Same-day visitors crossing borders: a Big Data approach using traffic control cameras

Transcription

1 Same-day visitors crossing borders: a Big Data approach using traffic control cameras M. Izquierdo Valverde J. Prado Mascuñano M. Velasco Gimeno National Statistics Institute (Spain) 14 th Global Forum on Tourism Statistics Venice, November

2 Index 1. Objectives 2. Definitions and sources 3. Statistic of Tourist Movements on Borders (FRONTUR) 4. First results 5. Theoretical approach 2

3 Index 1. Objectives 2. Definitions and sources 3. Statistic of Tourist Movements on Borders (FRONTUR) 4. First results 5. Theoretical approach 3

4 Objectives - To take advantage of machine-generated data (automated systems) to get a better framework to estimate same-day visitors crossing borders by road. - Two different approaches: - Inbound tourism - Outbound tourism 4

5 Index 1. Objectives 2. Definitions and sources 3. Statistic of Tourist Movements on Borders (FRONTUR) 4. First results 5. Theoretical approach 5

6 Definitions IRTS A traveller is someone who moves between different geographic location, for any purpose and any duration. - A visitor is traveller taking a trip to a main destination outside his/her usual environment, for less than a year, for any purpose (business, leisure or other personal purpose) other than to be employed by a resident entity in the country or placed visited. These trips taken by visitors are classified as tourism trips. - A visitor is classified as tourist (or overnight visitor), if his/her trip includes an overnight stay, or as a same-day visitor (or excursionist) otherwise. 6

7 Definitions IRTS 2008 In relation to the country of reference, it is recommended that the following three basic forms of tourism be distinguished: a) Domestic tourism, which comprises the activities of a resident visitor within the country of reference either part of a domestic trip or part of an outbound trip. b) Inbound tourism, which comprises the activities of a nonresident visitor within the country of reference on an inbound trip. c) Outbound tourism, which comprises the activities of a resident visitor outside the country of reference, either as part of an outbound trip or as part of a domestic trip. 7

8 Traffic loops Traffic loops: - Nº vehicles crossing the border: Intervals of 15 min By length And for both directions 8

9 Traffic loops time interval entry/exit small/medium/large 9

10 Traffic cameras Traffic cameras: - Who is coming? Vehicle nationality (residents/non residents) In both directions 10

11 Traffic cameras Around 5 millions of data each month % vehicles per nationality Next step: tracking the ID associated to the number plate 11

12 Traffic cameras 12

13 Index 1. Objectives 2. Definitions and sources 3. Statistic of Tourist Movements on Borders (FRONTUR) 4. First results 5. Theoretical approach 13

14 FRONTUR (by road) - Objective: to measure the number of non-resident visitors coming into Spain each month, and describe the main characteristics of the trips. - Framework: there is no a population framework (in the traditional sense). It must be built combining the following data: - Traffic loops - Aforo: sample operation that counts number of persons in vehicles, by type of vehicle and nationality. - Traffic cameras 14

15 Index 1. Objectives 2. Definitions and sources 3. Statistic of Tourist Movements on Borders (FRONTUR) 4. First results 5. Theoretical approach 15

16 First results VEHICLES AS SAME-DAY VISITORS: For vehicles with foreign-number plate: - vehicles registered as entrance during day D, their number plates are tracked to find them as exit during the same day For vehicles with Spanish number plate: - vehicles registered as exit during day D, their number plates are tracked to find them as entrance during the same day 16

17 First results For each border crossing, i, the number of vehicles coming into Spain, counted by cameras is: 17

18 may-16 First results Type of visitor All Non same-day visitor Same-day visitor Non identified number plate N % N % N % N % PF01 SEO DE URGELL, N-145 P.K ,63% ,77% ,58% ,00% PF02 PUIGCERDA-BOURMADAME, N-152 P.K ,77% ,11% ,10% ,00% PF03 PUIGCERDA-LLIVIA, N-154 P.K ,31% ,73% ,94% ,00% PF04 LA JUNQUERA, NII P.K ,20% ,82% ,96% ,00% PF05 PORTBOU, N-620 P.K ,01% ,84% 518 2,14% ,00% PF06 LA JUNQUERA AP-7 P.K ,92% ,43% ,64% ,00% PF07 LES, N-230 P.K ,39% ,47% ,12% ,00% PF08 CANFRANC, N-330 P.K ,03% ,90% ,06% ,00% PF09 TUNEL DE SOMPORT (BOCA SUR) P.K ,12% ,69% 197 1,17% ,00% PF10 BEHOVIA, NI P.K ,66% ,38% ,95% ,00% PF11 IRUN, AP-8 P.K ,13% ,30% 931 0,55% ,00% PF12 PUENTE DE SANTIAGO, N-121 P.K ,98% ,74% 710 0,27% ,00% PF13 PEKOTXETA, N-135 P.K ,98% ,03% 536 3,97% ,00% PF14 LANDIBAR, N-121 P.K ,72% ,18% ,09% ,00% PF15 TUY PUENTE INTERNACIONAL, A-55 P.K ,93% ,44% ,61% ,00% PF16 TUY PUENTE VIEJO, N-550 P.K ,21% ,21% 312 0,57% ,00% PF17 VERIN FECES DE ABAIXO, N-532 P.K ,80% ,39% ,79% ,00% PF18 TRABAZOS, N-122 P.K ,40% ,05% ,54% ,00% PF19 FUENTES DE OÑORO, N-620 P.K ,28% ,70% ,00% ,00% PF20 VALENCIA DE ALCANTARA, N-521 P.K ,01% ,31% 299 1,67% ,00% PF21 BADAJOZ, A-5 P.K ,53% ,00% ,46% ,00% PF22 AYAMONTE, A-49 P.K ,70% ,45% ,84% ,00% PF23 EL ROSAL DE LA FRONTERA, N-433 P.K ,99% ,14% 140 0,86% ,00% PF31 BIELSA, A138 P.K ,10% ,41% 414 6,47% ,00% PF32 ETXALAR, N-121A P.K ,35% ,85% 150 0,78% ,00% PF33 ISABA, NA140 P.K ,58% ,99% 841 7,42% ,00% PF35 GOIAN, ,43% ,23% ,33% ,00% 14 th Global PF36 Forum SALVATIERRA on Tourism DE Statistics MIÑO, Venice, 35,45% November ,97% ,57% ,00% Todo ,29% ,13% ,56% ,00%

19 First results Problems to solve: Lack of information: camera registers are incomplete - For a number plate we can find the following situation: Border crossing Date Number plate Nationality IN/OUT PF24 07/05/ AA PTR O PF24 08/05/ AA PTR I PF24 08/05/ AA PTR O When? PF24 09/05/ AA PTR O PF24 10/05/ AA PTR I PF24 10/05/ AA PTR O 19

20 First results Problems to solve: Same-day vehicles are underestimated If we transform same-day vehicles in same-day visitors (occupancy rate by vehicle), and compare the results to the ones obtained by FRONTUR, the underestimation is around 25% 20

21 First results Problems to solve: Definition of usual environment. Establish patterns for frequent border crossing vehicles: - To define a clear criteria to classify same-day vehicles as vehicles moving in its usual environment: border workers, border population, - Public transport crossing borders. Solution: integrated system traffic loops-traffic cameras. For each register, more complete information. Always combined with complementary sources as surveys. 21

22 Index 1. Objectives 2. Definitions and sources 3. Statistic of Tourist Movements on Borders (FRONTUR) 4. First results 5. Theoretical approach 22

23 Theoretical approach Next steps of this study: Integrate the information from traffic loops in terms of the length of vehicles with the data from cameras in terms of same-day visits: Different hypothesis are being testing 23

24 Thank you very much for your attention 24