Pilih Layer yang ingin ditampilkan melalui katalog yang tersedia (tab katalog). Gunakan GetFeatureInfo untuk mendapatkan informasi mengenai feature pada layer
Daftar layer spasial yang tersedia pada server. Double klik pada datagrid, atau pilih layer yang ingin ditampilkan, kemudian tekan button Add Layer untuk menambahkan layer pada peta.
Multi Radial Impact Simulator is a web map tool designed to help user to create multiple dynamic distance ring buffer around a point feature. It can also be used to build simple analytical report using custom data with predefined set of columns. Application will try to extract the containment: what objects inside each ring, group the data, display it, then summarize the result based on preferences.
How does it work?
You can use this tool to just generate multi ring buffer for a defined point location. Go to the second tab on the left, play with all available settings and hit export. To get statistical summary from your data, follow these 3 (three) simple steps:
1.
Prepare the data Your data should have quantitative fields or columns with number data type. In the scope of humanitarian aids/mitigation plan for example, you probably have fields such as districs, deaths, injured, missing, affected, destroyed, evacuated, human or economic impact, etc in which in most cases all of them are number. If so, you are ready to play with aggregation. For shp files, zip the data (*.shp, *.dbf, *.prj into a single zip file). Or alternatively, just use geojson format. Then load your data from application input panel.
2.
Configure column Once has been loaded, you'll be presented with configuration column with all fields found in the data. Select field/s and its corresponding aggregation method you want to summarize. SUM is the default method. It is also recomended that you select COUNT for qualitative column or descriptive field/s such as name or string identifier.
Summary Field
:
Field/s to be summarized
AGGR
:
Aggregation/Summary Method
Group
:
Assign value to ON if selected field has hierarchical or group value. In administrative division for example, turning the group value to ON and using COUNT as aggregation method in higher hierarchy will result in total count of all unique values from selected field found in the data.
Label
:
Field that will appear in summary detail panel grid. At least one label field must be selected for the grid to be displayed properly. Depends on your data, but fields like ID and/or NAME will be helpful for identifying details.
TIP ( Tooltip )
:
Used internally by application to enable tooltip on mouse over
Proceed when you confident with selections and hit OK.
Note that this step is not mandatory. You can skip configuration step by pressing OK. Again, you must select at least one LABEL field for detail panel datagrid to display named items.
Image : Configuration Example.
3.
Interact Try to drag, move, and change each ring distance by dragging or by typing input ring parameter from configuration panel and notice that summary panel info updates as you interact with the circle. You can move the circle using right click context menu on the map to quickly jump to a new location. You can also double-click summary detail grid to zoom to selected feature.
The sample data provided should give you better idea of what this description is all about.
Who is this for?
It can be used by individuals, governments or decision makers, NGOs, researchers, communities, or anyone who requires multi-clustered circle boundary simulation for modelling.
Limitation
Can only be applied to concentric circle pattern analysis.
A work in progress
The basic frame of this simulation was first written in actionscript during Mt.Merapi (Central Java) eruption in November 2010. It was a proof-of-concept design with primary goal to visualize configurable distances using polygonal ring zones. The codes used in this demo applies the same underlying principle. Only that it had been refactored and encapsulated to handle more tasks.
There are more to be done to improve existing approaches. One particular notion in the context of Indonesia regional level, is that the built-in data structure still need to be configured in a way so that current arrangement can cope with all administrative divisions. Deliberately prepared and properly abstracted model will advantage future development when model is exposed to relational join in spatial RDBMS. For most part however, it's all about defining scope and purpose. This showcase is intended to balance that abstraction with minimum effort and to see how the component works inside multi-layering scenario. List of GIS stacks behind the front end should show you how deep the customization can be made to achieve more advanced requirement. It is also important to note, that since requests are now automated, SQL queries can later be constructed to process further analysis.
For server-side processing and visualisation method, only Desa/Village (the 4th level of Indonesia administrative subdivisions) is available at the moment. It covers the whole islands and represents impact: polygon boundaries that have spatial relationship with current ring or areas that are being affected by each analytical zone footprint.
Visual output resulting from the built-in dataset also differs from that when user select their own custom data. Prepared data model is meant for precision query and coverage consistency. It is also designated to be used for advanced analysis involving thousands/millions records that can not be handled in browser. It makes most out of the power of PostGIS spatial database to conduct real geometry collision tests then delegates the result to OGC standard-compliant mapping server, in this case Geoserver, to get the final rendering. For user custom data, collision tests were done purely in browser using geometry bounding box.
