CoolFace
Datasetpublic

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.

sourceHugging Facecc-by-4.0updated 5mo agoView on Hugging Face
0likes6downloads
Dataset Card

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

DomainFood security and nutrition
Unit of observationGeolocated point observations
Rows (total)165
Columns119 (43 numeric, 76 categorical, 0 datetime)
Train split132 rows
Test split33 rows
Geographic scopeIRQ
PublisherCCCM Cluster
HDX last updated2023-10-18

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

python
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

ColumnTypeNull %Range / Sample Values
camp_nameobject0.6%caravans camp, AL-Shams collective centre, Habbaniya Tourist City-Blue camp (Al-Azraq)
enum_nameobject0.0%Manar Al-Jassas, Mahdi Ahmed, Basil Tawfeeq
enum_nubobject20.6%aljassas@unhcr.org, 0000, 610
coordinates_latitudefloat640.0%30.5491 – 37.1982 (mean 33.8223)
coordinates_longitudefloat640.0%42.4453 – 47.7582 (mean 44.0131)
governorateobject0.0%Anbar, Baghdad, Ninewa
site_typologyobject0.0%camp, collective_centre
areaobject53.9%aaf, htc-aq, bzbz
sub_siteobject55.2%IQ0102-0001-001, IQ0102-0001-002, IQ0102-0001-009
site_nameobject0.0%IQ0102-0019, IQ0102-0033, IQ0102-0002
site_newobject78.2%AL-Shams collective centre, Al-Israa Wa Al-Ma`araj Mosque / Al-Safa Mosque Previously, Al-Jawaden Mosque
monthobject0.0%january, december, november
camp_mngtobject0.0%
camp_mngmt_officeobject0.0%
camp_mngmt_male_staffint640.0%0.0 – 24.0 (mean 4.0727)
camp_mngmt_female_staffint640.0%0.0 – 10.0 (mean 0.8121)
camp_committeeobject16.4%
population_trackingobject0.0%
plots_occupied_plotsint640.0%2.0 – 5450.0 (mean 508.5455)
plots_not_occupied_plotsint640.0%0.0 – 1639.0 (mean 102.3333)
extension_total_plotsint640.0%8.0 – 7000.0 (mean 610.8545)
extension_total_plots_confirmobject0.0%
extension_extension_plannedobject0.0%
shelter_types_tent_cementint640.0%0.0 – 5000.0 (mean 320.9333)
shelter_types_tent_groundint640.0%0.0 – 7000.0 (mean 197.1697)
shelter_types_caravanint640.0%0.0 – 1600.0 (mean 64.6727)
shelter_types_improvised_shelterint640.0%0.0 – 1600.0 (mean 16.5758)
shelter_types_single_fam_unitint640.0%0.0 – 1800.0 (mean 16.9879)
shelter_types_collectiveint640.0%-12.0 – 46.0 (mean 1.2061)
shelter_types_rubhallint640.0%0.0 – 162.0 (mean 1.3879)
shelter_types_openairint640.0%0.0 – 20.0 (mean 0.1939)
shelter_types_overcrowded_family_plotsobject0.0%
total_hh_total_familiesint640.0%2.0 – 5450.0 (mean 479.0909)
ind_ages_ind_age_groups_males_0_4int640.0%2.0 – 2180.0 (mean 198.8667)
ind_ages_ind_age_groups_females_0_4int640.0%1.0 – 4760.0 (mean 225.6727)
ind_ages_ind_age_groups_males_5_17int640.0%0.0 – 5479.0 (mean 428.3697)
ind_ages_ind_age_groups_females_5_17int640.0%0.0 – 5279.0 (mean 424.6182)
ind_ages_ind_age_groups_males_18_59int640.0%
ind_ages_ind_age_groups_females_18_59int640.0%
ind_ages_ind_age_groups_males_60_overint640.0%
ind_ages_ind_age_groups_females_60_overint640.0%
ind_ages_total_populationint640.0%
ind_ages_vulnerabilities_groups_f_hohint640.0%
ind_ages_vulnerabilities_groups_child_hohint640.0%
ind_ages_vulnerabilities_groups_unaccompanied_separatedint640.0%
ind_ages_vulnerabilities_groups_ppl_physical_disint640.0%
ind_ages_vulnerabilities_groups_ppl_mental_disint640.0%
ind_ages_vulnerabilities_groups_preg_or_lacint640.0%
