Transport & migration Big Data A GIS-based approach for measuring migrants access to public transport in European cities

Transcription

1 EUROPEAN COMMISSION DIRECTORATE-GENERAL JOINT RESEARCH CENTRE D4I - Data Challenge on Integration of Migrants in Cities Final Workshop Yorgos N. Photis Professor, National Technical University of Athens Alex Bartzokas-Tsiompras PhD Candidate, National Technical University of Athens Transport & migration Big Data A GIS-based approach for measuring migrants access to public transport in European cities B r u s s e l s, N o v e m b e r 2 7,

2 1. Introduction Migration & Europe Migration is found to be the single biggest issue among EU citizens in half of the EU countries (CoR, 2018) Migration has had an important impact on demographic changes in Europe, & a clear urban dimension The strongest driver affecting migration to the EU is the presence of migrant communities (Migali et al, 2018) Cities can benefit from migration, however they must also face significant challenges (housing, public services) Infographic Source: The challenges of migration vary significantly between EU cities, even within cities, & their neighborhoods CoR, 2018, Reflecting on Europe: How Europe is perceived by people in regions and cities, European Committee of the Regions, Brussels Migali, S., Natale, F., Tintori, G., Kalantaryan, S., Grubanov-Boskovic, S., Scipioni, M., Farinosi, F., Cattaneo, C., Benandi, B., Follador, M., Bidoglio, G., McMahon, S. and Barbas, T., 2018, International Migration Drivers, Luxembourg

3 1. Introduction Access to Public Transport What is (geographical) access to Public Transport (P.T)? It refers to the location where a person lives, which can prevent him or not from accessing frequent transport services Levels of accessibility interact differently with distinct groups of people, & individuals What factors affect P.T accessibility? Transport demand Mobility (speed & distance) Land use density & mix Transport network connectivity The quality of transport options User information Integration of transport system links & modes Affordability Mobility substitutes Transport management Why access to Public Transport matters? People allocate nearly half of their spending to housing, & transportation P.T is where different people come together & participate in the activities that foster social development & cohesion P.T encourages independent city living, & supports access to jobs, housing, education, healthcare, culture, public services, social networks P.T is reflects social policies, & prevents social exclusion P.T promotes urban sustainability, & livability, & decreases cardependent lifestyles

4 1. Introduction Migration & Public Transport Migrants are highly dependent on local transit services Integration policies generate additional demand for transport in the city Many European professionals believe that the existing P.T infrastructure in Europe can cope with the increased, due to mass migration, level of demand (ULI, 2017) However, we still do not know how the P.T networks in European cities respond to the migration challenge Most of the existing data, & studies present evidence mainly at the national level, & thus it is difficult to recognize urban transport planning as a critical part of migration policies The D4i dataset enable us develop the first top-down approach to benchmark migrants access to P.T in a large number of European cities

5 1. Introduction Research Questions 1. Which European cities offer to migrants the best and the worst access to P.T? 2. How access to P.T varies between the native-born and foreign-born populations? Table 1. Basic statistics about the % of population Average Max Min Larger Cities Medium-sized Cities Native-born 86,2% 96,4% 73,7% Foreign-born 13,8% 26,3% 3,6% Native-born 85,9% 97,2% 72,8% Foreign-born 14,1% 27,2% 2,8% 26 Larger European cities (more than inh. based on high density boundary) 32 Medium-sized European cities (less than inh. based on high density boundary)

6 2. Methodology Big Data 1: G.T.F.S Transit Dataset/Global Format High Density City Boundaries (Source: Eurostat) OpenStreetMap Street Network Dataset Geospatial Analysis (GIS) 06:00 20:00 METRO/RAIL 833 m. 417 m. BUS/TRAM Poelman, H. & Dijkstra, L., 2015, Measuring access to public transport in European cities, DG Regio, Brussels VERY HIGH HIGH ACCESS TO PUBLIC TRANSPORT Big Data 2: D4I Dataset country Continent EU/Not EU L1 L3 L4 LARGER CITIES Statistical Analysis Final Results MAPS visualization Data Quality Issues MEDIUM MEDIUM-SIZED CITIES CHARTS LOW NO L1 L3 L4 country Continent EU/Not EU No Access Low Medium High Very High There is NO accessible P.T stop/station There is a P.T stop/station with less than 4 departures/hour There is a P.T stop/station with 4-10 departures/hour There is a P.T stop/station with more than 10 departures per hour There are both a bus/tram stop AND a metro/train station with more than 10 departures/hour

