The evolution of spatial analysis and modeling in decision processes

Beniamino Murgante
The evolution of spatial analysis and modeling in
decision processes
School of Engineering, University of Basilicata
beniamino.murgante@unibas.it
http://oldwww.unibas.it/utenti/murgante/Benny.html
https://www.researchgate.net/profile/Beniamino_Murgante

The evolution of spatial analysis and modeling in decision
processes - Beniamino Murgante

Executive Order 12906
Coordinating Geographic Data
Acquisition and Access: The National
Spatial Data Infrastructure (NSDI)

Web Mapping Service (WMS)
Web Feature Service (WFS)
Web Coverage Service (WCS)

Open Data Classification proposed by Tim Berners-Lee

• Volunteered Geographic
Information (VGI)
• Citizens as Voluntary Sensors
Goodchild, 2007

Matera and OpenStreetMap

Potenza and OpenStreetMap
07/01/2015 30/05/2015
Scorza F., Las Casas G., Murgante B., Amato F., (2019) "City Dashboards for Urban Planning"
Journal of Urban Technology

Journal of Urban Technology

http://resultmaps.neis-one.org/osm-typhoon-haiyan-2013-
contributors
http://www.theguardian.com/technology/2013/nov/15/online-volunteers-map-philippines-
after-typhoon-haiyan

http://faculty.washington.edu/kstarbi/TtT_Hurricane_Map_byEvent.html

http://http://www.cs.colorado.edu/~starbird/TtT_Irene_map_byEvent.html

Saganeiti L., Amato, F.; Nolè, G.; Murgante, B., “VGI and crisis mapping in an emergency situation.
Comparison of four case studies: Haiti, Kibera, Kathmandu, Centre Italy” Geomedia Volume 3 pp 26-
29, 2017

Google maps Amatrice OSM Amatrice
Saganeiti L., Amato, F.; Nolè, G.; Murgante, B., “VGI and crisis mapping in an emergency situation.
Comparison of four case studies: Haiti, Kibera, Kathmandu, Centre Italy” Geomedia Volume 3 pp 26-
29, 2017

Nuvola di punti
POST sisma
Dataset LiDAR DSM e DTM
ACQUISIZIONE DATI
POST
29 Agosto 2016
Rilievo Lidar
PCFVG
PRE
2008
Rilievo LiDAR
MATTM
Classificazione
punti
.las/.laz
Importazione
punti
.xyz
Nuvola di punti
RASTER GRID
Nuvola di punti
RASTER GRID
DSM DTMDSM
DSM POST sisma

Saganeiti L., Amato, F.; Nolè, G.; Potleca M., Vona M.; Murgante, B., “Change Detection and
Classification of Seismic Damage with LiDAR and RADAR Surveys in Supporting Emergency
Planning. The Case of Amatrice” Lecture Notes in Computer Science Volume 10407 pp 722-
731, Springer Verlag, Berlin, DOI: 10.1007/978-3-319-62401-3_53

DSMPRE – DSMPOST
Saganeiti L., Amato, F.; Nolè, G.; Potleca M., Vona M.; Murgante,
B., “Change Detection and Classification of Seismic Damage
with LiDAR and RADAR Surveys in Supporting Emergency
Planning. The Case of Amatrice” Lecture Notes in Computer
Science Volume 10407 pp 722-731, Springer Verlag, Berlin, DOI:
10.1007/978-3-319-62401-3_53

Edifici realizzati dopo il 2008Edifici interessati da crolli
DSMPRE – DSMPOST
Saganeiti L., Amato, F.; Nolè, G.; Potleca M., Vona M.; Murgante, B., “Change Detection and
Classification of Seismic Damage with LiDAR and RADAR Surveys in Supporting Emergency
Planning. The Case of Amatrice” Lecture Notes in Computer Science Volume 10407 pp 722-
731, Springer Verlag, Berlin, DOI: 10.1007/978-3-319-62401-3_53