ind_ages_vulnerabilities_groups_ppl_chronic_diseaseint640.0%
ind_ages_vulnerabilities_groups_elderly_at_riskint640.0%
ind_ages_vulnerabilities_groups_widowint640.0%
needs_priority_needsobject0.0%
nfi_items_blanketsobject0.0%
nfi_items_mattressobject0.0%
nfi_items_water_containerobject0.0%
nfi_items_ovenobject0.0%
nfi_items_fuelobject0.0%
nfi_items_kitchen_nfiobject0.0%
nfi_items_hygieneobject0.0%
nfi_items_feminineobject0.0%
nfi_items_other_nfi_itemsobject0.0%
nfi_items_other_specifyobject74.5%
wash_water_quantityint640.0%
wash_wash_sourceobject0.0%
wash_water_pointsint640.0%
wash_latrines_nonsegregatedint640.0%
wash_latrines_maleint640.0%
wash_latrines_femaleint640.0%
wash_shower_nonsegregatedint640.0%
wash_shower_maleint640.0%
wash_shower_femaleint640.0%
wash_wastebinsint640.0%
health_health_phcobject0.0%
health_health_femaleworkersobject14.5%
health_health_shcobject0.0%
health_health_services_pregobject0.0%
health_health_ambulanceobject0.0%
health_ilness_typeobject0.0%
food_security_food_accessobject0.0%
food_security_food_assistance_freqobject0.0%
food_security_food_assistance_typeobject5.5%
protection_securityobject0.0%
protection_departureobject0.0%
protection_temp_leaving_camp_medobject0.0%
protection_temp_leaving_camp_marktobject0.0%
protection_phones_allowedobject0.0%
protection_missing_docsobject0.0%
protection_uxoobject0.0%
protection_services_protobject24.2%
protection_host_tensionobject0.0%
protection_access_marketsobject0.0%
education_access_primary_schoolobject0.0%
education_access_secondary_schoolobject0.0%
education_boys_primaryobject13.9%
education_boys_secondaryobject23.6%
education_girls_primaryobject13.9%
education_girls_secondaryobject23.6%
education_reasons_not_schoolobject0.0%
education_non_formal_eduobject0.6%
education_non_formal_participationobject76.4%
who_what_partners_protectionobject0.0%
who_what_partners_gbvobject0.0%
who_what_partners_childprotobject0.0%
who_what_partners_educationobject0.0%
who_what_partners_shelterobject0.0%
who_what_partners_waterobject0.0%
who_what_partners_sanitationobject0.0%
who_what_partners_wasteobject0.0%
who_what_partners_nfiobject0.0%
who_what_partners_phcobject0.0%
who_what_partners_shcobject0.0%
who_what_partners_mhpssobject0.0%
who_what_partners_nutritionobject0.0%
who_what_partners_foodobject0.0%
who_what_partners_socialcohesionobject0.0%
who_what_partners_livelihoodobject0.0%
who_what_partners_communicationobject0.0%
esa_sourceobject0.0%
esa_processedobject0.0%

Numeric Summary

ColumnMinMaxMeanMedian
coordinates_latitude30.549137.198233.822333.2536
coordinates_longitude42.445347.758244.013143.8506
camp_mngmt_male_staff0.024.04.07273.0
camp_mngmt_female_staff0.010.00.81210.0
plots_occupied_plots2.05450.0508.5455220.0
plots_not_occupied_plots0.01639.0102.33337.0
extension_total_plots8.07000.0610.8545241.0
shelter_types_tent_cement0.05000.0320.933314.0
shelter_types_tent_ground0.07000.0197.16971.0
shelter_types_caravan0.01600.064.67270.0
shelter_types_improvised_shelter0.01600.016.57580.0
shelter_types_single_fam_unit0.01800.016.98790.0
shelter_types_collective-12.046.01.20610.0
shelter_types_rubhall0.0162.01.38790.0
shelter_types_openair0.020.00.19390.0

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

bibtex
@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.