scholarly journals Perubahan Lahan Permukiman di Kecamatan Beo Kabupaten Talaud

2020 ◽  
Vol 1 (3) ◽  
pp. 19
Author(s):  
Reinaldi Wangke ◽  
Maxi Tendean ◽  
Helena Sri Sulastriningsih
Keyword(s):  
Land Use ◽  
Information System ◽  
Data Analysis ◽  
Geographic Information System ◽  
Data Processing ◽  
Research Method ◽  
Analytical Approach ◽  
Residential Area ◽  
Land Area ◽  
Residential Land

The increase in population and development activities results in an increase in land use for settlements in an area over a certain period of time. The research objective is to determine changes in residential land in Beo District, Talaud Regency from 2014 to 2019. The research method is descriptive qualitative with an analytical approach using a geographic information system. Based on data processing, the land area for the residential area of Beo District in 2014 was 139.7 Ha and in 2019 it was 149.1 Ha. The results of data analysis show that changes in residential land in Beo District, Talaud Regency from 2014 to 2019 have increased, amounting to 9.4 Ha. The rate of land change for settlements in the Beo sub-district within the 5 year period is still classified as very low.

scholarly journals PEMANFAATAN CITRA SATELIT LANDSAT 8 UNTUK MEMETAKAN SEBARAN TITIK PANASSEBAGAI INDIKASI PENINGKATAN SUHU PERMUKAAN TANAH DI KABUPATEN KUTAI TIMUR

2021 ◽  
Vol 46 (3) ◽  
pp. 383
Author(s):  
Donny Dhonanto ◽  
Nurul Puspita Palupi ◽  
Ghaisani Salsabila
Keyword(s):  
Land Use ◽  
Information System ◽  
Data Analysis ◽  
Geographic Information System ◽  
Surface Temperature ◽  
Land Surface ◽  
Maximum Temperature ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Area Distribution

 Transformation of land-use cause forest area decrease that will affect microclimate (weather tends heat), thus hotspot may possible to scattered in that area and raise the transformation of surface temperature. The objective of this research is to determine the indication of surface temperature in the East Kutai District. The advantage of this research is to give information about hotspot area distribution based on land use and relate between hotspots with surface temperature increase so it is supposed to be one of the consider to transform land use in East Kutai District. This research was held from April until May 2019 at the Laboratory of Carthography and Geographic Information System, Faculty of Agriculture, Mulawarman University. This research using calculation of Land Surface Temperature (LST) value to determine the transformation of surface temperature in East Kutai District by data analysis from Landsat-8 OLI/TIRS satellite. Hotspot area distribution adapted to map of land-use so we found the source of the hotspot. The result of the research shows there are about 6 hotspots in land-use of plantation in 2017 and the increase of the surface temperature is not static cause by depending of rainfall in East Kutai District. Increasing of surface temperature in East Kutai District in October 2013 become 22.35 oC (for minimum temperature), whereas in May 2017 become 37.24 oC (for maximum temperature). 

Calculation of Land Consolidation Potential Based on GIS

2014 ◽  
Vol 580-583 ◽  
pp. 2769-2773
Author(s):  
Yan Hua Liu ◽  
Wei Qing Chen
Keyword(s):  
Land Use ◽  
Information System ◽  
Geographic Information System ◽  
Geographic Information ◽  
Land Consolidation ◽  
Cultivated Land ◽  
Thematic Maps ◽  
Residential Land ◽  
Rural Residential Land ◽  
In Virtue Of

Through the analysis of land use actuality, this paper summarizes the characteristics of land use, in virtue of geographic information system (GIS) platform, the database of land consolidation is constructed, combining with different calculation models of cultivated land and rural residential land consolidation potential, the consolidation potentials of cultivated land and rural residential land are calculated, and the thematic maps about land consolidation potential of each village and town in study area are mapped out. The results show that the land consolidation areas are mainly concentrated in the middle of study area.

Analysis of the level of urban residential land based on land cover/land use

2020 ◽  
Author(s):  
kang fengguang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Residential Area ◽  
Evaluation Index System ◽  
Geographical Information ◽  
Housing Construction ◽  
Land Area ◽  
Residential Land ◽  
Information Monitoring ◽  
Per Capita

<p>The level of urban residential land has a great relationship with the composition of urban residential land and the urban residential area of human settlements. Urban residential land includes residential land, road land, ancillary facilities and public green land. Geographical information monitoring land cover/land use includes cultivated land, garden land, forest land, grassland, housing construction area, roads, structures, artificial digging land, desert and bare land, and water. The Land cover/land use data and resident population spatialization data based on maps of housing construction areas are this article’s data sources. This article chose urban residential land types and per capita residential land area as an evaluation index system, establish the relationship between residential land indicators and geographical information monitoring indicators, calculate the area of various residential land areas and per capita land area, and follow the "Urban Residential Area Planning and Design Standards", a statistical analysis of the land use of residents in the five-, ten-, and fifteen-minute living quarters. Select a test area from each of the megacities, megacities, large cities, medium cities, and small cities, and use the statistical results to determine the level of urban residential land Perform a comparative evaluation.</p>

scholarly journals Pemetaan Kesesuaian Lahan Untuk Budidaya Rumput Laut Di Perairan Pulau Poteran, Kabupaten Sumenep, Jawa Timur

