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Iqbal Eko N.

@Iqbalen14

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GIS Specialist, Geographer, Researcher

$15 USD / 時間

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Indonesia (8:03 午前)

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登録日: 11月 8, 2024

$15 USD / 時間

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Bachelor of Geographic Information Science, Currently pursuing Master of Urban and Regional Planning in Bandung Institute of Technology. Highlight skills - GIS analyst : ArcMap, QGIS, ArcGIS Pro, Phyton - Academic Research : Writting, Literature study, Data analyst - Data science : Data visualization Researchgate : Iqbal Eko Noviandi Github : iqbaleko

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全て見る Cartography & Maps Freelancers 、 Indonesia

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This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

Perform GWR analysis that could use to predict dependent variable throught local effect of regression.
1st image : GWR coefficient map
2nd image : T-value across observation
3rd image : Map layout version of predicted dependent variable

Geographically Weighted Regression

Perform GWR analysis that could use to predict dependent variable throught local effect of regression.
1st image : GWR coefficient map
2nd image : T-value across observation
3rd image : Map layout version of predicted dependent variable

Geographically Weighted Regression

Perform GWR analysis that could use to predict dependent variable throught local effect of regression.
1st image : GWR coefficient map
2nd image : T-value across observation
3rd image : Map layout version of predicted dependent variable

Geographically Weighted Regression

this analysis find shortest distance to nearest feature (healthcare)

Accessibility map : measure distance to shortest healthcare

this analysis find shortest distance to nearest feature (healthcare)

Accessibility map : measure distance to shortest healthcare

this analysis find shortest distance to nearest feature (healthcare)

Accessibility map : measure distance to shortest healthcare

this analysis find shortest distance to nearest feature (healthcare)

Accessibility map : measure distance to shortest healthcare

This code was created to to construct spatial interaction network using Gravity Model approach. The specific gravity approach were used is the unconstrained gravity model or as known as clasical gravity model.

The main file can be found in this repository "GIScience_Gravity_model_spatial_interaction_network.ipynb" for a code

Input :

Polygon Shapefile of administrative data which include attribute/column that define weight (Population attribute is the example)

Output :

Point feature of administrative unit in shapefile point
Spatial interaction network in shapefile polyline
Mass-decay curve
Distance-decay curve
Attribute influence curve
Full statistic details of regression in table (as a print)

https://github.com/iqbaleko/GIScience_Gravity-model-spatial-interaction-network

GIScience: Spatial Interaction Network of Gravity model

This code was created to visualize terrain in a cloud dataset from Earth Engine (SRTM), which includes the globally available terrain dataset. To visualize a specific area of interest, change the coordinates in the indicated code field as instructed in notebook.

The main file can be found in this repository "Visualizing_3D_terrain_with_cloud_data.ipynb" Go to the Google colab file to preview the 3D interactive terrain visual

Input :

Coordinate of specific region (two point of coordinate to define region)

Output :

3D interactive terrain visualization
3D interactive terrain visualization with contour line

https://github.com/iqbaleko/GIScience_Visualizing-3D-terrain-with-cloud-data

GIScience: Visualizing 3D Terrain with Cloud Data

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

This modeling utilizes social network analysis (SNA) techniques in visualizing the road network connections between sub-districts in Bandung City, then conducting centrality and modularity analysis to understand the structure in more detail. To further understand the intensity of social interaction, this modeling involves a classical gravity model approach between sub-district areas. The results of social interaction modeling show several network highlights/flows that are dense with the intensity of social movement. Several of these highlights are validated/verified qualitatively showing that the modeling results are able to capture the phenomenon of high intensity of movement on several road sections.

Modelling social mobility in social network analysis

Perform GWR analysis that could use to predict dependent variable throught local effect of regression.
1st image : GWR coefficient map
2nd image : T-value across observation
3rd image : Map layout version of predicted dependent variable

Geographically Weighted Regression

Perform GWR analysis that could use to predict dependent variable throught local effect of regression.
1st image : GWR coefficient map
2nd image : T-value across observation
3rd image : Map layout version of predicted dependent variable

Geographically Weighted Regression

Perform GWR analysis that could use to predict dependent variable throught local effect of regression.
1st image : GWR coefficient map
2nd image : T-value across observation
3rd image : Map layout version of predicted dependent variable

Geographically Weighted Regression

this analysis find shortest distance to nearest feature (healthcare)

Accessibility map : measure distance to shortest healthcare

this analysis find shortest distance to nearest feature (healthcare)

Accessibility map : measure distance to shortest healthcare

this analysis find shortest distance to nearest feature (healthcare)

Accessibility map : measure distance to shortest healthcare

this analysis find shortest distance to nearest feature (healthcare)

Accessibility map : measure distance to shortest healthcare

This code was created to to construct spatial interaction network using Gravity Model approach. The specific gravity approach were used is the unconstrained gravity model or as known as clasical gravity model.

The main file can be found in this repository "GIScience_Gravity_model_spatial_interaction_network.ipynb" for a code

Input :

Polygon Shapefile of administrative data which include attribute/column that define weight (Population attribute is the example)

Output :

Point feature of administrative unit in shapefile point
Spatial interaction network in shapefile polyline
Mass-decay curve
Distance-decay curve
Attribute influence curve
Full statistic details of regression in table (as a print)

https://github.com/iqbaleko/GIScience_Gravity-model-spatial-interaction-network

GIScience: Spatial Interaction Network of Gravity model

This code was created to visualize terrain in a cloud dataset from Earth Engine (SRTM), which includes the globally available terrain dataset. To visualize a specific area of interest, change the coordinates in the indicated code field as instructed in notebook.

The main file can be found in this repository "Visualizing_3D_terrain_with_cloud_data.ipynb" Go to the Google colab file to preview the 3D interactive terrain visual

Input :

Coordinate of specific region (two point of coordinate to define region)

Output :

3D interactive terrain visualization
3D interactive terrain visualization with contour line

https://github.com/iqbaleko/GIScience_Visualizing-3D-terrain-with-cloud-data

GIScience: Visualizing 3D Terrain with Cloud Data

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Universitas Pendidikan Indonesia

2018 - 2022

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Bachelor of Geographic Information Science

Indonesia

2018 - 2022

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