Urban Indicators

Urban indicators are is an attempt to assess and compare cities performance. Each city has it’s own set of indicators. The methods used to calculate these indicators can be found in the city class below.

class City(city_data: pandas.core.series.Series, national_data: dict)

Projected population

from projected_census()


Estimated current population

\[pop_{c} = pop_{projected} - {pop}_{leavees} + {pop}_{idps}\]

From iom_displacement() we have:


agriculture_recovery() → float

Max peak before the crisis Last peak since the last year of the crisis We are looking at NVDI values of 0.6 to 1 What is the % increase in the max NVDI value before the conflict and the last year max NVDI value

damage_assessment() → float

How many buildings are damaged per 1000 people that were projected to have lived in the city?

\[\frac{n_{building} * 1000_{people} }{pop_{projected}}\]

Total Damaged Buildings

\[n_{building} = {n}_{{building}_{moderate}} + {n}_{{building}_{severe}} + {n}_{{building}_{destroyed}}\]

from damage_assessment() we have:


food_basket() → float

What is the % increase in the food basket price since the start of the conflict?

\[\frac{fb_{city_{2019}} - fb_{national_{2014}}}{fb_{national_{2014}}} * 100\]

wfp_prices() provides us with:


health() → float

% of facilities that are functioning

nightlight_output() → float

What is the % change of average brightness from the year before the war began, compared to the average brightness in last year?

\[\frac{n_{{brightness}_{pre\, war}} - n_{{brightness}_{last\, year}}}{n_{{brightness}_{pre\, war}}} * 100\]

nightlight() provides us with a monthly brightness value. We take the yearly sum of brightness per city.

\[\sum_{i=0}^{12\, months} n_{{brightness}_{2014_{2 + i}}} = n_{{brightness}_{pre\, war}}\]

The sum of average monthly brightness values in the city the year before the conflict began.

\[\sum_{i=0}^{12\, months} n_{{brightness}_{this\, year_{last\, month - i}}} = n_{{brightness}_{post\, war}}\]

The sum of average monthly brightness values in the city for the past year.

percentage_ipds() → float

What % of the population is composed of IDPs?

\[\frac{pop_{idps}}{pop_{projected}} * 100\]

The aggregated data we use for this calculation can be found in projected_census() and in iom_displacement()


What percentage of the schools in the district are damaged?

\[\frac{{n}_{{schools}_{functional}}}{{n}_{{schools}_{functional}} + {n}_{{schools}_{non\, functional}}}\]

From schools() we have:


wages() → int

wfp_prices() provides us with: