Premise crowdsourced surveys

Premise crowdsourced surveys are a way of validating, verifying the ground truth and understanding the needs of people on ground, especially in regions impacted by a crisis

community_needs = pd.read_csv("../../data/premise/Community_Needs.csv")
displacement = pd.read_csv("../../data/premise/Displacement.csv")
recent_earthquake = pd.read_csv("../../data/premise/Recent_Earthquake_in_Turkey.csv")
fuel_prices = pd.read_csv("../../data/premise/Premise  Turkey Fuel Prices Update.csv")
household_needs = pd.read_csv("../../data/premise/Premise  Household Needs TR.csv")

Price of a litre of petrol

The survey provides data and evidence in the form of photos to capture the price of one litre of petrol and diesel in the areas where the earthquake impact is the highest

fig, ax = plt.subplots(figsize=(10, 6), sharex=True)

turkey_adm2.boundary.plot(ax=ax, edgecolor="#D3D3D3", linewidth=0.5)
gdf.plot(column=" what_is_the_price_of_a_liter_of_diesel", ax=ax, legend=True)
# gdf.sjoin(turkey_adm2).plot(column=' what_is_the_price_of_a_liter_of_petrol', ax=ax, legend = True)

<Axes: >

Price of a litre of diesel

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fig, ax = plt.subplots(figsize=(10, 6))
turkey_adm2.boundary.plot(ax=ax, color="#d3d3d3")
turkey_adm2.sjoin(gdf).plot(column="Price of Diesel", ax=ax, legend=True)

ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)

ax.set_xticks([])
ax.set_yticks([])


plt.grid(False)

Comparing the price of petrol and diesel

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import seaborn as sns

fig, ax = plt.subplots(figsize=(6, 6))
# gdf[['Price of Diesel', 'Price of Petrol']].boxplot()

sns.set(style="whitegrid")
ax = sns.boxplot(x="Type", y="Price", data=fuel, showfliers=False)
ax = sns.swarmplot(x="Type", y="Price", data=fuel, color=".25")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.show()

Household Needs Survey from Premise

## Picking the relevant columns
household_columns = [
    " is_the_primary_health_care_facility_closest_to_you_currently_functional",
    " is_the_closest_hospital_to_you_currently_functional",
    "observation_lat",
    "observation_lon",
    " is_the_university_reference_hospital_near_you_functional",
    " do_you_have_access_to_functioning_food_markets",
    " in_the_past_six_months_has_the_amount_of_food_in_your_household_decreased_due_to_a_shortage",
]

Exploring the results in the admin region of Sahinbey

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fig, ax = plt.subplots(figsize=(10, 6))

turkey_adm2[turkey_adm2["adm2_en"] == "SAHINBEY"].boundary.plot(
    ax=ax, edgecolor="#D3D3D3", linewidth=0.5
)
# gdf.sjoin(turkey_adm2[turkey_adm2['adm2_en']=='SAHINBEY']).plot(ax=ax, column=' is_the_primary_health_care_facility_closest_to_you_currently_functional', legend=True)
gdf.sjoin(turkey_adm2[turkey_adm2["adm2_en"] == "SAHINBEY"]).plot(
    ax=ax, column=" is_the_closest_hospital_to_you_currently_functional", legend=True
)


ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)

ax.set_xticks([])
ax.set_yticks([])


plt.grid(False)

Analysing Community needs, Displacement and Recent earthquake needs surveys

columns_of_interest_community = [
    "in_your_neighborhood_is_lack_of_income_money_or_resources_to_survive_a_severe_problem",
    "in_your_neighborhood_is_lack_of_access_to_safe_drinking_or_cooking_water_a_severe_problem",
    "in_your_neighborhood_is_lack_of_access_to_functional_hygienic_bathrooms_a_severe_problem",
    "in_your_neighborhood_is_lack_of_access_to_personal_hygiene_products_and_services_a_severe_problem",
    "in_your_neighborhood_is_there_a_severe_problem_related_to_nutrition",
    "what_are_the_principal_concerns_related_to_nutrition_in_your_neighborhood",
    "have_there_been_significant_changes_in_the_total_quantity_of_food_available_to_people_in_your_neighborhood",
    "in_your_neighborhood_is_lack_of_access_to_medical_attention_a_severe_problem",
    "which_of_the_following_health_services_are_easily_accessible_in_your_local_area",
    "what_are_the_primary_issues_faced_by_children_in_your_neighborhood_in_relation_to_access_to_education",
]

