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Received: 12 September 2025

Revised: 15 December 2025

Accepted: 30 December 2025

Published: 8 January 2026

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Article

Integrated Revealing GIS Models to Monitor, Understand

and Foresee the Spread of Diseases and Support

Emergency Response

Cristiano Pesaresi * and Davide Pavia

Department of Letters and Modern Cultures, Sapienza University of Rome, 00185 Rome, Italy;

davide.pavia@uniroma1.it

*Correspondence: cristiano.pesaresi@uniroma1.it

Abstract

The importance of GIS models to monitor the spread of infectious diseases and support

emergency response has been underlined by a large body of literature and strengthened by

the COVID-19 pandemic to identify possible solutions able to recognise spatio-temporal

clusters and patterns, evaluate the presence of acceleration factors and deﬁne speciﬁc

actions. In the ﬁeld of applied research on health geography and geography of safety, this

work brieﬂy displays the main aims of the project “Integrated revealing GIS models to

monitor, understand and foresee the spread of diseases and support emergency response”

and shows some illustrative applications. The basic assumption of the project is to test

revealing models regarding key objectives of social utility, and one of its main aims is

to elaborate GIS applications able to understand the spread of COVID-19, relating the

geocalisations of the cases with speciﬁc variables. In order to provide targeted evidence

able to better highlight local differences, a number of elaborations derived from (Arc)GIS

models and based on data regarding COVID-19 according to sex, age and healthcare

facilities in the Rome municipality (Italy) are presented and contextualised as examples,

also replicable for precision preparedness.

Keywords: COVID-19; health geography; geography of safety; GIS models; infectious

diseases; 3D models

1. Introduction and International Framing

The key role played by geographical approach and GIS applications for public health

research and policy has been recently underlined [

], particularly in different thematic

areas concerning modelling geographic heterogeneity in health behaviour and outcome;

measuring spatial accessibility for patients and potential crowdedness for facilities; area-

based and individualised neighbourhood effects; constructing geographic areas for health

data dissemination and analysis; delineating hospital service areas; and spatial optimisation

towards a balance in efﬁciency and equality.

Some years before this, the importance of spatial databases and the mapping of health

information had been evidenced [

], i.e., to analyse the space–time clustering of diseases,

support exposure modelling, evaluate the risk and spread of infectious diseases, calculate

shortest paths in the case of emergency, draw buffer zones to identify areas with different

levels of exposure, provide a decision support system (DSS) to locate health services, study

disparities through effective geovisualisation and measure area characteristics.

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During the COVID-19 pandemic, various literary reviews [

–

] have underlined how

GIS models can be produced, for example, to relate COVID-19 to socio-economic variables;

to study the potential inﬂuence of air and land pollution; to analyse COVID-19 diffusion on

the basis of population mobility or effectiveness of health services; and to provide disease,

exposure and web-based mapping. Other works [

] have shown how geotechnologies and

AI techniques can be useful to support data tracking, the detection of clusters, prediction of

areas which may be affected and the creation of an early warning and alert system.

At the beginning of the global emergency, a contribution regarding the geographical

tracking and mapping of SARS-CoV-2 focused attention on the notable added value that

can be provided by GIS to support the global ﬁght against outbreaks [

]. While the situa-

tion unfolded in all its criticality, the possibility to effectively geolocalise and geovisualise

the data on contagions and deaths was evidenced. A joint discussion about the possible

beneﬁts obtainable by using GIS elaborations and dashboards also took place, showing

a set of applications developed in the United States and planned in Italy [

]. Moreover,

other works have discussed the effectiveness of animated choropleth and proportional sym-

bol cartograms in order to avoid visual biases and make communicative epidemiological

dashboards [

]; analysed some design frameworks which provide promising perspectives

to characterise geotechnological applications and map-based storytelling; and strengthened

the state of the targeted knowledge by proposing new techniques and avenues of reﬁne-

ment [

] (p. 222). A number of geotechnological proposals to tackle health emergencies

were also advanced concerning [

] ArcGIS Pro models of spatial and temporal spread in

digital dot maps derived from geocoding; digital ﬂow maps—with Tracker for ArcGIS and

Tracking Analyst—which show the routes that paucisymptomatic COVID-19 cases could

have covered on foot after leaving their home; and smart surveys for the identiﬁcation of

possible COVID-19 positives based on a conﬁdential geolocalised online questionnaire to

be submitted with ArcGIS Survey123.

