Software Data Penduduk Berdasarkan

Open Street Map (OSM) dan GeoNames adalah sumber gasetir global yang diperoleh secara partisipatif. Makna partisipatif disini adalah pengguna dapat memperoleh, menambahkan dan melakukan perbaikan terhadap data secara sukarela. Pengguna tidak dikenakan biaya serta tidak dibatasi oleh tempat dan waktu. Data yang diperoleh dari partisipasi pengguna tersebut dikenal dengan istilah Volunteer Geospatial Information (VGI). Kemudahan dalam memperoleh data spasial tersebut memudahkan pengguna dalam mengaplikasikan berbagai analisis, salah satunya adalah analisis pola. Berdasarkan hal tersebut, makalah ini bertujuan untuk mengilustrasikan pola kepadatan penduduk di Daerah Istimewa Yogyakarta menggunakan data OSM dan GeoNames. Gasetir pada GeoNames dengan tipe ‘ Populated Places’ dan tipe data ‘ Building’ pada OSM diplot sebagai data dengan tipe titik.

Menghitung Luas Data Raster Berdasarkan Jumlah dan Resolusi. Geosiana Press. Untuk menghitung dapat dilakukan dengan software Spreadsheet seperti MS Excel. Grafik Pertumbuhan Penduduk Berdasarkan Status Perkawinan Grafik Pertumbuhan Penduduk Berdasarkan Dusun Software ini mendapatkan penghargaan di tingkat nasional.

Berdasarkan hasil plotting, setiap area memiliki jumlah titik gasetir yang berbeda sehingga menghasilkan perbedaan densitas titik. Selanjutnya, pola densitas berdasarkan data GeoNames dan OSM dibandingkan dengan data kepadatan penduduk yang diperoleh dari Badan Pusat Statistik. Acheson, E., De Sabbata, S., & Purves, R. A quantitative analysis of global gazetteers: Patterns of coverage for common feature types. Computers, Environment and Urban Systems, 64, 309–320. Acheson, E., Villette, J., Volpi, M., & Purves, R. Gazetteer matching for natural features in Switzerland.

In Proceedings of the 11th Workshop on Geographic Information Retrieval (p. Flatow, D., Naaman, M., Xie, K. E., Volkovich, Y., & Kanza, Y. On the accuracy of hyper-local geotagging of social media content. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (pp. H., Finch, K. C., Snook, K.

R., Tseng, P. L., Hernandez, A. Ebola virus disease and social media: A systematic review. American Journal of Infection Control, 44(12), 1660–1671. Goodchild, M. Citizens as sensors: The world of volunteered geography.

GeoJournal, 69(4), 211–221. Graham, M., & De Sabbata, S. Mapping information wealth and poverty: the geography of gazetteers. Environment and Planning A, 47(6), 1254–1264.

Gu, Y., Qian, Z., & Chen, F. From Twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies, 67, 321–342. Serial key backuptrans. How good is volunteered geographical information? A comparative study of OpenStreetMap and ordnance survey datasets. Environment and Planning B: Planning and Design, 37(4), 682–703.

Neis, P., Zielstra, D., & Zipf, A. The Street Network Evolution of Crowdsourced Maps: OpenStreetMap in Germany 2007–2011. Future Internet, 4(4), 1–21.

O., Maina, J., Thuranira, P. N., Macharia, P. M., Alegana, V. A., English, M., Snow, R.

Access to emergency hospital care provided by the public sector in sub-Saharan Africa in 2015: a geocoded inventory and spatial analysis. The Lancet Global Health, 6(3), e342–e350. Mining location from social media: A systematic review. Computers, Environment and Urban Systems, 71(March), 209–240. Su, S., Wan, C., Hu, Y., & Cai, Z. Characterizing geographical preferences of international tourists and the local influential factors in China using geo-tagged photos on social media. Applied Geography, 73, 26–37.

Wang, Z., & Zipf, A. Using Openstreetmap Data to Generate Building Models with Their Inner Structures for 3D Maps. Wendel, J., Simons, A., Nichersu, A., & Murshed, S. Rapid development of semantic 3D city models for urban energy analysis based on free and open data sources and software. In Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics (p. Zhou, X., Xu, C., & Kimmons, B.

I have added the switch /3GB to the boot.ini in Windows and things have improved The sessions go beyond the 700 even 800 concurrent sessions. I have the following situation: Oracle Database 9i R2 installed on Windows 2000 AS which is running on HP Server with the following specification: CPU: 4 (32 bit) CPUs Pentium IV 2.6 HT RAM: 4 GB Hard Drives: 8 Hard drives with RAID 10 This server is dedicated to the Oracle Database which the users are accessing it locally and from the Internet. Oracle 64 bit java. However, once the sessions connected to the server started to reach 600 or little more we start to have the error: ORA-12500: TNS:listener failed to start a dedicated server process I have read about the 4GB Tunning and how to allow Oracle Process in Windows to grow more than the 2 GB limit.