2019 ◽  
Vol 8 (1) ◽  
pp. 36-40
Author(s):  
Hendra Irawan Ferdiansyah ◽  
Ibnu Pratikto ◽  
Suryono Suryono
Keyword(s):  
Information System ◽  
Quantitative Analysis ◽  
Geographic Information System ◽  
Human Resources ◽  
Research Method ◽  
Geographic Information ◽  
Seaweed Cultivation ◽  
Land Area ◽  
Gis Technology

Perairan Pulau Poteran merupakan salah satu wilayah yang berlokasi di Kabupaten Sumenep, Jawa Timur. Budidaya rumput laut di Pulau Poteran terdapat beberapa kendala dalam pengembangannya, yaitu dari sarana prasarana perikanan yang kurang memadai, keterbatasan pemahaman sumber daya manusia, modal, kelembagaan serta penentuan lokasi budidaya rumput laut. Penggunaan teknologi Sistem Informasi Geografis (SIG) di bidang kelautan dapat memberikan gagasan yang baru dalam kesesuaian lahan untuk budidaya rumput laut. Tujuan dilakukan penelitian ini adalah menganalisa lahan yang berpotensi untuk budidaya rumput laut di perairan Pulau Poteran dan mengetahui luas lahan yang efektif untuk pengembangan budidaya rumput laut di perairan Pulau Poteran. Metode penelitian yang digunakan adalah metode eksploratif dengan pendekatan analisa kuantitatif untuk mengetahui tingkatan dan luasan kesesuaian lahan budidaya rumput laut di perairan Pulau Poteran. Hasil penelitian menunjukkan bahwa luas untuk kategori sangat sesuai (S1) sebesar 7.335,59 ha, sedangkan untuk kategori sesuai (S2) memiliki luas sebesar 17.990,11 ha dan kategori tidak sesuai (S3) memiliki luas sebesar 24.665,28 ha. Luas lahan yang efektif untuk pengembangan budidaya rumput laut di perairan Pulau Poteran sebesar 4.401,35 ha (60% dari luas sangat sesuai) dengan jumlah rakit yang dioperasikan sebesar 785.955 unit dengan ukuran rakit 7 x 8 m. The territorial waters of Poteran Island are one of the areas located in Sumenep Regency, East Java. There are some problems in seaweed cultivation of Poteran Island, which are facilities, fishery infrastructure, limited human resources, financial, and institutional and the determination of the location of seaweed cultivation. The use of Geographic Information System (GIS) technology in the marine field can give new idea in land suitability for seaweed cultivation. The purpose of this research is to analyze the potential for seaweed cultivation in the waters of Poteran Island and know the effective land area for the development of seaweed cultivation in the waters of Poteran Island. The research method used is an exploratory method with a quantitative analysis approach to determine the level and extent of the suitability of seaweed cultivation in Poteran island waters. The results showed that the area for the very suitable category (S1) amounted to 7,335.59 hectare, while the corresponding category (S2) has an area of 17,990.11 hectare and the unsuitable category (S3) has an area of 24,665.28 hectare. Effective land area for the development of seaweed cultivation in the territorial waters of Poteran Island amounted to 4,401.35 hectare (60% of the area is very suitable) with the number of rafts operated by 785,955 units with a raft size of 7 x 8 m.

scholarly journals PENENTUAN LOKASI POTENSIAL UNTUK PENGEMBANGAN TERMINAL TIPE B SEBAGAI ALTERNATIF PENGGANTI TERMINAL CIBINONG DI WILAYAH ZONA CIBINONG RAYA KABUPATEN BOGOR

2016 ◽  
Vol 15 (1) ◽  
pp. 29-33
Author(s):  
Dita Anggraeni ◽  
Sucahyanto Sucahyanto ◽  
Ode Sofyan Hardi
Keyword(s):  
Information System ◽  
Data Analysis ◽  
Data Processing ◽  
Geographical Information System ◽  
Research Method ◽  
Geographical Information ◽  
Potential Zone ◽  
System Software ◽  
Type B ◽  
Descriptive Method