CLASSIFICATION OF SEISMIC DAMAGES 4 e 5 CLASSES EMS98
Classe 5
Δh≥1,50 m
Classe 4
0,40<Δh<1,50 m
Inf. classe 4
Δh≤0,40 m

Amato F., Botonico G., Graziadei A., Sassano G., Murgante B., Scorza F. ”Innovation,
technologies, Participation” International Journal of Electronic Governance 11(1):1 2019
DOI:10.1504/IJEG.2019.10018374

Walk, Count, Map, Draw, look for traces,
Photograph, annotate on a diary
DOI:10.1504/IJEG.2019.10018374

1. WALKABILITY
2. GREEN AREAS
3. ACTIVITIES AND USE OF SPACE
DOI:10.1504/IJEG.2019.10018374

WALKABILITY: walkability is a measure of how friendly an area is to walking
pedestrian network: sidewalks, porticoes, footpaths, flight of steps, road
crossings, discontinuity and not accessibility, Maintenance and conditions,
interruptions of the sidewalks

Amato F., Botonico G., Graziadei A., Sassano G., Murgante B., Scorza F. ”Innovation, technologies,
Participation” International Journal of Electronic Governance 11(1):1 2019

“Data is the new oil. It’s valuable, but if unrefined it cannot
really be used. It has to be changed into gas, plastic,
chemicals, etc to create a valuable entity that drives
profitable activity; so must data be broken down, analyzed
for it to have value”
Clive Humby 2006
"the paradox of the great civilization change consists in the
fact that we have practically unlimited access to information
and data and yet we are nearly unable to use it in any way".
Manuel Castels 2009
Models are the new gold

Dan Ariely
Big data: What is it?

The term “Big data” on scopus (in title, keywords, abstract)
Summer 2019
65230 documents (great part form USA and China)

It is vital to use theory as a guide to experimental design for
maximal efficiency of data collection and to produce reliable
predictive models and conceptual knowledge.
Rather than continuing to fund, pursue and promote ‘blind’ big
data projects with massive budgets, it could be better to
elucidate the multiscale and stochastic processes controlling
the behaviour of complex systems.
The field of big data is important but its impact is diminished
without big theory.
Coveney et al 2016

Tobler's First Law of Geography “All things are related, but nearby things are more
related than distant things” (1970)
åå
åå
==
==
= n
j
ij
n
i
n
j
ijij
n
i
w
wc
SAC
11
11
Where:
•n is the number of objects;
•i and j are two objects;
•xi is the value of object i attribute;
•cij is a degree of similarity of attributes i and j;
•wij is a degree of similarity of location i and j;
(Goodchild, 1986; Lee and Wong, 2001)
Autocorrelation

Geary C Ratio (1954)
2
__
2
)()(2
))()(1(
xxw
xxwN
c
iiijji
jiijji
-SSS
-SS-
=if cij=(xi−xj)2
Moran index (1948)
2
__
____
)()(
))((
xxw
xxxxwN
I
iiijji
jiijji
-SSS
--SS
=
if )()(
____
xxxxc jiij --=
Autocorrelation

Local Spatial
Autocorrelation
Statistics
å=
×=
N
j
jijii zwzI
1
Local Indicator
of Spatial
Association
(Anselin, 1995)
G function by
Getis and Ord
(1992)
( ) ( )( )å=
-
-
=
N
j
jij
X
i
i XXw
S
XX
I
1
2
Zi =
Xi − X( )
SX
Local version
of the Geary
Ratio

Clusters localization
with the Getis and
Ord function
Murgante B. Danese M. (2011)
"Urban versus Rural: the
decrease of agricultural
areas and the development of
urban zones analyzed with
spatial statistics"
International Journal of
Agricultural and Environmental
Information Systems
(IJAEIS) volume 2(2) pp. 16–
28 IGI Global, ISSN 1947-
3192,
DOI:10.4018/jaeis.201107010
2

