Analysis of Visualization Technology of 3D Spatial Geographic Information System

Traditional urban planning is generally expressed in a two-dimensional geographic information system, but its performance is limited to the plane direction. It cannot give people more natural feelings and visionary experiences. The rapid development of three-dimensional geographic information systems brings people geographic information. The three-dimensional intuitive experience, but the traditional three-dimensional geographic information system has the disadvantages that the spatial properties are incompatible, the image rendering speed is slow, and the visualization effect is poor. In this paper, the traditional domain-oriented processing method is improved in spatial data processing and modeling. An optimized object-oriented optimization algorithm is proposed. The three-dimensional geographic information is optimized based on a dynamic multiresolution model and multilevel detail processing technology. The rendering of the system enhances the visualization. Based on the optimization algorithm of data processing and visualization technology proposed in this paper, the spatial data processing platform GISdata of 3D GIS is designed in this paper. At the same time, the 3D GIS is visualized based on OpenGL visualization software. It is shown that the optimization algorithm proposed in this paper has excellent preexperimental effects.

MODEL INFORMASI KEPUTUSAN BERBASIS TOPSIS APLIKASI ANDROID

Information Technology in spatial data processing has developed to a point where these results are in line with the application challenges required by natural resource management. In addition, the internet, geomatics, and telecommunications are rapidly changing the way natural resources are managed and protected. These provide more accurate and up-to-date information and are quickly available to users. Regional potential is a product that exists in an area that can be developed and is able to provide benefits to the local community and can be used as a supporter of the national economy. This understanding gives the connotation that optimal management planning is needed in order to achieve the intended expectations, so this decision support system is presented in a simple form for easy access on Android smartphone devices. Applications are made with Eclipse as an editor as well as compile and builder and SQLite for the application database.

GemGIS – GemPy Geographic: Open-Source Spatial Data Processing for Geological Modeling

<p>Geological modeling methods are widely used to represent subsurface structures for a multitude of applications – from scientific investigations, over natural resource and reservoir studies, to large-scale analyses and geological representations by geological surveys. In recent years, we have seen an increase in the availability of geological modeling methods. However, many of these methods are difficult to use due to preliminary data processing steps, which can be specifically difficult for geoscientific data in geographic coordinate systems.</p><p>We attempt to simplify the access to open-source spatial data processing for geological modeling with the development of GemGIS, a Python-based open-source library. GemGIS wraps and extends the functionality of packages known to the geo-community such as GeoPandas, Rasterio, OWSLib, Shapely, PyVista, Pandas, NumPy and the geomodelling package GemPy. The aim of GemGIS, as indicated by the name, is to become a bridge between conventional geoinformation systems (GIS) such as ArcGIS and QGIS, and geomodelling tools such as GemPy, allowing simpler and more automated workflows from one environment to the other.</p><p>Data within the different disciplines of geosciences are often available in a variety of data formats that need to be converted or transformed for visualization in 2D and 3D and subsequent geomodelling methods. This is where GemGIS comes into play. GemGIS is capable of working with vector data created in GIS systems through GeoPandas, Pandas and Shapely, with raster data through rasterio and NumPy, with data obtained from web services such as maps or digital elevation models through OWSLib and with meshes through PyVista. Support for geophysical data and additional geo-formats are constantly added.</p><p>The GemGIS package already contains several tutorials explaining how the different modules can be used to process spatial data. It was decided against creating new data classes in case users are already familiar with concepts such as (Geo-)DataFrames in (Geo-)Pandas or PolyData/Grids in PyVista.</p><p>The GemGIS package is hosted at https://github.com/cgre-aachen/gemgis, the documentation is available at https://gemgis.readthedocs.io/en/latest/index.html. GemGIS is also available on PyPi. You can install GemGIS in your Python environment using ‘pip install gemgis’.</p><p>We welcome contributions to the project through pull requests and are open to suggestions and comments, also over Github issues, especially about possible links to other existing software developments and approaches to integrate geoscientific data processing and geomodelling.</p>

USE OF DISTRIBUTED DBMS FOR SPATIAL DATA PROCESSING

Currently, data volumes are growing exponentially. Geospatial data is one of the main elements of the concept of Big data. There is a very large number of tools for analyzing Big data, but not all of them take into account the features and have the ability to process geospatial data. The article discusses three popular open analytical tools Hadoop Spatial, GeoSpark, GeoFlink for working with geospatial data of very large volumes. Their architectures, advantages and disadvantages, depending on the execution time and the amount of data used are considered. Processing evaluations were also performed in terms of both streaming and packet data. The experiments were carried out on raster and vector data sets, which are satellite imagery in the visible range, NDVI and NDWI indices, climate indicators (snow cover, precipitation intensity, surface temperature), data from the Open Street Map in the Novosibirsk and Irkutsk Regions.

Pemetaan Lahan Potensial Perumahan Di Kota Surakarta Berdasarkan Pendekatan Analisis

The growth of trade centers, inns, office buildings, meetinghouses and other supporting facilities has a direct impact on the increase in population as well as the need for land for housing amid the limited land. This resulted the disparity problem between the built housing and the amount of housing needed by the community. This study aims to examine the potential of the land for the provision of housing based on the results of spatial data processing. This study used quantitative analysis method through a geographic information system based spatial analysis approach. The existence of the built-up area and the suitability of the Surakarta City space utilization plan was taken into consideration. In the process, it was found that the tendency for locations was in the northern part of Surakarta City where the results of the land value analysis showed that the area was suitable for housing, but the security analysis showed inappropriate results and even restricted. The findings in the housing land suitability analysis showed that the results of the accessibility analysis and the results of the analysis of the affordability of water and sanitation systems had a significant effect. The analysis found that out of 51 urban villages there were 43 urban villages that had potential land for housing with a level of conformity from appropriate to inappropriate level. Intisari: Pertumbuhan pusat perdagangan, penginapan, gedung perkantoran, gedung pertemuan dan fasilitas pendukung lainnya berdampak langsung pada penambahan populasi dan kebutuhan lahan untuk perumahan di tengah lahan yang terbatas. Hal ini mengakibatkan masalah kesenjangan antara perumahan yang dibangun dengan jumlah perumahan yang dibutuhkan oleh masyarakat. Penelitian ini bertujuan untuk menguji potensi lahan untuk penyediaan perumahan berdasarkan hasil pengolahan data spasial. Metode analisis kuantitatif digunakan melalui pendekatan analisis spasial berbasis Sistem Informasi Geografis (SIG). Keberadaan area terbangun dan kesesuaian rencana pemanfaatan ruang Kota Surakarta menjadi pertimbangan. Dalam prosesnya, ditemukan bahwa kecenderungan lokasi berada di bagian utara Kota Surakarta di mana hasil analisis nilai tanah menunjukkan bahwa daerah tersebut cocok untuk perumahan, tetapi analisis keamanan menunjukkan hasil yang tidak sesuai dan bahkan dibatasi. Temuan dalam analisis kesesuaian lahan perumahan menunjukkan bahwa hasil analisis aksesibilitas dan hasil analisis keterjangkauan sistem air dan sanitasi memiliki pengaruh yang signifikan. Kajian ini menemukan bahwa dari 51 kelurahan terdapat 43 kelurahan yang memiliki lahan potensial untuk perumahan dengan tingkat kesesuaian yang sesuai sampai dengan tidak sesuai.