Finding suitable spots for placing heart defibrillator equipments in public¶
In this sample, we will observe how site suitability analyses can be performed using the ArcGIS API for Python. The objective of this sample is to find locations in the city of Philadelphia that are suitable for placing AED (Automated External Defibrillator) for public emergencies.
The criteria for a suitable place are those that have high incidence of OHCA (Out of Hospital Cardiac Arrests) and be accessible to public, such as commercial areas.
As inputs, we start with geocoded OCHA (Out-of-Hospital Cardiac Arrest) point data, along with a few base layers for the city of Pittsburgh published as feature layers. As output, we need to generate a list of locations that have a high incidence of heart-attacks and located within commercial areas, allowing easy access at times of emergencies.
Connect to the GIS¶
from arcgis.gis import *
gis = GIS("https://python.playground.esri.com/portal","arcgis_python","amazing_arcgis_123")
Preview the input datasets¶
ohca_item = gis.content.search("Pittsburgh_heart_attacks", "Feature Layer")[0]
ohca_item
Feature Layer Collection by arcgis_python
map1 = gis.map("Pittsburgh, PA", zoomlevel=12)
map1
map1.add_layer(ohca_item)
Let us take a look at the layers available in this item
for lyr in ohca_item.layers:
print(lyr.properties.name)
Outline of the analysis¶
For the rest of this analysis, we will use the Zoning polygon layer and build a 600-foot buffers around it to represent areas that are accessible to commercial zones. Next, we will use the Heart attack incidence point layer to build a density raster. This raster depicts those areas that have a higher incidence of cardiac arrests. Finally, we will overlay the buffers on the density raster to pick places that are suitable to place new AED devices.
Create a 600 feet buffer around commercial areas¶
The Zoning feature layer contains polygon features that represent different zones such as commercial, residential etc. We need to select those features that correspond to commercial zones and create a buffer of 600 feet around them. The 600 feet area roughly corresponds to two-blocks, a walk able distance in case of an emergency.
Select commercial zones¶
To select the commercial zones using a query, we need to know what columns and values are available. Hence, let us construct a small query that gives the first few rows / features.
zoning_flayer = ohca_item.layers[2]
zoning_fset = zoning_flayer.query(result_record_count=10, return_all_records=False)
zoning_fset.sdf
The column zoning_grouped contains zoning categories. We are intersted in those polygons that correspond to the Commercial category.
zoning_commercial_fset = zoning_flayer.query("zoning_grouped = 'Commercial'")
commercial_zone_df = zoning_commercial_fset.sdf
commercial_zone_df.head(5)[['name','zoning_grouped']] #display the first 5 results
commercial_zone_df.shape
Let us draw the selected polygons on a map
zone_map = gis.map("Pittsburgh, PA")
zone_map