Clusters localization
with the Getis and
Ord function

G function by Getis and Ord
Nolè, G., Lasaponara, R., Lanorte A., Murgante, B., (2014) " Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using
Multi-Temporal Satellite Data" International Journal of Agricultural and Environmental Information Systems, 5(2), 20-38, April-June
2014 IGI Global DOI:10.4018/IJAEIS.2014040102

Local Geary

Local Indicator of Spatial Association

Nolè, G., Lasaponara, R., Lanorte A., Murgante, B.,
(2014) " Quantifying Urban Sprawl with Spatial
Autocorrelation Techniques using Multi-
Temporal Satellite Data" International Journal
of Agricultural and Environmental Information
Systems, 5(2), 20-38, April-June 2014 IGI Global
DOI:10.4018/IJAEIS.2014040102

Lanorte, A., Danese M., Lasaponara R., Murgante B. (2011) "Multiscale mapping of burn area and severity using
multisensor satellite data and spatial autocorrelation analysis" International Journal of Applied Earth
Observation and Geoinformation

Luxor city 416 km2
Population 506,588

Elfadaly, A., Attia, W., Qelichi, M.M., Murgante B., Lasaponara R. (2018) “Management of
Cultural Heritage Sites Using Remote Sensing Indices and Spatial Analysis
Techniques”Surveys in Geophysics, Volume 39, Issue 6, pp 1347–1377.

Amato, F., Pontrandolfi, P., Murgante, B., 2015, Supporting planning activities with the
assessment and the prediction of urban sprawl using spatio-temporal analysis, Ecological
Informatics, Volume 30, November 2015, Pages 365–378 doi:10.1016/j.ecoinf.2015.07.004

Amato, F., Pontrandolfi, P., Murgante, B., 2015, Supporting planning activities with the
assessment and the prediction of urban sprawl using spatio-temporal analysis, Ecological
Informatics, Volume 30, November 2015, Pages 365–378 doi:10.1016/j.ecoinf.2015.07.004
Quantity of
built-up
areas [ha]
measured at
different
times.

Amato F., Martellozzo F., Nolè G., Murgante B. (2016) “Preserving cultural heritage by
supporting landscape planning with quantitative predictions of soil consumption”
Journal of Cultural Heritage Elsevier doi:10.1016/j.culher.2015.12.009.

Results
Calibration
Coefficients
Growth simulation
cycles
Initial
patterns
Land Use/Land
Cover change
model,
SLEUTH

Amato F., Martellozzo F.,
Nolè G., Murgante B. (2016)
“Preserving cultural
heritage by supporting
landscape planning with
quantitative predictions of
soil consumption” Journal of
Cultural Heritage Elsevier
doi:10.1016/j.culher.2015.12.
009.

Amato, F.; Maimone, B.A.; Martellozzo, F.;
Nolè, G.; Murgante, B. (2016) The Effects
of Urban Policies on the Development of
Urban Areas. Sustainability 2016, 8, 297.
doi:10.3390/su8040297

Amato, F.; Maimone, B.A.; Martellozzo, F.; Nolè, G.; Murgante, B. (2016) The Effects
of Urban Policies on the Development of Urban Areas. Sustainability 2016, 8,
297. doi:10.3390/su8040297

Amato, F.; Maimone, B.A.;
Martellozzo, F.; Nolè, G.;
Murgante, B. (2016) The
Effects of Urban Policies
on the Development of
Urban
Areas. Sustainability 2016,
8, 297.
doi:10.3390/su8040297

Journal of Urban Technology DOI 10.1080/10630732.2019.1614895

Amato, F.; Martellozzo, F.; Murgante B.; Clarke, K. (2018) Balancing urban expansion against
agriculture and natural land loss: Scenario-based Modelling of urbanization and ecological losses in
Italy to 2030 Applied Geography 91, pp. 156-167 DOI: 10.1016/j.apgeog.2017.12.004