There are many tools available for buffer operation such as Multi-Ring Buffer, Multi-Distance Buffer, InaSAFE plugin package for QGIS, or ArcGIS buffer to perform full-fledged analysis. Aside from being deliverable via web, what makes this tool unique is that both the distance buffering and aggregation are performed simultaneously using drag and drop. It leverages user ability to perceive their data as it rapidly generates answer for simple questions such as how many properties/people are there or how much land is there being affected in each zoning category. Output is then formulated using user-selected well-known statistic aggregate functions: COUNT, SUM, AVERAGE, MIN, and MAX. These functions are expandable.
APPLICATION LOGS:
Added WMS layer catalog to facilitate spatial view from available data
Added Arbitrary SQL Tool for data extraction and Query-On-Demand
Clientside shpw-writer.js apparently doesn't support polygon hole. Reversing the resulting multiple circle polygon order should do the trick, but only for display. Made another round trip to server for shp exporting using command line GDAL OGR2OGR tool.
Descriptions of the data used in the sample and featured layers:
1.
POPULATION
Extracted from PODES 2014, data represents population by sex and age group in Daerah Istimewa Yogyakarta (DIY) Province. The Village Potential Statistics (PODES) from the statistic bureau (BPS/Badan Pusat Statistik) is a complete enumeration of village/desa characteristics for all of Indonesia, with a sample of +/- 65,000. It could provide information on population and distribution, land uses, industrial development corridors, and other economic activities.
2.
GFS 10-meter WIND SPEED
Model estimation of average wind vector at 10 m above the ground. In most cases, actual observation of wind velocity at 10 m above ground is a little bit lower than modeled one. This visualization of wind direction in near-real time combines the visualization of wind direction data from the Global Forecast System (GFS) spectral model: http://nomads.ncep.noaa.gov GFS_high_resolution_doc.shtml as well as code developed by Cameron Beccario, to create a visualization by interpolating wind direction between measurements. The wind direction visualization utilizes data from the NOAA National Operational Model Archive & Distribution System, which may be accessed at http://nomads.ncep.noaa.gov hires_weather_datasets. Wind direction data are collected by satellite 4 times per day.
3.
DAFTAR SUAR INDONESIA (DSI)
List of Indonesia navigational beacons or DSI (Daftar Suar Indonesia) based on BPI (Berita Pelaut Indonesia) document. BPI (Berita Pelaut Indonesia) or Indonesian Notices to Mariners (ID-NM) is prepared and broadcasted weekly by Indonesian Navy HydroOceanographic Services (Dishidros). The sailors strongly requested to notify immediately to the Dishidros on the discovery of new hazards of shipping, changes or defects, in aids to navigation and of shortcomings in Indonesian nautical charts and other publications.
4.
Real-Time Flight Tracking Radar ( DEMO )
This demo utilizes a single data snapshot from ESRI GeoEvent Extension (Flight Aware Connector). For each update interval (2.5s), each flight position is then estimated using current speed and bearing (azimuth angle) to its flight destination airport. For deployment, these variables should be coming and updated regularly from terminal i.e: network of ADS-B receivers such as data stream from Flightradar24. This data stream provides a real-time feed of flight positions, flight status, ground data, combined from several data sources including Automatic Dependent Surveillance-Broadcast (ADS-B), MLAT and radar data around the world.
Indikator
Zoom ke BBOX
Multi Radial Impact Simulator is a web map tool designed to help user to create multiple dynamic distance ring buffer around a point feature. It can also be used to build simple analytical report using custom data with predefined set of columns. Application will try to extract the containment: what objects inside each ring, group the data, display it, then summarize the result based on preferences.
Model estimation of average wind vector at 10 m above the ground. In most cases, actual observation of wind velocity at 10 m above ground is a little bit lower than modeled one. This visualization of wind direction in near-real time combines the visualization of wind direction data from the Global Forecast System (GFS) spectral model: 
List of Indonesia navigational beacons or DSI (Daftar Suar Indonesia) based on BPI (Berita Pelaut Indonesia) document. BPI (Berita Pelaut Indonesia) or Indonesian Notices to Mariners (ID-NM) is prepared and broadcasted weekly by Indonesian Navy HydroOceanographic Services (Dishidros). The sailors strongly requested to notify immediately to the Dishidros on the discovery of new hazards of shipping, changes or defects, in aids to navigation and of shortcomings in Indonesian nautical charts and other publications.
This demo utilizes a single data snapshot from ESRI GeoEvent Extension (Flight Aware Connector). For each update interval (2.5s), each flight position is then estimated using current speed and bearing (azimuth angle) to its flight destination airport.