ABSTRACT This research aims to map the location of a potential designed as the location to develop B type terminal as an alternative of Cibinong terminal in Cibinong Zone Raya Bogor Regency. This research was conducted in six districts in Cibinong Raya zone region. The districts are Cibinong, Citeureup, Sukaraja, Babakan Madang, Bojong Gede, and Tajur Halang. The researcher used descriptive method as the research method. The technique of data analysis refers to the suitability matrix that has been made under the term parameters of type-B terminal development. The parameters were given the scoring and then processed further by the geographical information system software. After that, the processed results were classified into some classes based on the suitability level of each parameter. The three classifications are the result of the total scores divided into three classifications. For Potential 1 (P1) with a range 14-20, Potential 2 (P2) with a range 713 and Potential 3 (P3) with a range 0-6. The result showed that based on data processing geographical information system in these included in classifying according 1 ( potential 1 ) spread right and left sides jakarta-bogor roads , major oking jayaatmaja cibinong , sukahati-karadenan and the brave believe. Classifying according to 2 ( potential 2 ) spread in roads major oking jayaatmaja citeureup, the kranggan – gunung putri and highways jagorawi. To determine the locations is 3 potential zone with the highest states to be terminals,this zone is Cibinong Zone , Karadenan Zone and Sukahati Zone. Of the three the zone,Cibinong Zone the most suitable to be locations terminal development. Keywords: Potential, Location, Terminal

scholarly journals Automated Classification of Agricultural Lands Using GIS and NES

2018 ◽  
Vol 7 (4.38) ◽  
pp. 1146
Author(s):  
V. K. Kalichkin ◽  
A. I. Pavlova ◽  
A. F. Petrov ◽  
V. A. Smolyakov
Keyword(s):  
Land Use ◽  
Information System ◽  
Geographic Information System ◽  
Interpolation Method ◽  
Ground Surface ◽  
Geographic Information ◽  
Automated Classification ◽  
Stream Power ◽  
Stream Power Index

The article proposes the methodology for the automated classification of uplands using Geographic Information System (GIS) and Neural Expert System (NES). Quantitative indicators of topography are used as the basis of the proposed classification. A database consisting of topographic, soil, and land use maps was created using ArcGIS 10 geographic information system. A topologically correct digital elevation model (DEM) was created by the ANUDEM interpolation method. The DEM contains the following maps: hypsometric, steepness and slopes exposure, plan, profile, common curvature of the ground surface, and cumulative runoff maps. The boundaries of elementary surfaces (ES), which are homogeneous morphological formations, are established. Parameters characterizing the Stream Power Index (SPI) are taken into account. The essence of the proposed classification consists in attributing of ES to a certain group of lands based on aggregate of features. To do this, partial scales were created, containing indicators of topography, soil cover, land drainage conditions, as well as the degree of erosion development. The authors formed knowledge base for traning the NES using GIS database and partial scales of estimates. Teaching of neural network was carried out. The classification and topology of land was carried out by means of the NES. The uplands are distributed in flat and slightly convex areas. They are characterized by the following indicators: the curvature of the ground surface: plan curvature (0 – 0.03), profile curvature (0 – 0.15), common curvature (0 – 0.22); slope angles (less than 1.5о); horizontal dissection in elevation (less than 0.5 km/km2), vertical dissection (less than 5 m); and SPI (from -13.80 to -6.47). Electronic map of uplands of LLC «Salair» land-use area was created in the ArcGIS 10 environment.  

scholarly journals Epidemiological study and spatial modeling of peste des petits ruminants (PPR) in central area of Iran

2017 ◽  
pp. 5899-5909 ◽  
Author(s):  
Azam Mokhtari ◽  
Zahra Azizi ◽  
Soheila Rabiaee Fradonbeh
Keyword(s):  
Information System ◽  
Spatial Distribution ◽  
Data Analysis ◽  
Geographic Information System ◽  
Disease Control ◽  
Spatial Modeling ◽  
Small Ruminant ◽  
Statistical Tests ◽  
Central Area ◽  
Peste Des Petits Ruminants

Objective. Estimate the prevalence and spatial modeling of PPR in the small ruminant population of Chaharmahal and Bakhtiari, Iran, during 2009–2014. Materials and methods. Data were collected from veterinary organization and Offices in Chaharmahal and Bakhtiari province and data analysis was carried out using and IBM SPSS version 22 and Office 2010. For spatial modeling geographic information system (QGIS and PCI-Geomatic) was used. Results. This study showed that the overall prevalence of PPR during the years 2009 to 2014 was 1.37%. Koohrang, Ardal, Lordegan, Ben, Borougen, Shahrekord, Farsan and Kiar cities had the highest prevalence of PPR, respectively. The highest PPR infection rate was observed in the March and goat more affected rather than other ruminants. Conclusions. Our findings provide evidence of a rather common prevalence of PPR and its spatial distribution in Chaharmahal and Bakhtiari province. Using statistical tests for data analysis of PPR and its spatial modeling researchers can predict the incidence of disease in the future and could select appropriate measures of disease control.

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