Amato, F.; Martellozzo, F.; Murgante B.; Clarke, K. (2018) Balancing urban expansion against
agriculture and natural land loss: Scenario-based Modelling of urbanization and ecological losses in
Italy to 2030 Applied Geography 91, pp. 156-167 DOI: 10.1016/j.apgeog.2017.12.004
The Number of Total Transactions (NTT).
The Housing Market Index (HMI), which consists in the ration of NTT with the
stock of residential unit existing in a specific territorial unit.
Code of Cultural
and Landscape
Heritage
The Natura 2000
Project
Housing Market
Index
Areas within a 300m buffer from the coastal line (CBCPa).
Areas within a 300m buffer from the coastal line of lakes (CBCPb).
Water streams, river zones and areas within a buffer of 150m from these (CBCPc).
Glaciers and perennial snow areas (CBCPd).
National and regional reserves and parks (CBCPe).
Forests and woodlands (CBCPf).
Wetlands (CBCPg).
Sites of Community Importance (SCIs) and Special Areas of Conservation (SACs).
SACs are usually included within SCIs.
Special Protection Areas (SPAs). In Italy, these areas cover about 19% of inland
and 4% of marine areas.
Important Bird Areas (IBA)
𝑆"#$ = ∑'
(
𝑊' ∗ 𝑉'
𝑆"#$ is the value assumed by each pixels of the synthesis map
𝑊' is the weight of ith criteria
𝑉' is the value of the map representing the ith criteria at any given location

Amato, F.; Martellozzo, F.; Murgante B.;
Clarke, K. (2018) Balancing urban
expansion against agriculture and
natural land loss: Scenario-based
Modelling of urbanization and ecological
losses in Italy to 2030 Applied
Geography 91, pp. 156-167 DOI:
10.1016/j.apgeog.2017.12.004
Scenario
conservation
Scenario
BAU/diffusion
CBCPa 0,112 0,085
CBCPb 0,112 0,085
CBCPc 0,112 0,085
CBCPd 0,112 0,085
CBCPe 0,112 0,085
CBCPf 0,112 0,085
CBCPg 0,112 0,085
SCIs 0,047 0,033
SPAs 0,047 0,033
IBA 0,031 0,022
HMI 0,02 0,32
Criteria’s weights resulting from the
AHP and used for the scenario
conservation and for the scenario
BAU/diffusion.

Amato, F.; Martellozzo, F.; Murgante
B.; Clarke, K. (2018) Balancing urban
expansion against agriculture and
natural land loss: Scenario-based
Modelling of urbanization and
ecological losses in Italy to
2030 Applied Geography 91, pp. 156-
167 DOI: 10.1016/j.apgeog.2017.12.004

Amato, F.; Martellozzo, F.; Murgante B.; Clarke, K. (2018) Balancing urban expansion against agriculture
and natural land loss: Scenario-based Modelling of urbanization and ecological losses in Italy to
2030 Applied Geography 91, pp. 156-167 DOI: 10.1016/j.apgeog.2017.12.004

Amato, F.; Martellozzo, F.; Murgante B.; Clarke, K. (2018) Balancing urban expansion against agriculture and
natural land loss: Scenario-based Modelling of urbanization and ecological losses in Italy to 2030 Applied
Geography 91, pp. 156-167 DOI: 10.1016/j.apgeog.2017.12.004
LUCC dynamics between 2012 and 2030
according to the Diffusion scenario
LUCC dynamics between 2012 and 2030
according to the Conservation scenario

Cosentino, C.; Amato, F.; Murgante, B. Population-Based Simulation of Urban Growth: The Italian Case
Study. Sustainability 2018, 10, 4838.
FUTure Urban-Regional Environment Simulation

Cosentino, C.; Amato, F.; Murgante, B. Population-Based Simulation of Urban Growth: The Italian Case
Study. Sustainability 2018, 10, 4838.