Different studies later explored the innovative and proﬁtable ways in which tech-

niques, methodological applications and geospatial–temporal analysis have been employed

to tackle the complexities of the COVID-19 emergency, looking into perspectives of re-

producible elaborations [

] (p. 193). Therefore, epidemic digital maps, which integrate

outbreak characteristics and visually show the data, have become a fundamental means for

institutions and the population to monitor and understand risk insights [16] (p. 142).

Additional examples have been provided by connecting retrospective data and im-

proved algorithms to support predictive hypotheses through a Geospatial AI and satellite-

based Earth observation cognitive system [

–

]. In these cases, data and algorithms have

provided territorial scenarios able to support a concrete geovisualisation of geographical–

probabilistic information. On the basis of the numerous applications developed during

the COVID-19 pandemic, the potential function of AI for science, in integrating and en-

hancing traditional methodologies, has been discussed, both considering multiple strength

points which can facilitate proactive responses to possible outbreaks and needs of ad hoc

reﬁnement and precaution [20].

A systematic review focused on space–time cluster detection techniques for

infectious diseases then evidenced that—with a notable acceleration during the

COVID-19 emergency—public health units have increasingly used geospatial tech-

nologies and approaches for disease surveillance and promoting place-based health

initiatives [

] (p. 1). Spatial analysis, based on watchful selection and evaluation of

data provenance and supported by rigorous investigation criteria, can thus provide no-

table progress in many aspects of interdisciplinary research about the COVID-19 pandemic

and other infectious diseases [22] (p. 29).

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In recent years the essential role of GIS mapping and analysis has been further

evidenced by the (United States) National Center for Chronic Disease Prevention and

Health Promotion (NCCDPHP) with particular attention to heart disease and stroke

surveillance, attesting to the multiplicity of the ﬁelds of application. Speciﬁc tools

for heart disease and stroke surveillance have been planned, for example an Interac-

tive Atlas (Division for Heart Disease and Stroke Prevention; https://www.cdc.gov/

heart-disease-stroke-atlas/about/index.html, accessed on 10 December 2025), in addi-

tion to other initiatives that promote the application of GIS for various chronic disease

topics (https://www.cdc.gov/chronic-disease/data-surveillance/index.html, accessed

on 10 December 2025).

In this framework, it has been afﬁrmed that the understanding of the preva-

lence and territorial differences of chronic disease is a fundamental challenge in or-

der to identify vulnerable areas, record any possible improvements and adopt ad hoc

measures [

] (p. 1). GIS mapping, geovisualisation and animated elaborations help in

recognising whether some chronic diseases disproportionately concern certain areas and

groups, so as to implement mitigation efforts in the zones mainly affected, to identify

possible relationships between prevalence and behaviours or pollutants, and to foresee

changes over time.

Various examples have been given to underline the added value of GIS mapping and

analysis in terms of public health research, practice and policy, with reference to mapping

the overlap of poverty level and prevalence of diagnosed chronic kidney disease [

];

ﬁnding optimal locations for implementing innovative hypertension management ap-

proaches [

]; analysing stroke mortality and stroke hospitalisations in the light of cultural

differences and similarities in geographic patterns [

]; identifying priority geographic

locations for diabetes self-management education and support services [

]; promoting a

behavioural risk factor surveillance system after having identiﬁed people who have never

been screened for (colorectal) cancer [

]; studying ethnic disparities in adult obesity to

support local action [29].

To test applied tools and solutions and record innovation elements of social utility, the

project “Integrated revealing GIS models to monitor, understand and foresee the spread of

diseases and support emergency response” (funded by Sapienza University of Rome, in the

category “Large research projects” 2024) has been devised by considering some main aims

both to monitor the spread of communicable diseases and support strategical measures

in the case of different emergencies, such as cardiac arrest. In particular, the attention in

this work is focused on GIS mapping and elaborations based on data regarding COVID-19

on the basis of sex, age and healthcare facilities (in the Rome municipality) according to a

replicable approach for other communicable diseases.

2. Objectives

From an interdisciplinary and applied point of view, the project presented here pursues

a number of aims, above all linked to the following.

