumers (KTT). GIS Suvarna Sutera 150/22 kV started operating in 2019 consisting of 2 transformers with total capacity of 2x60 MVA (120 MVA). The total load on GIS Suvarna Sutera is 390 A (23%) for Transformer I and 142 A (8%) for Transformer II.[3] Therefore, the reliability of electricity supply in the area needs attention, especially when disturbances occur. On 02 August 2024 there was a disruption on the GIS Suvarna Sutera and SUTT 150 kV Suvarna – Sindang Jaya #1 line and on 03 August 2024 there was a disruption on the GIS Suvarna Sutera and the SUTT 150 kV Suvarna – Sindang Jaya line #2. To follow up on disturbances in the system, verification, analysis and evaluation steps are needed to handle the disturbance. Next, the Engineer's Code of Ethics, Principles of Professionalism, and Safety, Occupational Health and Environment (K3L) were analyzed. The series of work that has been carried out at PT PLN (Persero) UPT Cikupa has met the assessment standards which are the reference for analysis in this engineering practice report. From the K3L aspect, attention still needs to be paid to the implementation of work. In activities for future projects, aspects that are corrected in this engineering practice report can be taken into consideration. "

"Kekeringan selalu terjadi setiap tahunnya di Indonesia salah satunya yaitu di Kabupaten Sumba Timur yang berada di Pulau Sumba, Nusa Tenggara Timur. Penelitian ini bertujuan untuk mengetahui bagaimana pola sebaran kekeringan meteorologis dengan menggunakan metode Standardized Precipitation Index atau SPI sebagai indikator kekeringan berdasarkan variabel curah hujan dan kekeringan pertanian di Kabupaten Sumba Timur dengan menggunakan metode Normalized Difference Drought Index atau NDDI sebagai indikator kekeringan dalam pengolahan data citra satelit serta menganalisis bagaimana hubungan kekeringan meteorologis terhadap kekeringan pertanian di Kabupaten Sumba Timur. Hasil analisis pola sebaran wilayah kekeringan meteorologis berdasarkan nilai SPI dan nilai NDDI menunjukan pola sebaran kekeringan yang menyebar secara acak. Adapun kekeringan meteorologis berdasarkan nilai SPI bergerak dari wilayah selatan yang di dominasi oleh wilayah ketinggian dengan tingkat miring menuju ke wilayah tengah Kabupaten Sumba Timur sedangkan kekeringan pertanian berdasarkan nilai NDDI bergerak dari wilayah utara yang di dominasi oleh wilayah ketinggian dengan tingkat landai menuju ke wilayah tengah Kabupaten Sumba Timur. Berdasarkan kedua metode penentuan kekeringan tersebut didapatkan klasifikasi tingkat kekeringan dimulai dari kekeringan normal hingga ekstrim. Adapun untuk kekeringan meteorologis menunjukan beberapa wilayah mengalami kekeringan parah namun pada kekeringan pertanian menunjukan beberapa wilayah mengalami kekeringan ringan. Wilayah kekeringan meteorologis dan kekeringan pertanian yang terjadi pada tahun 2019 dan 2020 memiliki beberapa perbedaan sehingga hal ini dapat menunjukan bahwa belum adanya korelasi antara wilayah yang mengalami kekeringan meteorologis juga merupakan wilayah yang mengalami kekeringan pertanian.

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Droughts always occur every year in Indonesia, one of which is in East Sumba Regency on Sumba Island, East Nusa Tenggara. This research aims to determine the distribution pattern of meteorological drought using the Standardized Precipitation Index or SPI method as a drought indicator based on rainfall and agricultural drought variables in East Sumba Regency using the Normalized Difference Drought Index or NDDI method as a drought indicator in processing satellite image data and analyze how meteorological drought is related to agricultural drought in East Sumba Regency. The results of the analysis of the distribution pattern of meteorological drought areas based on SPI values and NDDI values show a random distribution pattern of drought. Meanwhile, meteorological drought based on SPI values moves from the southern region which is dominated by high altitude areas with sloping levels towards the central region of East Sumba Regency, while agricultural drought based on NDDI values moves from the northern region which is dominated by high altitude areas with sloping levels towards the central region of the Regency. East Sumba. Based on the two methods of determining drought, a classification of drought levels is obtained starting from normal to extreme drought. As for the meteorological drought, it shows that several regions are experiencing severe drought, but the agricultural drought shows that several regions are experiencing mild drought. The areas of meteorological drought and agricultural drought that occurred in 2019 and 2020 have several differences, so this can show that there is no correlation between areas experiencing meteorological drought and also areas experiencing agricultural drought."