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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."