The ﬁrst objective is to elaborate (Arc)GIS models able to understand the rapid spread

of COVID-19 in some study areas, relating the geocalisations of the cases with some socio-

demographic and (co)morbidity aspects and the presence of vulnerable facilities. The

involvement of similar variables in the models also helps us better understand territorial

differences recorded in terms of infection and lethality. Moreover, it makes it possible to

create a link between the past pandemic and possible future emergencies. On the basis

of speciﬁc sources, provided by Local Health Units (ASLs) and undergoing data cleaning

and quality, the data—geolocated on satellite imagery through accurate geocoding—are

studied in relation to the presence of some factors that can face the emergency (hospitals)

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and have an important position for medicine supply (pharmacies), or hold a delicate role

(nursing homes) and even accelerate the diffusion and represent particularly susceptible

places (elderly healthcare residences). This information would be mapped in detail based

on ofﬁcial sources such as the open data portals of the Ministry of Health, after being

recorded in geodatabases and overlaid as integrative layers. Moreover, the geolocated data

provided by ASLs can be analysed in relation to speciﬁc variables, for example regarding

population age structure and sex, and can also be studied considering behaviour-related

threats or indicators of deprivation, i.e., evaluated by census sections on the basis of the

quantitative and administrative data from the National Statistics Institute (ISTAT). In this

way it is possible to move from animation surveillance models which cartographically

represent the data to reveal GIS models which highlight territorial elements, relations,

possible weak points or strategic factors. Furthermore, it makes it possible to integrate the

dynamic space–time diffusion simulator in a GIS environment to analyse the COVID-19

spread in Rome in terms of new cases, deaths and different outcomes [

], and elaborate

replicable models for precision preparedness.

The second objective is to program a system of auto-implementation able to auto-

update and auto-upgrade on the basis of new data. The base programming language to

develop the above-mentioned system is Python (3.11.11), for its wide compatibility and

integration with ArcGIS products [

–

], which will become essential for ad hoc solutions

and the following spatial analysis. A similar system can be a decision support tool able

to monitor the evolution of any airborne infectious diseases. The data provided by the

ASLs would be recorded and organised in a single geodatabase in cloud computing, able

to join and automatically map the data in order to feed an effective real-time geotechno-

logical system. A test system would therefore be developed and be particularly useful

at the beginning of an epidemic (or a pandemic) to quickly identify areas potentially af-

fected by the spread of disease and in the subsequent phases to prepare speciﬁc measures

and actions, meeting and disclosing a series of needs which came to light during the

COVID-19 emergency.

3. Materials and Methods

The punctual mapping of COVID-19 is based on the data provided by the UOC

Hygiene and Public Health Service—Local Health Unit Rome 1 (Rome, Italy). These data

have been subject to a speciﬁc process of data cleaning and they have been represented

and displayed through geocoding and then aggregated, i.e., by sub-municipal areas on

satellite imagery.

Particularly, the geocoding was based on the domicile and residence address of the

people who were positive for COVID-19 according to the datasets received and optimised

for data quality.

In fact, many works have evidenced the importance of methods and techniques able

to improve the accuracy and the spatial representation of the data in the case of geocoding

and connected analysis [36–38].

Errors or duplications can derive from different situations: missing or incomplete data

concerning patients or their addresses; replication of records; cases of coincidence of names

common to different areas; wrong settings of the geocoder’s parameters (e.g., input address

ﬁelds; country or region of reference), etc.

In this way, after accurate data cleaning (Table 1), 3055 cases were geolocalised. If

compared with previous works [

], the current results of geocoding show slight differ-

ences since the geocoder used to perform the operation (i.e., “Esri World Geocoder”) has

increased its overall performance by the extension of its coverage and the update of the

toponyms connected to its features.

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Table 1. Table which summarises the data cleaning process of the COVID-19 cases, previously

described [30] (p. 88), using a four-step schema based on a synthesis of the criteria used.

1—

Management criteria

First control and merge of the two input tables with the COVID-19 cases, which were

provided by the UOC Hygiene and Public Health Service—Local Health Unit Rome 1 in

two time intervals (useful for a map production able to distinguish the patients according

to the two different periods): from 25 February to 11 June 2020; from 12 June to 26

September 2020.

2—

Temporal criteria

Search and elimination of the table cells with a null value in the ﬁeld “Date of insertion”, a

ﬁeld used to record the timestamp (DD/MM/YYYY) of the patient’s access to the different

healthcare structures which ascertained the cases.

