library(tidyverse)This repo contains explorations of data that is published openly. Topics include: Waste Management, Air Quality, Wastewater Management, Faecal Sludge Management, WASH (Water, Sanitation, and Hygiene).
For now, all exploration is done in a single R Markdown script rendered to this README file. As the document grows, it will be split into sets of scripts per topic.
Reference: (Innovations 2020)
Results of municipal solid waste characterization study conducted in Abidjan, Cote d’Ivoire, in February-March, 2018 by Waste2Worth Innovations.
dat <- read_csv("data/raw/@innovations2020abidjan/Abidjan, Cote d'Ivoire, waste characterization data, Feb-Mar 2018.csv") %>%
janitor::clean_names() %>%
group_by(sample) %>%
mutate(
percent = mass_kg / sum(mass_kg) * 100
) %>%
ungroup()- 2080 observations
- 8 days of data collection
- 80 samples with measurements in 26 waste categories each
dat %>%
count(date)## # A tibble: 8 × 2
## date n
## <chr> <int>
## 1 1-Mar 260
## 2 2-Mar 260
## 3 23-Feb 260
## 4 24-Feb 156
## 5 26-Feb 286
## 6 27-Feb 260
## 7 28-Feb 234
## 8 3-Mar 364
dat %>%
count(sample)## # A tibble: 80 × 2
## sample n
## <chr> <int>
## 1 1-A1 26
## 2 1-A2 26
## 3 1-A3 26
## 4 1-A4 26
## 5 1-A5 26
## 6 1-B1 26
## 7 1-B2 26
## 8 1-B3 26
## 9 1-B4 26
## 10 1-B5 26
## # … with 70 more rows
dat %>%
count(date, sample)## # A tibble: 80 × 3
## date sample n
## <chr> <chr> <int>
## 1 1-Mar 1-A1 26
## 2 1-Mar 1-A2 26
## 3 1-Mar 1-A3 26
## 4 1-Mar 1-A4 26
## 5 1-Mar 1-A5 26
## 6 1-Mar 1-B1 26
## 7 1-Mar 1-B2 26
## 8 1-Mar 1-B3 26
## 9 1-Mar 1-B4 26
## 10 1-Mar 1-B5 26
## # … with 70 more rows
dat %>%
count(category) ## # A tibble: 26 × 2
## category n
## <chr> <int>
## 1 (Household) Hazardous waste 80
## 2 All Low Value Plastic 80
## 3 Aluminum Waste 80
## 4 Cardboards 80
## 5 Copper waste 80
## 6 Ferrous Metals 80
## 7 Fines 80
## 8 Food/Organic 80
## 9 Glass 80
## 10 HDPE (#2) 80
## # … with 16 more rows
dat %>%
arrange(sample)## # A tibble: 2,080 × 5
## date sample category mass_kg percent
## <chr> <chr> <chr> <dbl> <dbl>
## 1 1-Mar 1-A1 Food/Organic 36.6 49.8
## 2 1-Mar 1-A1 Paper (white) 0 0
## 3 1-Mar 1-A1 Other recyclable Paper 0.4 0.545
## 4 1-Mar 1-A1 Non-recyclable Paper 0.72 0.981
## 5 1-Mar 1-A1 Cardboards 2.92 3.98
## 6 1-Mar 1-A1 Textile 3.83 5.22
## 7 1-Mar 1-A1 All Low Value Plastic 4.29 5.84
## 8 1-Mar 1-A1 Medium Value Plastic (#4) 2.46 3.35
## 9 1-Mar 1-A1 PP (#5) 0.55 0.749
## 10 1-Mar 1-A1 PET (#1) 0.32 0.436
## # … with 2,070 more rows
dat %>%
group_by(sample) %>%
summarise(
sum_mass_kg = sum(mass_kg)
) %>%
arrange(desc(sum_mass_kg)) ## # A tibble: 80 × 2
## sample sum_mass_kg
## <chr> <dbl>
## 1 24-B2 166
## 2 24-B1 166.
## 3 23-A3 135.
## 4 23-B5 134.
## 5 23-B2 128.
## 6 23-B4 127.
## 7 23-B3 126.
## 8 26-B5X 126.
## 9 3-A2 123.
## 10 23-B1 119.
## # … with 70 more rows
dat %>%
arrange(sample) %>%
group_by(category) %>%
summarise(
count = n(),
mean = mean(percent),
sd = sd(percent),
min = min(percent),
max = max(percent)
) %>%
arrange(desc(mean)) %>%
knitr::kable(digits = 1)| category | count | mean | sd | min | max |
|---|---|---|---|---|---|
| Food/Organic | 80 | 62.5 | 11.0 | 27.4 | 89.7 |
| Inerts | 80 | 5.8 | 7.8 | 0.0 | 33.3 |
| All Low Value Plastic | 80 | 5.7 | 2.8 | 2.7 | 13.6 |
| Hygiene Items | 80 | 5.2 | 3.5 | -0.6 | 13.6 |
| Medium Value Plastic (#4) | 80 | 3.4 | 1.7 | 0.0 | 7.5 |
| Textile | 80 | 3.1 | 2.5 | 0.1 | 12.0 |
| Cardboards | 80 | 2.1 | 1.9 | 0.0 | 8.8 |
| Non-recyclable Paper | 80 | 2.0 | 1.7 | 0.0 | 8.2 |
| Glass | 80 | 1.6 | 2.1 | 0.0 | 12.9 |
| Unclassified Waste | 80 | 1.4 | 2.8 | 0.0 | 17.4 |
| Other recyclable Paper | 80 | 1.2 | 1.3 | 0.0 | 6.0 |
| Other Ferrous Metals | 80 | 1.0 | 0.8 | 0.0 | 3.1 |
| Rattan & Wood | 80 | 0.9 | 1.1 | 0.0 | 5.7 |
| PET (#1) | 80 | 0.8 | 1.1 | 0.0 | 8.7 |
| Onter Non-Ferrous Metals | 80 | 0.8 | 0.6 | 0.0 | 4.0 |
| PP (#5) | 80 | 0.4 | 0.4 | 0.0 | 2.4 |
| HDPE (#2) | 80 | 0.4 | 0.4 | 0.0 | 2.2 |
| Leather | 80 | 0.4 | 0.8 | 0.0 | 5.0 |
| Rubber | 80 | 0.4 | 0.5 | 0.0 | 2.2 |
| Ferrous Metals | 80 | 0.3 | 0.5 | 0.0 | 2.8 |
| (Household) Hazardous waste | 80 | 0.2 | 0.2 | 0.0 | 1.2 |
| Aluminum Waste | 80 | 0.1 | 0.2 | 0.0 | 0.7 |
| Paper (white) | 80 | 0.1 | 0.3 | 0.0 | 2.3 |
| PVC (#3) | 80 | 0.1 | 0.3 | 0.0 | 2.4 |
| Copper waste | 80 | 0.0 | 0.1 | 0.0 | 1.3 |
| Fines | 80 | 0.0 | 0.0 | 0.0 | 0.1 |
dat %>%
ggplot(aes(x = sample, y = percent, fill = category)) +
geom_col()
dat %>%
ggplot(aes(x = reorder(category, percent), y = percent)) +
coord_flip() +
geom_jitter(width = 0.1, alpha = 0.3)
Reference: (Ramadan, Rachman, and Matsumoto 2022)
readxl::read_excel("data/raw/@ramadan2022activity/10163_2022_1371_MOESM2_ESM.xls")## # A tibble: 20 × 17
## ...1 `Sub-district` District `Average Slope` `Average elevati…
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Cluster … Wonolopo Mijen 15 230
## 2 <NA> Podorejo Ngaliyan 5 123
## 3 <NA> Rowosari Tembalang 10 41
## 4 <NA> Tugurejo Tugu 2 5
## 5 Cluster … Penggaron Kidul Pedurung… 2 19
## 6 <NA> Kandri Gunungpa… 5 230
## 7 <NA> Tambakharjo Semarang… 5 6
## 8 <NA> Gedawang Banyuman… 5 214
## 9 Cluster … Gayamsari Gayamsari 2 10
## 10 <NA> Karangroto Genuk 2 8
## 11 <NA> Karang Tempel Semarang… 2 10
## 12 <NA> Sampangan Gajah Mu… 5 18
## 13 Cluster … Jagalan Semarang… 2 10
## 14 <NA> Barusari Semarang… 5 12
## 15 <NA> Candi Candisari 5 55
## 16 <NA> Purwosari Semarang… 2 6
## 17 <NA> <NA> <NA> NA NA
## 18 <NA> Total education facili… <NA> NA NA
## 19 <NA> Total healthcare facil… <NA> NA NA
## 20 <NA> Total economic facilit… <NA> NA NA
## # … with 12 more variables: Area of agricultural land (ha) <dbl>,
## # Area of non-agricultural land (ha) <dbl>,
## # Number of Population (inhabitant) <dbl>, Area (km2) <dbl>,
## # Number of Household (Unit) <dbl>, Housing Density (HH/km2) <dbl>,
## # Population Density (people/km2) <dbl>,
## # Total Education Facility (Unit) <dbl>,
## # Total Healthcare Facility (Unit) <dbl>, …
Innovations, Waste2Worth. 2020. “Abidjan, Cote d’Ivoire, Waste Characterization Data, Feb-Mar 2018.” Zenodo. https://doi.org/10.5281/zenodo.4012765.
Ramadan, Bimastyaji Surya, Indriyani Rachman, and Toru Matsumoto. 2022. “Activity and Emission Inventory of Open Waste Burning at the Household Level in Developing Countries: A Case Study of Semarang City.” Journal of Material Cycles and Waste Management, February. https://doi.org/10.1007/s10163-022-01371-3.
Zürich, Stadt. 2022. “Abfallgefässe - Stadt Zürich.” https://www.stadt-zuerich.ch/geodaten/download/Abfallgefaesse.