Urban sprawl vs Urban sprinkling
Urban sprawl - Europe Urban sprinkling – Italy (Policoro)
Urban sprawl: “the spreading of urban developments (such as houses and
shopping centers) on undeveloped land near a city”
Urban sprinkling: “a small quantity distributed in drops or
scattered particles”

Building density
and population
density
Year Population (No.) Residential Buildings [BR] (n) Dp (Inhabitants/ha) Db (BR/ha)
1950 627,586 117,687 0.63 0.12
1989 610,186 238,603 0.61 0.24
1998 597,468 269,019 0.60 0.27
2006 591,338 285,072 0.59 0.28
2013 578,391 297,810 0.58 0.30

2 Methods:
- xi and yi are the coordinates of the centroid of each
polygon of urbanized areas falling in each cell of 1 km2 of
the grid in Method 1, and in each municipality in Method
2, respectively;
- x* and y* are the coordinates of the centroid of the
greater nucleus. In Method 1, the greater nucleus of
each cell at each temporal instant is considered. In
Method 2, the greater nucleus of each municipality at
time t0 (i.e., the first temporal instant considered) is
considered.
- R is the radius of the circular area of similar size to
that of the sum of urbanized areas present, for each cell
(Method 1) and for each municipality (Method 2).
SPX = +∞SPX = 0
NOT
FRAGMENTED
HIGHLY
FRAGMENTED
SPX index
- 1x1 Km square grid
- Administrative level

Fragmentation Degree SPX 1
Not fragmented SPX = 0
Low fragmentation 0 < SPX < 50
Medium-low fragmentation 50 ≤ SPX < 100
Medium fragmentation 100 ≤ SPX < 150
Medium-high
fragmentation
150 ≤ SPX < 200
High fragmentation SPX ≥ 200
SPX index - Method 1

Fragmentation Degree SPX 1
Low fragmentation SPX < 1
Medium-low fragmentation 1 ≤ SPX < 3.5
Medium fragmentation 3.5 ≤ SPX < 6
Medium-high fragmentation 6 ≤ SPX < 8.5
High fragmentation SPX ≥ 8.5
1 All SPX index values were divided by 1000.
SPX index - Method 2

Residential buildings in NATURA 2000 sites
EUAP: national park of Pollino_Basilicata region (Italy)
Site Denomination
Bio-geographic
region
Updating
Surface
[ha]
SCI-SAC SPA Municipality
N° of
residential
buildings [2013]
C
Gravine di
Matera
Mediterranea 2012 6968,49 ZSC ZPS
Matera -
Montescaglioso
843
B Serra di Calvello Mediterranea 2012 1641,35 ZSC
Calvello -
Marsico Nuovo
17
C
Valle del Tuorno
- Bosco Luceto
Mediterranea 2010 75,35 ZSC ZPS
Savoia di
Lucania
0
B
Abetina di
Laurenzana
Mediterranea 2012 324,39 ZSC Laurenzana 1

Residential buildings in hydro-geological risk areas
Years
Residential
buildings
(total)
(n)
Residential
buildings in R3
(n)
Residential
buildings in R4
(n)
Residential
buildings in
R3+R4
(n)
Residential
buildings in
R3+R4
(%)
2006 285.072 10.816 13.079 23.895 8,38
2013 297.810 11.012 13.266 24.278 8,15 (+1,60%)
R3: HIGH RISK
dangers for the safety of persons, functional
damage to buildings and infrastructures and
significant damage to the environmental heritage
R4: VERY HIGH RISK
loss of life and serious injury to people, serious
damage to buildings, infrastructure and
environmental heritage, destruction of socio-
economic activities.