3—

Spatial criteria

(1/2)

Search of the table cells with a null value in the ﬁeld called “Domicile”, considered the

most suitable for the geocoding process since the address where the patients theoretically

spent their isolation. When it was possible, the cells with a null value in the ﬁeld

“Domicile” were ﬁlled with the data concerning the residence (if it was in the study area, as

for the domicile), which theoretically represents the stable place of living of the patients.

4—

Spatial criteria

(2/2)

Revision of the attributes stored in the column “Domicile” (organised as previously edited),

to correct or complete all of the addresses—especially the ones that did not starts with the

words like “st.” and “sq.”—potentially misunderstandable by the locator used for the

geocoding process, that is to say the Esri’s “ArcGIS World Geocoding Service”.

In order to provide additional information able to predispose a harmonic system

useful both in ordinary and extraordinary situations, a series of layers have been organised

regarding hospitals, nursing homes and pharmacies in the municipality of Rome. The

open data have been obtained from the ofﬁcial source of the Italian Ministry of Health and

processed through geocoding function. These layers can also be overlaid and analysed

together with the ones concerning the spread of diseases and in this case with the maps

which show the distribution of COVID-19 cases in the period considered.

4. Results and Discussion

In order to provide some illustrative examples, targeted elaborations have been pro-

duced with ArcGIS Pro and they are presented and discussed here. Some examples

focused on the Rome municipality chosen as the study area are therefore shown regarding

the following:

The mapping of COVID-19 through geocoding processes, for the period from

25 February to 26 September 2020, with distinctions according to sex, and the digital

choropleth maps, derived from the previous elaborations and based on quantitative

data obtained from the aggregation of geocoding by sub-municipal areas (SCAs).

The mapping of COVID-19 through geocoding processes, for the same period, with

distinctions according to age, and particularly focused on people aged 65 or more

with a consequent speciﬁc subdivision into people aged 65–74, 75–84, and 85 or more.

The mapping of the healthcare facilities through geocoding processes based on the

recent ofﬁcial data, with reference to hospitals, nursing homes and pharmacies.

4.1. Mapping the COVID-19 Data According to Sex Through Punctual Geocoding and

Choropleth Maps

Figures 1and 2show a subdivision of 1648 males and 1407 females in order to provide

a distinction according to sex (cumulative data for the whole period between 25 February

and 26 September 2020). These elaborations provide detailed distributive data which are

functional for geographical screening and territorial diagnostic imaging.

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Figure 1. The male cases of COVID-19 (according to the geocoding process) on 26 September 2020

(cumulative data for the whole period between 25 February and 26 September 2020). Elaboration

by D. Pavia, C. Pesaresi and C. De Vito on data by UOC Hygiene and Public Health Service—Local

Health Unit Rome 1.

Figure 2. The female cases of COVID-19 (according to the geocoding process) on 26 September 2020

(cumulative data). Elaboration by D. Pavia, C. Pesaresi and C. De Vito on data by UOC Hygiene and

Public Health Service—Local Health Unit Rome 1.

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In fact, in a recent work [

] (p. 293), it was underlined that geocoding makes it

possible to effectively visualise the spread of the diseases throughout space and over time,

as well as the territorial distribution of resources and their proximity to the inhabitants,

providing notable inputs to promote public health guidelines and actions on the basis of

different needs.

From a distributive point of view, the digital choropleth maps in Figures 3and 4, which

derive from Figures 1and 2, underline some critical situations since they are the result

of the aggregation of cases by sub-municipal areas. Methodologically, three classes have

been built with the same values, taking the cue from the quantile method in an integrated

perspective between data related to male and female cases, and using two chromatic scales.

Figure 3, regarding males, shows high values recorded in the “centroid” (made by two

SCAs) and North-West areas, medium values of the horizontal East sector and one SCA in

the West sector, and low values of the South-West sector. A tripartition is evident according

to the following tendential distinction: SCAs in the North area and the “centroid” have

high values; SCAs in the Central zone have medium values, with heavy crowding in the

East sector; SCAs in the South zone have low values. Figure 4, regarding females, shows

high values recorded by the “centroid” and the same North-West area, medium values

in a more limited East sector and in the extreme South-West SCA, and low values in the

South-West sector and in the extreme East SCA. A slightly more interspersed situation

therefore appears.

Figure 3. The male cases of COVID-19 on 26 September 2020 aggregated per SCA (cumulative data).

Elaboration by D. Pavia, C. Pesaresi and C. De Vito on data by UOC Hygiene and Public Health

Service—Local Health Unit Rome 1.

