Mapping Urban Health

Prepared by

Patel Shrujal Bharatkumar (PP0006017) Guided by

Prof. Bindi Dave | Prof. Ashish

MAPPING URBAN HEALTH The shortest route of health facility from HOMEWELL SENIOR CARER to PRIMROSE RETIREMENT COMMUNITY OF PUEBLO Layers selected: a. Health Facilities b. Road Network The shortest route from Home Well Senior Carer to Primrose retirement community of Pueblo is found using the Network Analysis.

The Lenght of the shortest route between these two points come out to be around 250 km.

MAPPING URBAN HEALTH Six Nearest Ambulance Service from the fire incident Layers selected: a. Ambulance b. Road Network c. Hypothetical Fire Incident Spot The six nearest ambulance service from a fire incident is found using the Network Analysis.

A hypothetical spot was selected where the incident has occured and then the six nearest facilities were identified.

The Nearest ambulance was found to be at a distance of around 6 Km. and the Farthest ambulance service at a distance of about 10 Km.

MAPPING URBAN HEALTH Service Area Network within 20 kilometer from Acute Treatment Units, Birth Centres, Community Mental Health Centre Layers selected: a. Health Facilities b. Road Network The service area network within 20 Km. is found using the Network Analysis.

There are only few areas in the county that are covered within 20 Km. while a majority of the areas remains unserviced, suggesting that a large number of population to travel a lot to access this facility.

But when looked upon with the population chart across the regions where these facilities are placed are the regions where a large amount of population of county is concentrated.

MAPPING URBAN HEALTH Mean Center of Hospital Layers selected: a. Hospitals b. County Boundaries The mean centre for hospitals is obtained using the spatial statistics tool.

Mean centre of Hospitals give us an idea of geographical ditribution of hospitals across the county.

It also helps us in identifying the ideal location where the facilities have a greater use and importance.

As visible from the map the mean centre for Colorado county falls into Jefferson sub county. Here it is the good position as when overlayed with population density map it was observed that the area where mean centre lie is the location with high concentration of population densities.

MAPPING URBAN HEALTH Cluster and Outlier Analysis Layers selected: a. Health Facilities b. Zip Code Boundary The Cluster and Outlier analysis for Hospitals and HHA is obtained using the spatial statistics tool.

Cluster and Outlier analysis on the basis of LIC Beds helps us in identifying the concentration and spread of health facilitie (in this case).

As visible from the map a majority of the facilities lie into the category of not significant, where as few lie into category of high outlier, and low outlier suggesting a concentration of LIC Beds into the facilties in these areas. When looked upon across the population we see that a larger population is concentrated in the area where there is a high concentration of the clusters.

MAPPING URBAN HEALTH Hotspot Analysis for Ambulance on base of LIC Beds Layers selected: a. Ambulance Facilities b. Zip Code Boundary The hotspot analysis for ambulance facilities is obtained using the spatial statistics tool.

Hotspot analysis here was done on the based the number of LIC Beds. this tool basically identifies statiscally significant spatial cluster of high and low values.

There are no significant hotspots for ambulance observed in colorado county, but there are coldspots identified in the area where there is high population concentration, suggesting a requirement of more ambulance facilities in these areas.

MAPPING URBAN HEALTH Multi Distance Cluster Analysis K Function

Clustered

ExpectedK ObservedK

200000 190000 180000 170000 160000 150000 140000 130000 120000 110000 L(d) 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000

Layers selected: a. Health Facilities The multi distance cluster analysisfor health facilities is obtained using the spatial statistics tool.

The analysis helps us in understanding wether the predicted clustring pattern of the facilities is actually clustered or not. It analyses the result based on comparision of the Observed K value and the Expected K value.

20000

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Distance

90000 100000 110000 120000 130000

Dispersed

Here, since the observed k value is greater than that of the expected k value we can say that the pattern of distribution of health facilities is clustered.

MAPPING URBAN HEALTH High - Low Clustering Pattern for Ambulance Layers selected: a. Ambulance Facilities The average nearest neighbour for ambulance facilities is obtained using the spatial statistics tool.

The tool is mostly used to analyse the pattern of spread of (here) Ambulance facilities across colorado county. The pattern is identified using Z-score and P-value. looking at the score for clustering viz far from 1, we can conclude that null hypothesis of random distribution is true. In a sense that there is a random pattern of distribution of ambulance facilities according to LIC Beds.There is no particular pattern followed when we look at ambulance facilities from perspective of number of LIC beds.

MAPPING URBAN HEALTH Average Nearest Neighbour Analysis for Ambulance Layers selected: a. Ambulance Facilities The average nearest neighbour for ambulance facilities is obtained using the spatial statistics tool.

The tool is mostly used to analyse the pattern of spread of (here) Ambulance facilities across colorado county. The pattern is identified using Z-score and P-value. looking at the NNR viz close to 1, we can conclude that null hypothesis of random distribution is proved false.

The above statement also proves the ambulance facilities are clustered and not spread.

There can be various issues due to clusterisation of such facilities, like it takes much longer to reach certain areas of the county which is not good ideally.

MAPPING URBAN HEALTH Similarity between Health Facilities and Ambulance Layers selected: a. Ambulance Facilities b. Health Facilities The similarity search for ambulance facilities and Health Facilities is obtained using the spatial statistics tool.

The tool is mostly used to analyse the similarity between any two feature datasets Here, we can see that the most similarity in terms of LIC bedsis found in the areas where there is high concentration of population.

Suggesting that areas with low population require more number of such similar functioning institutes in order to reduce load on certain prime institutes and also to reduce the distance that is required to travel in emergency.