1. Before 2015
2. between 2015 and 2017
3. After 2017
1. Micro-mini plants (small size) up to 200
kW
2. Medium-sized plants between 200 kW and
1 MW
3. Large plants with an installed power
greater than 1MW, radius of influence.
Classification of wind power plants:
Periods of wind power plants installation

SPX index
- xi and yi are the coordinates of the centroid (Ci) of
each polygon of urbanized areas falling in each cell of 1
km2 of the grid.
- x* and y* are the coordinates of the centroid of the
greater nucleus (C*). The greater nucleus of each cell at
each temporal instant is considered.
- R is the radius of the circular area of similar size to
that of the sum of urbanized areas present, for each cell.
SPX = +∞SPX = 0
NOT
FRAGMENTED
HIGHLY
FRAGMENTED
1km
C*
C1
C2
C3
C4
C5
C6
C7C8

Fragmentation Degree SPX
Not urbanized Null
Not fragmented SPX = 0
Low fragmentation 0 < SPX < 50
Medium-low fragmentation 50 ≤ SPX < 100
Medium fragmentation 100 ≤ SPX < 150
Medium-high fragmentation 150 ≤ SPX < 200
High fragmentation SPX ≥ 200

Layer
Original
format
Influence radius
[m]
Aggregation distance
[m]
LB
LBr Polygon 10 50
LBi Polygon 25 50
LRES
LRESw Point 15 – 25 - 35 250
LRESf Polygon 25 -
LHw Polygon - -

The study area (about 300 km²) includes five municipalities in the province of Potenza: Cancellara,
Pietragalla, San Chirico Nuovo, Tolve e Vaglio Basilicata.
Tipologia
Potenza
generatore (kW)
Micro-mini eolico P < 200
Medio eolico 200 ≤ P < 1000
Grande eolico P ≥ 1000
Comune di Cancellara
(PZ)

Applicazione del modello “Habitat Quality”
Land cover map at 2013 Land cover map at 2018

Applicazione del modello “Habitat Quality”
THREATS
If 𝑆,- = 0 then 𝐷0, it is not a function of the threat r-th and therefore it will be: 𝐷0, = 0
!
𝑖-02 = 1 −
567
58 9:6
if linear (I)
𝑖-02 = 𝑒𝑥𝑝 −
>.@@
58 9:6
𝑑02 if esponential (II)
2. Relative distance
3. Level of legal/institutional
/social /physical protection
𝑫 𝒙𝒋 = ∑ 𝒓F𝟏
𝑹 ∑𝒋F𝟏
𝒀 𝒓 𝒘 𝒓
∑ 𝒓K𝟏
𝑹 𝒘 𝒓
𝒓 𝒚 𝒊 𝒓𝒙𝒚 𝜷 𝒙 𝑺𝒋𝒓 (III)
4. Relative sensitivity (𝐒𝐣𝐫)
1. Relative impact
𝑖-02 = 𝐼𝑚𝑝𝑎𝑐𝑡 𝑜𝑓 𝑡ℎ𝑟𝑒𝑎𝑡 𝑟 𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑡𝑒𝑑 𝑖𝑛 𝑦 𝑤𝑖𝑡ℎ 𝑒𝑓𝑓𝑒𝑐𝑡𝑠 𝑖𝑛 𝑥 𝑐𝑒𝑙𝑙
𝑑02 = 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑥 𝑎𝑛𝑑 𝑦 𝑐𝑒𝑙𝑙𝑠
d𝑟𝑚𝑎𝑥= Maximum distance where the 𝑡ℎ𝑟𝑒𝑎𝑡 𝑟 can have effects
𝐷0, = Total level of 𝑡ℎ𝑟𝑒𝑎𝑡 at the cell x due to the 𝑗𝑡ℎ LULC
𝑤 𝑟 = 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑡ℎ𝑟𝑒𝑎𝑡 𝑟
ry= 𝑇ℎ𝑟𝑒𝑎𝑡 𝑟 𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑡𝑒𝑑 𝑖𝑛 𝑦
𝛽0 = 𝐴𝑐𝑐𝑒𝑠𝑠𝑖𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑒𝑙𝑙 𝑥𝑡ℎ
𝑆,- = Sensitivity of 𝑗𝑡ℎ LULC at 𝑡ℎ𝑟𝑒𝑎𝑡 𝑟

