I need desertation written on the below | Engineering homework help

1. What is the spatial impact of a hospital closure on the residences and workplaces of healthcare workers?

Example projects §Other ideas: § Employment impact (in surrounding areas) of construction of a new stadium § Impact of the implementation of a community development program in Chicago on building permits § Characteristics of TNP travel patterns by neighborhood § Economic impact of TNP accessibility in Chicago § Retail market area delineation Description2.html Pollution Equity in London A Brief Description These datasets are obtained from the London Datastore https://data.london.gov.uk - each one relates to a kind of pollution - either potentially problematic materials in the air, or levels of noise. The key files are

• Rail_Lden_London.geojson
• Road_Lden_London.geojson

These measure daily average decibel levels for road- and rail-originated noise in London. In addition, the file pm25.csv gives average levels of \(\textsf{PM}_{\textsf{2.5}}\) per Output Area (OA) in London. OAs are the smallest geographical census units in the UK ( https://data.london.gov.uk/dataset/pm2-5-map-and-exposure-data). \(\textsf{PM}_{\textsf{2.5}}\) is the concentration of solid particles and liquid droplets with a diameter less than 2.5 micrometres, and is thought to be the air pollutant which has the greatest impact on human health. Finally the file LOAC.geojson contains a map layer with the boundary each of the OAs in London, together with their OAC (output area classification). The OAC is a geodemographic classification applied to each OA used to describe the social and economic composition of its population. There are three levels of detail for the classification: Supergroup, Group and Subgroup with column names Sub,Group and Super. Typical examples of the codes are: - Supergroup H (Urban Settlements) : The areas are characterised by a slightly younger age structure than nationally, with higher proportions of all groups aged 45 and under (covering the age groups 0 to 4 years, 5 to 14 years and 25 to 44 years). Ethnic groups are over-represented compared with the national picture and households are more likely to live in semi-detached or terraced housing. - Group H2 (Suburban Traits) : This group has a higher proportion of people aged 25 to 44 years than the supergroup and a higher proportion have Chinese, Black, African, Caribbean or Black British ethnicity, and are likely to live in flats. - Subgroup H2a (City Periphery) : The subgroup has a slightly older age profile than its parent group and a larger proportion were born in the UK. Residents are more likely to live in a terraced property or flats. Thus, ‘drilling down’ to more detailed levels gives a greater level of description. Full details can be found here: https://www.ons.gov.uk/methodology/geography/geographicalproducts/areaclassifications/2011areaclassifications/penportraitsandradialplots - note that in our data the Supergroups are labelled A-H, but are referred to in the description as 1-8, similar for the Groups (but with roles of letters and numbers reversed) and Subgroups.

The noise pollution data can be read in to R using the following code:

library(tidyverse) library(sf) rail_noise <- st_read('Rail_Lden_London.geojson') head(rail_noise) Note that the noise levels (NoiseClass) are categorical and stored as characters. They can be converted into ordered factors. NoiseLevels <- c("<=54.9","55.0-59.9","60.0-64.9","65.0-69.9","70.0-74.9",">=75.0" ) Order_Levs <- function(x) ordered(x,levels=NoiseLevels) rail_noise <- rail_noise %>% mutate(NoiseClass=Order_Levs(NoiseClass)) ggplot(rail_noise) + geom_sf(mapping=aes(fill=NoiseClass,col=NoiseClass)) Here, the colour coding corresponds to the noise level category. The lowest category (\(\le \textsf{54.9}\)) is left out of the geographical layer here. The OAC data can also be read in and visualised.Firstly, here is a quick glance at the data oac <- st_read('LOAC.geojson') ## Reading layer `LOAC' from data source `/Users/chrisbrunsdon/Dropbox/NCG603_sandbox/NCG616/London/LOAC.geojson' using driver `GeoJSON' ## Simple feature collection with 25053 features and 21 fields ## Geometry type: MULTIPOLYGON ## Dimension: XY ## Bounding box: xmin: 503574.2 ymin: 155850.8 xmax: 561956.7 ymax: 200933.6 ## Projected CRS: OSGB 1936 / British National Grid head(oac) ## Simple feature collection with 6 features and 21 fields ## Geometry type: MULTIPOLYGON ## Dimension: XY ## Bounding box: xmin: 526546.8 ymin: 170665 xmax: 544029.6 ymax: 193902 ## Projected CRS: OSGB 1936 / British National Grid ## OA11CD LSOA11CD WD11CD_BF WD11NM_BF LAD11CD LAD11NM RGN11CD ## 1 E00023264 E01004612 E05000626 Tooting E09000032 Wandsworth E12000007 ## 2 E00003359 E01000692 E05000111 Chislehurst E09000006 Bromley E12000007 ## 3 E00023266 E01004615 E05000626 Tooting E09000032 Wandsworth E12000007 ## 4 E00020264 E01004027 E05000548 Riverside E09000028 Southwark E12000007 ## 5 E00023263 E01004613 E05000626 Tooting E09000032 Wandsworth E12000007 ## 6 E00007412 E01001492 E05000204 Lower Edmonton E09000010 Enfield E12000007 ## LSOA11NM USUALRES HHOLDRES COMESTRES POPDEN HHOLDS AVHHOLDSZ PunCare ## 1 Wandsworth 032C 462 459 3 115.2 143 3.2 42 ## 2 Bromley 002D 269 259 10 36.7 133 1.9 24 ## 3 Wandsworth 034B 277 277 0 183.4 133 2.1 26 ## 4 Southwark 003E 415 415 0 96.1 191 2.2 22 ## 5 Wandsworth 033D 304 304 0 165.2 131 2.3 22 ## 6 Enfield 025E 427 427 0 165.5 150 2.8 39 ## PLMTACT area OA Sub Super Group geometry ## 1 15.36 0.041 E00023264 C3d C C3 MULTIPOLYGON (((527635.5 17... ## 2 12.64 0.073 E00003359 F1a F F1 MULTIPOLYGON (((543581.5 17... ## 3 25.27 0.015 E00023266 B1a B B1 MULTIPOLYGON (((526613.5 17... ## 4 13.98 0.044 E00020264 B3a B B3 MULTIPOLYGON (((533563.5 17... ## 5 15.46 0.019 E00023263 G1b G G1 MULTIPOLYGON (((527822 1720... ## 6 13.11 0.027 E00007412 G2b G G2 MULTIPOLYGON (((535109.5 19... There are quite a few variables - probably the key ones are the Supergroup, Group and Subgroup OAC classifications, the Output Area ID code (to link with other data), and some of the census-derived variables (ie POPDEN for population density). Here the pattern of Supergroup classification is mapped: ggplot() + geom_sf(data=oac,mapping=aes(fill=Super),col=NA) + scale_fill_brewer(type='qual',palette = 'Set3') Clearly there is a geographical pattern. The file pm25.csv contains average densities of \(\textsf{PM}_{\textsf{2.5}}\) (for 2013, averaged across Output Areas) - this can be read in and joined to the OAC file. pm25 <- read_csv('pm25.csv') head(pm25) ## # A tibble: 6 × 3 ## OA11CD LAD11NM PM252013me ## <chr> <chr> <dbl> ## 1 E00024024 Westminster 18.0 ## 2 E00023833 Westminster 18.2 ## 3 E00023830 Westminster 18.7 ## 4 E00023831 Westminster 17.9 ## 5 E00024021 Westminster 17.2 ## 6 E00023887 Westminster 17.5 The variable OA11CD is the Output Area code. LAD11NM is the name of the local authority district (from the 2011 census) and PM252013me is the average level of \(\textsf{PM}_{\textsf{2.5}}\). Note that OA11CD is also in the oac object, and so one can join this table to that one, and map the \(\textsf{PM}_{\textsf{2.5}}\) levels. oac <- oac %>% left_join(pm25,by='OA11CD') %>% rename(pm25=PM252013me) ggplot() + geom_sf(data=oac,mapping=aes(fill=pm25),col=NA) + scale_fill_viridis_c() Suggested Research Questions

• Are some social groups more exposed to high \(\textsf{PM}_{\textsf{2.5}}\) levels?
• Are some social groups more exposed to rail or road based noise levels than others?
• Are different groups subject to different kinds of pollution?
• Are there links between the different kinds of pollution?