7 3.1 RESULTS - Migrants Access to Public Transport in Larger European Cities 100,0 Host-country nationals have statistically significant (p=.028) higher shares of population with low/no access than Not-EU27 or EU27 migrants 80,0 % of Native-born, EU, Not-EU population Nativeborn 60,0 40,0 EU27 20,0 0,0 Not-EU27 GENOVA GENOVA MADRID MADRID BARCELONA DUSSELDORF SEVILLA SEVILLA LYON LYON TORINO TORINO BILBAO BILBAO STUTTGART STUTTGART PARIS PARIS BERLIN BERLIN HAMBURG HAMBURG MALAGA MALAGA ROMA ROMA HANNOVER LILLE LILLE MARSEILLE MARSEILLE TOULOUSE TOULOUSE RUHRGEBIET COLOGNE COLOGNE HAGUE HAGUE PALERMO PALERMO BORDEAUX BORDEAUX AMSTERDAM ROTTERDAM MANCHESTER 83% 18% 68% 71% 38% 43% 8 45% 53% 55% 68% 65% 47% 44% 24% 28% 15% GENOVA MADRID BARCELONA DUSSELDORF SEVILLA LYON 19% TORINO BILBAO STUTTGART PARIS BERLIN HAMBURG MALAGA ROMA HANNOVER 5% LILLE MARSEILLE 19% TOULOUSE RUHRGEBIET 9% COLOGNE 6% HAGUE PALERMO BORDEAUX AMSTERDAM 18% ROTTERDAM MANCHESTER 23% 26% 26% 72% 15% 27% 6 37% 36% 46% 53% 47% 69% 59% 72% 55% 57% 53% 43% 37% 36% 37% 28% % 23% 25% GENOVA MADRID BARCELONA DUSSELDORF SEVILLA LYON TORINO BILBAO STUTTGART PARIS BERLIN HAMBURG MALAGA ROMA HANNOVER 5% LILLE MARSEILLE TOULOUSE RUHRGEBIET 14% COLOGNE 8% HAGUE PALERMO BORDEAUX AMSTERDAM ROTTERDAM MANCHESTER 8 19% 41% 66% 48% 53% 4 47% 46% 49% 78% 49% 72% 49% 4 44% 36% 31% 32% 28% 22% 21% 24% 18% 21% 5% 1 7% i) Native-born ii) Not-EU27 migrants iii) EU27 migrants Madrid, Genova, Barcelona Manchester, Rotterdam, Amsterdam Genova, Madrid, Torino Manchester, Bordeaux, Cologne Genova, Madrid, Barcelona Manchester, Malaga, Roma Madrid Rome Manchester Typologies of access: No Access Low Medium High Very High

8 Americans Europeans Africans Asians Oceanians 3.1 RESULTS - Migrants Access to Public Transport in Larger European Cities i) Africans ii) Asians iii) Americans iv) Europeans v) Oceanians GENOVA GENOVA MADRID MADRID BARCELONA BARCELONA DUSSELDORF DUSSELDORF SEVILLA SEVILLA LYON LYON TORINO TORINO BILBAO BILBAO STUTTGART STUTTGART PARIS PARIS BERLIN BERLIN HAMBURG HAMBURG MALAGA MALAGA ROMA ROMA HANNOVER HANNOVER LILLE LILLE MARSEILLE MARSEILLE TOULOUSE TOULOUSE RUHRGEBIET RUHRGEBIET COLOGNE COLOGNE HAGUE HAGUE PALERMO PALERMO BORDEAUX BORDEAUX AMSTERDAM AMSTERDAM ROTTERDAM ROTTERDAM MANCHESTER 8 64% 46% 41% 38% MADRID BARCELONA DUSSELDORF 28% GENOVA 17% 18% BILBAO STUTTGART PARIS BERLIN HAMBURG 59% 43% 29% 4 26% 9% 38% 32% SEVILLA LYON TORINO MALAGA ROMA HANNOVER 7% LILLE MARSEILLE TOULOUSE RUHRGEBIET 2 COLOGNE HAGUE PALERMO BORDEAUX AMSTERDAM ROTTERDAM MANCHESTER 83% 49% GENOVA MADRID BARCELONA DUSSELDORF 28% 32% 34% 25% SEVILLA LYON TORINO BILBAO STUTTGART PARIS BERLIN HAMBURG MALAGA 59% 36% 41% 29% 26% 7% 82% 31% 3 71% ROMA HANNOVER 6% LILLE MARSEILLE TOULOUSE RUHRGEBIET 13% COLOGNE HAGUE PALERMO BORDEAUX AMSTERDAM ROTTERDAM MANCHESTER 69% 23% 3 46% 63% 49% 72% 47% 44% 24% GENOVA MADRID BARCELONA DUSSELDORF 36% 31% SEVILLA LYON TORINO BILBAO STUTTGART PARIS BERLIN HAMBURG MALAGA ROMA HANNOVER 7% LILLE MARSEILLE TOULOUSE RUHRGEBIET 18% COLOGNE HAGUE PALERMO BORDEAUX AMSTERDAM ROTTERDAM 29% 22% 9% MANCHESTER 78% 45% 36% 37% GENOVA MADRID BARCELONA DUSSELDORF 38% 21% 4 31% SEVILLA LYON TORINO BILBAO STUTTGART PARIS BERLIN HAMBURG 28% MALAGA ROMA HANNOVER 5% LILLE MARSEILLE TOULOUSE RUHRGEBIET 11% COLOGNE 32% 22% 25% 18% 22% HAGUE 7% PALERMO BORDEAUX AMSTERDAM ROTTERDAM MANCHESTER 2 93% 93% 53% 87% 66% 63% 94% 92% 65% 24% 29% 12% 43% 7% 32% 29% No Access Low Medium High Very High 100,0 50,0 0,0

9 3.1 RESULTS - Migrants Access to Public Transport in Larger European Cities but what more the actual numbers tell us about access to P.T in European cities? Top 5 Cities with the highest number of population with low/no access in P.T Top 5 Cities with the highest number of population with medium access in P.T Top 5 Cities with the highest number of population with high/very high access in P.T AFRICANS AMERICANS ASIANS EUROPEANS OCEANIANS NATIVE-BORN PARIS ROTTERDAM MANCHESTER RUHRGEBIET MANCHESTER 575 RUHRGEBIET MANCHESTER BARCELONA RUHRGEBIET PARIS AMSTERDAM 178 PARIS ROTTERDAM AMSTERDAM PARIS COLOGNE ROTTERDAM 78 MANCHESTER AMSTERDAM HAGUE COLOGNE BERLIN HAGUE 73 ROMA MARSEILLE PARIS AMSTERDAM HAMBURG BARCELONA 31 BERLIN AFRICANS AMERICANS ASIANS EUROPEANS OCEANIANS NATIVE-BORN PARIS BARCELONA RUHRGEBIET RUHRGEBIET MANCHESTER 497 RUHRGEBIET MANCHESTER ROTTERDAM MANCHESTER PARIS AMSTERDAM 147 PARIS LILLE HAGUE COLOGNE COLOGNE HAGUE 124 MANCHESTER LYON AMSTERDAM PARIS BERLIN ROTTERDAM 92 ROMA RUHRGEBIET PARIS HAMBURG HAMBURG ROMA 22 BERLIN AFRICANS AMERICANS ASIANS EUROPEANS OCEANIANS NATIVE-BORN PARIS MADRID PARIS PARIS MANCHESTER PARIS AMSTERDAM BARCELONA BERLIN BERLIN AMSTERDAM RUHRGEBIET LYON AMSTERDAM RUHRGEBIET RUHRGEBIET BERLIN 966 MADRID MARSEILLE PARIS MANCHESTER MADRID HAGUE 813 BERLIN MADRID ROTTERDAM HAMBURG HAMBURG BARCELONA 685 BARCELONA

10 3.1 RESULTS - Migrants Access to Public Transport in Larger European Cities Average population mix (entropy) & P.T access typologies 1 0,9 0,8 0,7 Lyon, FR 10,1% 0,6 0,5 0,4 0,3 0,2 Roma, IT 6% 0,1 0 Paris, FR 5,2% No Access Low Medium High Very High Mean Entropy Global Moran s I (entropy) PT ACCESS: Hot Spot ENTROPY: Hot Spot PT ACCESS: Cold Spot ENTROPY: Hot Spot

11 3.2 RESULTS - Migrants Access to Public Transport in Medium-sized European Cities 100,0 80,0 60,0 40,0 20,0 0,0 % of Native-born, EU, Not-EU population Nativeborn EU27 RENNES RENNES HEIDELBERG HEIDELBERG NUREMBERG NUREMBERG BREST BREST SAN SEBASTIAN SAN SEBASTIAN FLORENCE FLORENCE BOLOGNA BOLOGNA GRENOBLE GRENOBLE PAMPLONA PAMPLONA MONCHENGLADBACH AACHEN AACHEN ANGERS ANGERS STRASBOURG STRASBOURG MALLORCA MALLORCA KARLSRUHE KARLSRUHE SAINT ETIENNE SAINT ETIENNE UTRECHT UTRECHT MONTPELLIER MONTPELLIER WUPPERTAL WUPPERTAL LIVORNO LIVORNO BRAUNSCHWEIG NANCY NANCY PRATO PRATO GRONINGEN GRONINGEN CAGLIARI CAGLIARI NICE NICE BONN BONN MANNHEIM MANNHEIM OLDENBURG OLDENBURG KREFELD KREFELD PISA PISA EINDHOVEN EINDHOVEN Not-EU27 57% % 88% 82% 81% 81% 59% 78% 72% 72% 67% 68% 64% 67% 47% 47% 49% 48% 38% MONCHENGLADBACH 31% RENNES HEIDELBERG NUREMBERG BREST SAN SEBASTIAN FLORENCE BOLOGNA GRENOBLE PAMPLONA AACHEN ANGERS STRASBOURG 2 MALLORCA KARLSRUHE SAINT ETIENNE UTRECHT MONTPELLIER WUPPERTAL LIVORNO BRAUNSCHWEIG NANCY PRATO GRONINGEN CAGLIARI NICE BONN MANNHEIM OLDENBURG KREFELD 1 PISA EINDHOVEN 37% 92% 93% 92% 87% 87% 58% 9 86% 82% 82% 81% 78% 78% 71% 68% 44% 59% HEIDELBERG NUREMBERG SAN SEBASTIAN FLORENCE BOLOGNA GRENOBLE PAMPLONA MONCHENGLADBACH AACHEN ANGERS STRASBOURG MALLORCA KARLSRUHE SAINT ETIENNE UTRECHT MONTPELLIER WUPPERTAL LIVORNO BRAUNSCHWEIG GRONINGEN CAGLIARI MANNHEIM OLDENBURG 19% KREFELD EINDHOVEN 29% 31% RENNES BREST NANCY i) Native-born ii) Not-EU27 migrants iii) EU27 migrants Rennes, Florence, San Sebastian Rennes, Florence, Nuremberg Rennes, Florence, Grenoble Eindhoven, Pisa, Krefeld Eindhoven, Pisa, Groningen Mannheim, Pisa, Eindhoven PRATO NICE BONN PISA 9 86% 88% 88% 88% 86% 8 82% 69% 72% 68% 68% 57% 43% 53% 44% 36% 17% 14% Typologies of access: No Access Low Medium High Very High Florence Saint-Etienne Mannheim Eindhoven

12 8 97% 95% 48% 17% 88% 99% % 9 23% 14% EINDHOVEN PISA KREFELD OLDENBURG MANNHEIM NICE CAGLIARI GRONINGEN PRATO NANCY BRAUNSCHWEIG BONN LIVORNO WUPPERTAL MONTPELLIER UTRECHT SAINT ETIENNE KARLSRUHE MALLORCA STRASBOURG ANGERS AACHEN MONCHENGLADBACH PAMPLONA GRENOBLE BOLOGNA FLORENCE SAN SEBASTIAN BREST NUREMBERG HEIDELBERG RENNES 36% 53% 43% 83% 57% 64% 86% 69% 88% 87% 86% 86% 38% 15% 17% EINDHOVEN PISA KREFELD OLDENBURG MANNHEIM NICE CAGLIARI GRONINGEN PRATO NANCY BRAUNSCHWEIG BONN LIVORNO WUPPERTAL MONTPELLIER UTRECHT SAINT ETIENNE KARLSRUHE MALLORCA STRASBOURG ANGERS AACHEN MONCHENGLADBACH PAMPLONA GRENOBLE BOLOGNA FLORENCE SAN SEBASTIAN BREST NUREMBERG HEIDELBERG RENNES 31% 64% 46% 83% 83% 48% 72% % 81% 57% 88% 94% 92% 94% 93% 86% 55% 21% 3 41% EINDHOVEN PISA KREFELD OLDENBURG MANNHEIM NICE CAGLIARI GRONINGEN PRATO NANCY BRAUNSCHWEIG BONN LIVORNO WUPPERTAL MONTPELLIER UTRECHT SAINT ETIENNE KARLSRUHE MALLORCA STRASBOURG ANGERS AACHEN MONCHENGLADBACH PAMPLONA GRENOBLE BOLOGNA FLORENCE SAN SEBASTIAN BREST NUREMBERG HEIDELBERG RENNES 31% 44% 94% 6 81% 67% 83% 78% 8 82% 92% 92% 93% 78% 37% 19% 11% 44% EINDHOVEN PISA KREFELD OLDENBURG MANNHEIM NICE CAGLIARI GRONINGEN PRATO NANCY BRAUNSCHWEIG BONN LIVORNO WUPPERTAL MONTPELLIER UTRECHT SAINT ETIENNE KARLSRUHE MALLORCA STRASBOURG ANGERS AACHEN MONCHENGLADBACH PAMPLONA GRENOBLE BOLOGNA FLORENCE SAN SEBASTIAN BREST NUREMBERG HEIDELBERG RENNES 3 46% 41% 83% 82% 92% 87% 58% 86% 8 92% 87% 93% 37% 87% 24% 3 59% EINDHOVEN PISA KREFELD OLDENBURG MANNHEIM NICE CAGLIARI GRONINGEN PRATO NANCY BRAUNSCHWEIG BONN LIVORNO WUPPERTAL MONTPELLIER UTRECHT SAINT ETIENNE KARLSRUHE MALLORCA STRASBOURG ANGERS AACHEN MONCHENGLADBACH PAMPLONA GRENOBLE BOLOGNA FLORENCE SAN SEBASTIAN BREST NUREMBERG HEIDELBERG RENNES 0,0 50,0 100,0 EINDHOVEN PISA KREFELD OLDENBURG MANNHEIM NICE CAGLIARI GRONINGEN PRATO NANCY BRAUNSCHWEIG BONN LIVORNO WUPPERTAL MONTPELLIER UTRECHT SAINT ETIENNE KARLSRUHE MALLORCA STRASBOURG ANGERS AACHEN MONCHENGLADBACH PAMPLONA GRENOBLE BOLOGNA FLORENCE SAN SEBASTIAN BREST NUREMBERG HEIDELBERG RENNES Africans Asians Americans Europeans Oceanians No Access Low Medium Very High High i) Africans ii) Asians iii) Americans iv) Europeans v) Oceanians 3.2 RESULTS - Migrants Access to Public Transport in Medium-sized European Cities

13 3.2 RESULTS - Migrants Access to Public Transport in Medium-Sized European Cities Top 5 Cities with the highest number of population with low/no access in P.T Top 5 Cities with the highest number of population with medium access in P.T Top 5 Cities with the highest number of population with high/very high access in P.T but what the actual numbers tell us about access to P.T in European cities? AFRICANS AMERICANS ASIANS EUROPEANS OCEANIANS NATIVE-BORN MONTPELLIER EINDHOVEN EINDHOVEN MANNHEIM EINDHOVEN 96 EINDHOVEN EINDHOVEN MALLORCA MANNHEIM BONN GRONINGEN 67 NICE NICE PAMPLONA BONN WUPPERTAL BONN 24 BONN SAINT ETIENNE 919 GRONINGEN 549 WUPPERTAL EINDHOVEN HEIDELBERG 10 MANNHEIM STRASBOURG 776 UTRECHT 346 PRATO KREFELD UTRECHT 8 WUPPERTAL AFRICANS AMERICANS ASIANS EUROPEANS OCEANIANS NATIVE-BORN EINDHOVEN MALLORCA BONN WUPPERTAL EINDHOVEN 73 WUPPERTAL BONN PAMPLONA EINDHOVEN BONN UTRECHT 29 BONN NICE EINDHOVEN WUPPERTAL MANNHEIM SAN SEBASTIAN 25 EINDHOVEN MONTPELLIER GRONINGEN PRATO NUREMBERG GRONINGEN 17 GRONINGEN STRASBOURG BONN 697 KREFELD KREFELD MALLORCA 5 PRATO AFRICANS AMERICANS ASIANS EUROPEANS OCEANIANS NATIVE-BORN NICE MALLORCA NUREMBERG NUREMBERG UTRECHT 273 BOLOGNA MONTPELLIER PAMPLONA MANNHEIM WUPPERTAL SAN SEBASTIAN 225 FLORENCE UTRECHT FLORENCE BONN MANNHEIM EINDHOVEN 83 NUREMBERG STRASBOURG SAN SEBASTIAN WUPPERTAL BONN GRONINGEN 77 NICE GRENOBLE UTRECHT BOLOGNA KARLSRUHE BONN 72 STRASBOURG