Habitat Quality Map
𝑸 𝒙𝒋 = 𝑯𝒋 𝟏 −
𝑫 𝒙𝒋
𝒛
𝑫 𝒙𝒋
𝒛
+ 𝒌 𝒛
Results of "Habitat Quality" model
𝑫 𝒙𝒋 = †
𝒓F𝟏
𝑹
†
𝒋F𝟏
𝒀 𝒓
𝒘 𝒓
∑ 𝒓F𝟏
𝑹
𝒘 𝒓
𝒓 𝒚 𝒊 𝒓𝒙𝒚 𝜷 𝒙 𝑺𝒋𝒓
Habitat Degradation Map
(IV)
(III)
𝑄0, = Habitat Quality at the cell x belonging to 𝑗𝑡ℎ LULC
𝐷0, = Total level of 𝑡ℎ𝑟𝑒𝑎𝑡 at the cell x due to the 𝑗𝑡ℎ LULC
𝐻, = Habitat suitability of 𝑗𝑡ℎ LULC
(IV) 𝑆𝑐𝑎𝑙𝑒 𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟s
z = 2,5
K= 5 → 𝟏 −
𝑫 𝒙𝒋
𝒛
𝑫 𝒙𝒋
𝒛
“ 𝒌 𝒛 = 0,5 when 𝑫 𝒙𝒋 = 5
K= 3 → 𝟏 −
𝑫 𝒙𝒋
𝒛
𝑫 𝒙𝒋
𝒛
“ 𝒌 𝒛
= 0,5 when 𝑫 𝒙𝒋 = 3

Habitat Quality Map - 2013 Habitat Quality Map - 2018

Habitat Degradation Map - 2013 Habitat Degradation Map - 2018

0 50 100 150 200 250 300
BASSA
MODERATA
ELEVATA
MOLTO ELEVATA
Superficie (ha)
Qualità
Habitat Quality (2013-2018)
2018
2013
Legenda
QUALITÁ
SUPERFICIE (𝑘𝑚>)
Legenda
2013
2018
In the output map the areas
characterized by low quality
values increased from 2013 to
2018.
Definition of Habitat
Quality classes
Discussion of “Habitat Quality" model results

HABITAT QUALITY DIFFERENCE 2013-2018

Definition of Habitat
Degradation classes
Discussion of “Habitat Degradation " model results
DEGRADO
SUPERFICIE (𝑘𝑚>)
In the output map the
areas characterized by
moderate and very high
degradation values
increased from 2013 to
2018.

DIFFERENZA HABITAT DEGRADATION 2013-2018

Pilogallo, A., Saganeiti, Pontradolfi P. Scorza F., Murgante B., Comparing territorial performances of
Renewable Energy Sources' plants with integrated Ecosystem Services losses assessment: Melfi
municipality sample area in Basilicata region (Italy). Sustainable Cities and Society (Accepted)

Pilogallo, A., Saganeiti,
Pontradolfi P. Scorza F., Murgante
B., Comparing territorial
performances of Renewable
Energy Sources' plants with
integrated Ecosystem Services
losses assessment: Melfi
municipality sample area in
Basilicata region (Italy).
Sustainable Cities and Society
(Accepted)

Pair-wise matrixes for
each scenario and
resulting weights
summaring table
biodiversity
carbon
storage
agricultural
vocation
Pilogallo, A., Saganeiti, Pontradolfi P. Scorza F., Murgante B., Comparing territorial performances of
Renewable Energy Sources' plants with integrated Ecosystem Services losses assessment: Melfi
municipality sample area in Basilicata region (Italy). Sustainable Cities and Society (Accepted)

Beniamino Murgante
School of Engineering, University of Basilicata
beniamino.murgante@unibas.it
http://oldwww.unibas.it/utenti/murgante/Benny.html
https://www.researchgate.net/profile/Beniamino_Murgante
Thank you for your
attention

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