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Figure 4. The female cases of COVID-19 on 26 September 2020 aggregated per SCA (cumulative data).

Elaboration by D. Pavia, C. Pesaresi and C. De Vito on data by UOC Hygiene and Public Health

Service—Local Health Unit Rome 1.

Concerning the diachronic evolution of the data, Figure 5shows the dynamics recorded

by male and female cases. In particular, it is evident that the highest number of cases, both

for males and females, was recorded in March, which shows the highest peaks. In fact, in

Italy the DPCM dated 9 March 2020 declared the status of lockdown and the restrictive

measures devised to contain the contagion were extended throughout the whole country

but the real effects required some weeks to become concrete. A certain time delay tends

to be recorded between the adoption of containment actions and the results, due to the

infections that have already occurred and are being spread. In April the cases started to

decrease, showing weeks with marked improvements and situations with some differences

between males and females. The lockdown continued until 18 May, according to Decree-

Law No. 33 dated 16 May 2020, and in June and July the number of cases had considerably

reduced. It was due to the “tail” effect of the lockdown and speciﬁc measures adopted

by a high number of people, like face masks, disinfectants and social distancing. The

numbers started to increase once again in August, which was perceived as the month

characterised by the desire to return to normality, travelling and hoping to have left behind

the emergency. Therefore in September a new wave was recorded with a high number of

cases, both for males and females, requiring a further phase of ad hoc actions. The size of

the data observed in September was similar to that of April but with the tangible difference

that in April the cases were (considerably) decreasing, whereas in September they were

symptomatic of a worsening situation.

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Figure 5. The temporal evolution of male and female cases of COVID-19 between 25 February and

26 September 2020. Elaboration by D. Pavia, C. Pesaresi and C. De Vito on data by UOC Hygiene and

Public Health Service—Local Health Unit Rome 1.

4.2. Mapping the COVID-19 Data According to Age Through Punctual Geocoding

In Figure 6the geocoding has been performed by extracting only people aged 65 or

more from the whole, since people become more fragile and could probably have comor-

bidities with the increase in age. The elaboration shows the results of punctual geocoding,

and cartographically shows 976 cases (32% of the total) who constitute the potentially more

vulnerable “slice” of the population. Figure 6is therefore focused on a group of people

who require particular attention because the outcomes could be characterised by severe

conditions or deaths.

Figure 6. The cases of COVID-19 (according to the geocoding process) on 26 September 2020 in

people aged 65 or more (cumulative data). Elaboration by D. Pavia, C. Pesaresi and C. De Vito on

data by UOC Hygiene and Public Health Service—Local Health Unit Rome 1.

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Figure 7, derived from Figure 6, shows a subdivision into three classes, 65–74 years,

75–84 and 85 or more, so as to provide a distinction considering a social–demographic

criterion to which some speciﬁc needs could be related. In fact, the lower level of positive

reaction to the illness can depend on older age and related co-morbidities. Therefore, the

punctual digital map reveals the distribution of the 976 cases of people aged 65 or more,

subdivided into 352 cases for people aged 65–74, 255 cases for people aged 75–84, and

369 cases for people aged 85 or more. The integration with the data contained in the medical

record can provide a series of useful information to spatially frame the areas characterised

by situations with the greatest severity.

Figure 7. The cases of COVID-19 (according to the geocoding process) on 26 September 2020 in

people aged 65–74, 75–84, 85 or more (cumulative data). Elaboration by D. Pavia, C. Pesaresi and C.

De Vito on data by UOC Hygiene and Public Health Service—Local Health Unit Rome 1.

Currently, the organisation of a similar multilevel geodatabase, able to make it possible

to overlay thematic layers and data aggregations, is supporting a cascade production of

digital maps for a relational geospatial analysis of different variables in order to dynamically

analyse and geovisualise the COVID-19 spread in Rome and with a view to possible

applications of territorial healthcare.

4.3. Mapping the Healthcare Facilities as Complementary Layers and from a Perspective

of Preparedness

Figure 8shows the geolocation of the hospitals recorded in the ofﬁcial dataset (re-

ferred to 2023) and evidences a concentration in the centre of the city and along an “align-

ment” directed to the West side. Moreover, some sub-municipal areas contain more than

one hospital, while some others have no inner structures and people must inevitably resort

to the facilities of other sub-municipal areas. This is for example the case of sub-municipal

areas III, VII and VIII. From a geographical point of view, the difference is also evident

between the centre and periphery, and only three hospitals are outside the Great Ring

Road and two of these are very nearby in the South-West sub-municipal area X, while the

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third one is present on the East side (sub-municipal area VI). The ﬁgure also highlights

the possibility to query the map and obtain the data from different hospitals in a pop-up

with some general information, which from a perspective of social utility and preparedness

would be integrated with other ad hoc data, for example, hospital wards, number of beds,

presence of paediatric emergency department, etc.

Figure 8. The hospitals geolocated (through the geocoding process) within the Rome municipality.

An exempliﬁcative pop-up provides some general information on the Policlinico Umberto I hospital.

Authors’ elaboration on data by Italian Ministry of Health.

Figure 9shows the geolocation of the nursing homes recorded in the ofﬁcial dataset

(referring to 2023) and a concentration in the centre of the city is evident. All the nursing

homes are present inside the Great Ring Road, with the exception of two in the South-

West sector, particularly in the sub-municipal area X. All the sub-municipal areas contain

almost one nursing home, but while some sub-municipal areas have several nearby nursing

homes, ﬁrst of all number II, other contexts have only one facility, and this is the case of

sub-municipal areas IV (in the East side) and IX (in the South zone), the latter having a

very large extension; in addition, sub-municipal area VI, at the East extremity, contains

two nursing homes, but they are very near each other and in the rest of the area there are

no nursing homes. In the Figure, a table frame shows the number of nursing homes per

sub-municipal area, providing a synthetic report.

Figure 10 shows the geolocation of the pharmacies recorded in the ofﬁcial dataset

(referring to 2025) and evidences a high number in the city centre and inside the Great Ring

Road, even though upon careful examination, supported by dynamic zoom in, some zones

seem empty, e.g., a wide area of sub-municipal VIII. The presence of pharmacies in the

territory plays a crucial social role because they provide medicine and some health services,

including (COVID-19 or other) swabs.

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Figure 9. The nursing homes (n. h.) geolocated (through the geocoding process) in the Rome

municipality. A table frame shows the number of nursing homes per sub-municipal area (SCA).

Authors’ elaboration on data by Italian Ministry of Health.

Figure 10. The pharmacies geolocated (through the geocoding process) in the Rome municipality. An

inset map, with a red frame, provides a zoom on an area (in the West side) where there are many

buildings (and residents) and few pharmacies; another inset map, with a blue frame, on the contrary

provides a zoom on an area (in the centre of Rome) where there are many buildings (and residents)

and many pharmacies. Authors’ elaboration on data by Italian Ministry of Health.

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As recently afﬁrmed with reference to the COVID-19 pandemic, uninterrupted supply

of essential medicine and resilient pharmaceutical systems are the backbone of healthcare

systems and key elements in the control of dangerous diseases able to strain emergency

departments, hospitals and related structures [40] (p. 1), [41] (p. 1).

Using satellite imagery (Figure 10), a certain correspondence tendentially appears

between built-up areas and pharmacies; that is to say that where there are many buildings

(and residents), there is also a notable number of pharmacies. Nevertheless in some cases

this relation is not satisﬁed and inset maps which provide a speciﬁc zoom can highlight

critical situations with potential difﬁculties, ampliﬁed in the case of extraordinary situations.

In terms of data concerning the territorial distribution of the pharmacies, a general

framework is provided by Table 2, which shows the high numbers recorded by the central

sub-municipal area I (316 pharmacies), followed by sub-municipal areas VII (241) and V

(200), in the East sector. On the other hand, Table 2shows the low values recorded by the

vast sub-municipal area XV (80 pharmacies), in the North sector, followed by sub-municipal

areas XI (90) and XIII (96), in the West sector. These data can be useful to have a comparative

measure based on territorial distribution and as a reference to consider possible planning

actions able to increase the number of pharmacies in areas which require implementations.

Table 2. Number of pharmacies (15/06/2025) per sub-municipal area (SCA). Authors’ elaboration on

data by Italian Ministry of Health.

SCA Pharmacies SCA Pharmacies

I 316 IX 102

II 179 X 125

III 146 XI 90

IV 119 XII 117

V 200 XIII 96

VI 113 XIV 121

VII 241 XV 80

VIII 110 Total 2155

5. Conclusions

This research was conducted to shed some light on the progress made in various

directions of social utility, enhancing the possibility of making interdisciplinary approaches

and GIS applications a proﬁtable union for effective preparedness. The work moves

towards perspectives of organisation of a multilevel system able to provide a wide number

of harmonic layers for surveillance, predictive hypotheses, and socio-sanitary resilience.

Therefore, some demonstrative GIS elaborations that help us understand the spread

of communicable diseases in local contexts have been shown, here taking COVID-19 as

a signiﬁcant example. In particular, these targeted elaborations regard the mapping of

COVID-19 through geocoding processes (on data provided by the UOC Hygiene and

Public Health Service—Local Health Unit Rome 1), for the period between 25 February

and 26 September 2020, with distinctions according to sex, and the digital choropleth maps

based on the data derived from the aggregation per sub-municipal area of the results of

geocoding; the mapping of COVID-19 through geocoding processes (on data provided by

the UOC Hygiene and Public Health Service—Local Health Unit Rome 1), for the same

period, with distinctions according to age, and focused on people aged 65 or more with

a further subdivision in people aged 65–74, 75–84, 85 or more; and the mapping of the

healthcare facilities through geocoding processes (on the recent open data from the Ministry

of Health), with reference to hospitals, nursing homes and pharmacies.

https://doi.org/10.3390/ijgi15010032

ISPRS Int. J. Geo-Inf. 2026,15, 32 14 of 16

The GIS integration between evolutive and distributive dimensions makes it possible

to follow the spread of COVID-19 in detail and in its complexity. The resulting elaborations

make it possible to show how a health emergency could be tackled, moving from retrospec-

tive data to real-time data that can be progressively updated and implemented. The prior

organisation of multiple layers based on the geolocalisation of different types of facilities,

with the possibility of overlaying these with data on possible future infections, creates the

foundations of a dynamic territorial response system.

That is a multiple answer to common needs, since the increasing availability of

spatial data and GIS functions, supported by thorough methodologies, provides a

concrete opportunity to advance knowledge, promptly respond to new public health

emergencies [

] (p. 41) and build aware community resilience [

] (p. 5). We must

count on rigorous planning and ad hoc tools for the re-thinking of territories and imple-

mentational strategies [

] (pp. 15–16), also taking inspiration from the lessons of the

COVID-19 pandemic [

], which—in spite of its gravity—has paradoxically provided a

fruitful chance to test integrated GIS models able to support emergency management and

coordinate speciﬁc actions.

Author Contributions: Conceptualisation, Cristiano Pesaresi; methodology, Cristiano Pesaresi;

software, Cristiano Pesaresi and Davide Pavia; validation, Cristiano Pesaresi and Davide Pavia;

formal analysis, Cristiano Pesaresi and Davide Pavia; investigation, Cristiano Pesaresi and Davide

Pavia; resources, Cristiano Pesaresi and Davide Pavia; data curation, Cristiano Pesaresi and Davide

Pavia; writing—original draft preparation, Cristiano Pesaresi and Davide Pavia; writing—review

and editing, Cristiano Pesaresi and Davide Pavia; visualisation, Cristiano Pesaresi and Davide

Pavia; supervision, Cristiano Pesaresi; project administration, Cristiano Pesaresi; funding acquisition,

Cristiano Pesaresi All authors have read and agreed to the published version of the manuscript.

Funding: The project ‘Integrated revealing GIS models to monitor, understand and foresee the spread

of diseases and support emergency response’ was funded by Sapienza University of Rome, in the

category ‘Large research projects’ 2024, protocol number RG124190D55D4B34.

Data Availability Statement: Data regarding COVID-19 used for the elaborations have been provided

by UOC Hygiene and Public Health Service—Local Health Unit Rome 1 (Rome), thanks to Vito

Cerabona and Enrico Di Rosa. Data regarding healthcare facilities have been obtained by Italian

Ministry of Health at the following links (accessed on 10 December 2025): https://www.dati.salute.

gov.it/it/dataset/farmacie/; https://www.salute.gov.it/portale/documentazione/p6_2_8_1_1.jsp?

lingua=italiano&id=13.

Acknowledgments: This paper was devised together by the authors, but particularly Cristiano

Pesaresi wrote Sections 1,2,4,4.1 and 5. Davide Pavia wrote Sections 3,4.2 and 4.3. The authors

thank Corrado De Vito for his support and suggestions and for the contacts provided.

Conﬂicts of Interest: The authors declare no conﬂicts of interest.

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