electricsheepasia/asia-iraq-fsmt-sites-feb-2017
Formal Sites Monitoring Tool (FSMT) Publisher: CCCM Cluster · Source: HDX · License: cc-by · Updated: 2023-10-18 Abstract The Formal Sites Monitoring Tool (FSMT) is a camp management monitoring tool, designed to provide a synopsis of the main demographic information at site level as well key humanitarian indicators for all formal sites across Iraq. Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2023-10-18.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-iraq-fsmt-sites-feb-2017.
Formal Sites Monitoring Tool (FSMT)
Publisher: CCCM Cluster · Source: HDX · License: cc-by · Updated: 2023-10-18
Abstract
The Formal Sites Monitoring Tool (FSMT) is a camp management monitoring tool, designed to provide a synopsis of the main demographic information at site level as well key humanitarian indicators for all formal sites across Iraq.
Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2023-10-18. Geographic scope: IRQ.
Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).
Dataset Characteristics
Variables
Geographic — coordinates_latitude (range 30.5491–37.1982), coordinates_longitude (range 42.4453–47.7582), site_typology (camp, collectivecentre), `populationtracking, extensiontotalplots` (range 8.0–7000.0) and 33 others.
Temporal — month (january, december, november).
Demographic — camp_mngmt_male_staff (range 0.0–24.0), camp_mngmt_female_staff (range 0.0–10.0), total_hh_total_families (range 2.0–5450.0), ind_ages_ind_age_groups_males_0_4 (range 2.0–2180.0), ind_ages_ind_age_groups_females_0_4 (range 1.0–4760.0) and 16 others.
Outcome / Measurement — governorate (Anbar, Baghdad, Ninewa).
Identifier / Metadata — camp_name (caravans camp, AL-Shams collective centre, Habbaniya Tourist City-Blue camp (Al-Azraq)), enum_name (Manar Al-Jassas, Mahdi Ahmed, Basil Tawfeeq), site_name (IQ0102-0019, IQ0102-0033, IQ0102-0002), wash_wash_source, esa_source and 1 others.
Other — enum_nub (aljassas@unhcr.org, 0000, 610), area (aaf, htc-aq, bzbz), sub_site (IQ0102-0001-001, IQ0102-0001-002, IQ0102-0001-009), site_new (AL-Shams collective centre, Al-Israa Wa Al-Maaraj Mosque / Al-Safa Mosque Previously, Al-Jawaden Mosque), camp_mngt` and 47 others.
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/asia-iraq-fsmt-sites-feb-2017")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()Schema
Numeric Summary
Curation
Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 1 column(s) with >80% missing values were removed: `educationrecreational`. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
Limitations
- Data originates from CCCM Cluster and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling:
enum_nub,area,sub_site,site_new,nfi_items_other_specify,protection_services_prot,education_boys_secondary,education_girls_secondary.... - Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.
Citation
@dataset{hdx_asia_iraq_fsmt_sites_feb_2017,
title = {Formal Sites Monitoring Tool (FSMT)},
author = {CCCM Cluster},
year = {2023},
url = {https://data.humdata.org/dataset/iraq-fsmt-sites-feb-2017},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.