columns_displacement = [
    "are_you_originally_from_the_area_you_are_located_in_now",
    "how_likely_are_you_to_return_to_your_place_of_origin_in_the_next_1_month",
    "do_you_live_in_a_refugee_or_idp_camp?",
    "how_long_have_you_been_displaced_for?",
    "to_your_knowledge,_does_your_community_have_a_positive_view_of_the_idps_and_refugees_residing_in_your_area?",
    "in_the_past_week_has_there_been_an_increase_or_decease_in_the_number_of_refugee_or_idps_present_in_your_local_area",
]

columns_recent_earthquake = [
    "did_you_observe_any_damage_to_your_home_during_the_recent_earthquake",
    "did_you_notice_any_damage_to_key_infrastructure_or_buildings_in_your_community_because_of_the_recent_earthquake",
    "was_your_local_school_house_damaged_or_destroyed_in_the_earthquake",
    "did_you_observe_any_damage_to_roads_as_a_result_of_the_earthquake",
    "are_there_currently_supplies_or_utilities_you_need_that_you_cannot_currently_access_as_a_result_of_the_earthquake",
    "what_supplies_or_utilities_do_you_need_that_you_cannot_currently_access_as_a_result_of_the_earthquake",
]

Visualizing number of observations from each admin region in the community needs dataset

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from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, ax = plt.subplots(figsize=(12, 10))
# gdf.plot(column = 'mean_of_me', ax=ax, alpha =0.4)
gdf.plot(column="observation_id", ax=ax, cmap="Oranges", alpha=0.7)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)

ax.set_xticks([])
ax.set_yticks([])

sm = plt.cm.ScalarMappable(
    cmap="Oranges",
    norm=plt.Normalize(
        vmin=gdf["observation_id"].min(), vmax=gdf["observation_id"].max()
    ),
)


divider = make_axes_locatable(ax)
dvider_kwargs = dict(position="left", size="1.5%", pad=0)
fig.colorbar(
    sm,
    cax=divider.append_axes(**dvider_kwargs),
    # format = matplotlib.ticker.FuncFormatter(lambda x, pos: ''),
    # ticks = matplotlib.ticker.FixedLocator([])
)

ax.set_title("Recent Earthquake Observation Counts")

# plt.savefig('../images/recent_earthquake_observations.png')

Text(0.5, 1.0, 'Recent Earthquake Observation Counts')

displacement["idp_color"] = displacement["idp_color"].str.replace("_", " ")

Change in Displcement of IDPs/refugees

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fig, ax = plt.subplots(figsize=(10, 6))

turkey_adm2.boundary.plot(ax=ax, edgecolor="#D3D3D3", linewidth=0.5)
displacement.plot(column="idp_color", ax=ax, legend=True, cmap="Spectral")

ax.set_xticks([])
ax.set_yticks([])
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)

# ax.legend(loc='upper right')
ax.set_title("Change in Displacement of IDP/Refugees")
plt.show()
# plt.legend()

Food availability

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fig, ax = plt.subplots(figsize=(10, 6))

turkey_adm2.boundary.plot(ax=ax, edgecolor="#D3D3D3", linewidth=0.5)
food_availability.plot(column="food_color", ax=ax, legend=True, cmap="viridis")

ax.set_xticks([])
ax.set_yticks([])
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)

ax.set_title("Change in Food Availability")

# leg = ax.legend()
# leg.get_frame().set_alpha(0)

Text(0.5, 1.0, 'Change in Food Availability')

Damage to key infrstructure of buildings

Interestingly, it was reported that there were damaged buildings even outside the earthquake impacted regions

fig, ax = plt.subplots(figsize=(10, 6))

turkey_adm2.boundary.plot(ax=ax, edgecolor="#D3D3D3", linewidth=0.5)
convert_to_gdf(recent_earthquake).plot(
    column="did_you_notice_any_damage_to_key_infrastructure_or_buildings_in_your_community_because_of_the_recent_earthquake",
    ax=ax,
    legend=True,
)

ax.set_xticks([])
ax.set_yticks([])
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)

ax.set_title("Damage to key infrastructure or buildings")

Text(0.5, 1.0, 'Damage to key infrastructure or buildings')