Building Latent Constructs:311

In the present analysis I continued to build the measures for the latent and manifest variables. I will be measuring the latent construct which is resilience. I will be measuring it based on the frequency of reporting public requests at the tract level. Additionally, the time taken to respond to requests and the diversity of the income and race at the tract level. I will start the analysis with the aggregates. I aggregated on two levels. The first level is the sum and the average of public request, and I grouped them by tract. Second, I aggregated the average time taken for public requests and I grouped them by the tract ID.

Aggregation: Public requests total and average

PublicRequests<-df_311 %>% group_by(CT_ID_10) %>% summarise(Average_PUBLICRequest=mean(PUBLIC),Total_PUBLICRequest=sum(PUBLIC))

Aggregation: Average time

df_311$DELTA_DT<-as.character(as.numeric(df_311$DELTA_DT))

Time <- sqldf(“select DELTA_DT,PUBLIC,CT_ID_10, sum(DELTA_DT)/count(DELTA_DT) as average_time from df_311 where PUBLIC =’1′  group by CT_ID_10”)
nrow(Time)

Merging

After I aggregated, I did the merge with the census data, the first merge is between the total and the sum the of public requests and the census data. The second merge is the average public requests and the census data.

The Analysis

I will focus on the below sample of tracts to do my analysis, the selection of the tracts based on the number of requests and the average time taken to respond to the requests accordingly I will be measuring my latent construct and see also the correlations between different variables.

(25025070500 -25025070300-25025130300-25025090901-25025010405)

25025070300

Let’s start with the first tract we have here, we have total public requests are 1228 while the total population is 4450. The medhouseincome 115887. In-terms of the race in this tract, we have the following White:0.7388, Black:0.0139, Asian:0.11, Hispanic 0.07, Two or more: 0.04. The percentage of renters in this tract is 0.52 while the homeowners 0.470. The time taken to respond to the public request in this tract is 7 days.

25025070500

The second tract we have here, we have total public requests are 1096 while the total population is 5843 The medhouseincome 62717. In-terms of the race in this tracts, we have the following White:0.497, Black:0.06, Asian:0.193 ,Hispanic 0.21, Two or more: 0.02. The percentage of renters in this tract is 0.58 while the homeowners 0.419 The average time taken to respond to the public requests in this tracts is 16 days.

25025130300

 The third tract we have here, we have total public requests are 100 while the total population is 4456 The average income 123241 In-terms of the race in this tracts, we have the following White:0.83, Black:0.04, Asian:0.05 ,Hispanic 0.06, Two or more: 0.02. The percentage of renters in this tract is 0.23 while the home owners 0.76 The time taken to respond to the public request in this tracts is 11 days.

25025090901

The fourth tract we have here, we have total public requests are 14 while the total population is 3653 The average medhouseincome 25662 In-terms of the race in this tract, we have the following White:0.251, Black:0.11, Asian:0.26, Hispanic 0.33, Two or more: 0.02. The percentage of renters in this tract is 0.99 while the home owners 0.002 The time taken to respond to the public request in this tracts is 7 days.

25025000802

The Fifth tract we have here, we have total public requests are 109 while the total population is 6701 The average income 60772 In-terms of the race in this tract, we have the following White:0.53, Black:0.04, Asian:0.20, Hispanic 0.12, Two or more: 0.033 The percentage of renters in this tract is 0.81 while the homeowners 0.18 The time taken to respond to the public request in this tracts is 51 days.

Based on the previous analysis on the random selected sample, we can understand that the higher the income and the higher the percentage of homeowners. The frequency of reporting public problems increases and the faster the response from 311 to solve the problem (Example Tract One)

The lower the income, the less the problems reported the percentage of renters as well will increase accordingly to the percentage of homeowners, Also the longer the time it takes to solve the problem (Example Tract Four) Based on previous tract we can say that there might be a correlation between the race and income and if the residents are homeowners or renters to the number of the public problems reported as well as to the response from the system, but we need to do further analysis to be sure of the accuracy of the analysis.

Visualizations:

The below bar plots to visualize the percentage of race in each tract and it is correlation with the med house income. As we can see the higher the income the less race/ethnicity diversity in the tract and the less the income the more diverse the tract is.

The graph below to see the correlation between the total population in the tract and the average time taken to solve the request.

Conclusions

Based on the present analysis, I tried to measure the resilience in different tracts through different variables which include: the system performance, the community engagement through reporting public requests as well as the diversity in each tract based on income and race. There is some bias which is challenging because some of the neighborhoods don’t have problems in the public sphere to report. However, my ultimate goal for this measurement is to see which neighborhoods are more resilient. For instance, if there is a high number of public requests, diversity and the system is responsive, accordingly this neighborhood is considered resilient.

In contrast, if the neighborhood is less diverse in-terms of race and income but the there is public engagement, and the system is responsive this is could also be a good example to guide policy makers, planners and researchers. The effect of this would be more informative planning to have more resilient cities through evidence on what’s needed in this particular neighborhood to be resilient. This knowledge would also guide better planning to achieve resilience in other neighborhoods.