14 3.2 RESULTS - Migrants Access to Public Transport in Medium-Sized European Cities Average population mix (entropy) & P.T access typologies 1,25 1 Saint-Etienne, FR 15,2% 0,75 0,5 Pamplona, ES 10,8% 0,25 0 Prato, IT 8,4% No Access Low Medium High Very High Mean Entropy Global Moran s I (entropy) PT ACCESS: Hot Spot ENTROPY: Hot Spot PT ACCESS: Cold Spot ENTROPY: Hot Spot

15 4. Conclusions EU policy relevance What we found? Overall, the P.T infrastructure in European cities provides high access levels to the foreign-born population The host-country nationals, as well as the EU27, & European migrants achieve worse access to P.T than all other migrant groups, particularly in larger cities In some cities where the foreign-born population is a small proportion, the differences between access levels of migrants & the native-born population are sharper: For example Seville (Asians), Bilbao (EU27, Europeans), Malaga (EU27, Europeans), Pisa (Africans), & San Sebastian (Asians) About 87% (Larger cities), & 8 (Medium-sized cities) of African, Asian, & American migrants, have high/very high access to P.T Oceanians are a negligible part of the population in all cities, & their patterns vary significantly between cities The most challenging larger cities are: Paris for African migrants, Manchester for Asian migrants, Rotterdam for American migrants, & Ruhrgebiet for European migrants The most challenging medium-sized cities are: Montpellier for African migrants, Eindhoven for Asian, & American migrants, & Mannheim for European migrants

16 4. Conclusions EU policy relevance What kind of strategies can we propose? Compact cities with high P.T access (e.g., Madrid, Barcelona), should disperse migrant communities for better social cohesion results, by regenerating brownfield areas throughout their territory. Outward expansion of social housing should be limited, though More sprawled cities with dispersed multiethnic communities (e.g. German cities), & higher shares of population with low access to P.T, should increase multimodality & connections in targeted peripheral mixed-use districts (enhance polycentric urban development) Policies should ensure urban transport/housing affordability, particularly in inner-city & transit-rich migrant communities (e.g. Paris, Berlin, Madrid) Integration plans should incorporate public transport & mobility studies, towards a better coordination between transport operators & urban authorities Do not focus only on geographical accessibility but also on physical access, time-based access, fear-based access, space management access, & economic access to the transportation system In the long-term successful migrants' integration processes might lead to better off; hence, cities should raise awareness about sustainable mobility & promote improvements of the walking environment to avoid car -based transport assimilation Promote qualitative research case studies that will help planners to understand the preferences, the stability & the time span of migrant households decisions about where to live, how to commute daily for different types of residential, & socioeconomic environments Conclusions Our analysis can assist cities to cross-benchmark their P.T access levels and EU-policy makers to allocate structural & cohesion funds in areas in need of specialized support initiatives which are currently affected by multiple glocalized challenges (e.g. climate change, migration). The D4i dataset enables researchers in Europe to formulate, for the first time, more sophisticated, data-based and comparable, migrants-related urban accessibility measures (e.g., food markets, healthcare, education), and thus support European migration policy agenda.

17 Yorgos N. Photis Professor, National Technical University of Alex Bartzokas-Tsiompras PhD Candidate, National Technical University of EUROPEAN COMMISSION DIRECTORATE-GENERAL JOINT RESEARCH CENTRE D4I - Data Challenge on Integration of Migrants in Cities Final Workshop Thank you for your attention!