Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
|   rank | location              |   total passengers | annual change   |   capacity | capacity in use   |
|-------:|:----------------------|-------------------:|:----------------|-----------:|:------------------|
|      1 | são paulo             |           32777330 | 9.24%           |   26000000 | 126 , 06%         |
|      2 | rio de janeiro        |           17495737 | 17.00%          |   18000000 | 97 , 19%          |
|      3 | são paulo             |           16775770 | 0.11%           |   12000000 | 139 , 79%         |
|      4 | brasília              |           15891530 | 3.20%           |   10000000 | 158 , 91%         |
|      5 | belo horizonte        |           10398296 | 9.05%           |    5000000 | 207 , 96%         |
|      6 | rio de janeiro        |            9002863 | 5.73%           |    6000000 | 150 , 04%         |
|      7 | campinas              |            8858380 | 17.04%          |    3500000 | 253 , 09%         |
|      8 | salvador              |            8811540 | 4.96%           |    6000000 | 146 , 85%         |
|      9 | porto alegre          |            8261355 | 5.45%           |    6100000 | 135 , 43%         |
|     10 | curitiba              |            6828334 | 2.03%           |    6000000 | 113 , 80%         |
|     11 | recife                |            6433410 | 0.78%           |    9000000 | 71 , 48%          |
|     12 | fortaleza             |            5964308 | 5.61%           |    3000000 | 198 , 80%         |
|     13 | vitória               |            3642842 | 14.46%          |     560000 | 650 , 50%         |
|     14 | belém                 |            3342771 | 11.56%          |    2700000 | 123 , 80%         |
|     15 | florianópolis         |            3395256 | 8.75%           |    1100000 | 308 , 65%         |
|     16 | manaus                |            3131150 | 3.70%           |    1800000 | 173 , 95%         |
|     17 | goinia                |            3076858 | 9.80%           |     600000 | 512 , 80%         |
|     18 | cuiabá                |            2761588 | 8.25%           |    1600000 | 172 , 59%         |
|     19 | natal                 |            2660864 | 2.88%           |    1500000 | 177 , 39%         |
|     20 | são luís              |            1991099 | 8.01%           |    1010000 | 197 , 13%         |
|     21 | foz do iguaçu         |            1741526 | 2.96%           |    1500000 | 116 , 10%         |
|     22 | maceió                |            1719979 | 11.02%          |    1200000 | 143 , 31%         |
|     23 | campo grande          |            1655073 | 9.20%           |     900000 | 183 , 89%         |
|     24 | aracaju               |            1373401 | 25.63%          |    1300000 | 105 , 64%         |
|     25 | navegantes            |            1277486 | 9.38%           |     600000 | 212 , 91%         |
|     26 | joão pessoa           |            1252559 | 9.64%           |     860000 | 145 , 62%         |
|     27 | londrina              |            1098848 | 14.23%          |     800000 | 137 , 35%         |
|     28 | ribeirão preto        |            1077010 | 3.35%           |     480000 | 224 , 37%         |
|     29 | porto velho           |            1050682 | 6.79%           |     920000 | 114 , 20%         |
|     30 | teresina              |            1044865 | 2.86%           |     450000 | 232 , 19%         |
|     31 | uberlndia             |            1011490 | 11.48%          |     600000 | 168 , 58%         |
|     32 | são josé do rio preto |             770569 | 15.13%          |     270000 | 285 , 39%         |
|     33 | belo horizonte        |             774881 | 2.33%           |    1200000 | 64 , 57%          |
|     34 | maringá               |             757719 | 13.61%          |     430000 | 176 , 21%         |
|     35 | palmas                |             579395 | 15.09%          |     370000 | 156 , 59%         |
|     36 | macapá                |             573560 | 2.36%           |     170000 | 337 , 38%         |
|     37 | ilhéus                |             532130 | 3.70%           |     300000 | 177 , 37%         |
|     38 | santarém              |             487168 | 5.62%           |     225000 | 216 , 51%         |
|     39 | petrolina             |             458588 | 23.25%          |     150000 | 305 , 72%         |
|     40 | juazeiro do norte     |             451087 | 31.51%          |     100000 | 451 , 08%         |

Question:
Airport Capacity Utilization Rate is defined as the percentage of the total airport capacity that is currently in use. Based on this definition, which airport has the highest Airport Capacity Utilization Rate?


id: 27f4204a35777e335600e3f00a4cd9fe
qtype: NumericalReasoning
label: Vitória